Theoretical framework Deliverable D3.1.

Theoretical framework Deliverable D3.1.

The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

European project co-funded by Erasmus+, key Action 3, Forward Looking Cooperation Projects

2 / 78

Project information Project acronym:

tMAIL

Project title:

Teacher Mobile Application for Innovative Learning

Project number:

562207-EPP-1-2015-1-BE-EPPKA3-PI FORWARD

Sub-programme or KA:

Key Action 3 - Forward Looking Cooperation Project

Project website:

http://www.tmailproject.eu

Project e-mail address:

[email protected]

Twitter:

@EUtMAIL

Project period:

From 01/11/2015 to 31/10/2017

Project manager:

Prof. dr. Koen Lombaerts

Project coordinator:

Dr. Jeltsen Peeters

Project coordinator organisation:

Vrije Universiteit Brussel (VUB)

Consortium partners:

Universidad Autónoma de Madrid (UAM)

University of Hull (UH)

University of Vienna (UNIVIE)

Youth Entrepreneurial Service Foundation (YES)

GO! Education of the Flemish Community (GO!)

European Distance and E-learning Network (EDEN)

Kidimedia ltd

Copyright:

CC BY-NC-SA 4.0


3 / 78

Main Authors Ernesto Panadero, Universidad Autónoma de Madrid. Jeltsen Peeters, Vrije Universiteit Brussel. Kevin Burden, University of Hull. Wolfgang Greller, Vienna University of Education

Contributing Authors Daniel García-Pérez, Universidad Autónoma de Madrid. Esther García Andrés, Universidad Autónoma de Madrid. Valerie Thomas, Vrije Universiteit Brussel. Paul Hopkins, University of Hull. Ray Kirtley, University of Hull. Elisa Serafinelli, University of Hull.


4 / 78

Table of contents Project information ................................................................................................................................ 2 Main Authors .......................................................................................................................................... 3 Contributing Authors .............................................................................................................................. 3 Foreword ................................................................................................................................................ 6 1.

Self-regulated Learning .................................................................................................................. 8

1.1.

Introduction .......................................................................................................................... 8

1.2.

What is self-regulated learning? Definitions and state of the art ......................................... 8

1.3.

What is Socially Shared Regulated Learning and why is it important? ............................... 11

1.4.

How are self- and socially shared regulation measured? ................................................... 12

1.5.

Development of self-regulated learning skills .................................................................... 14

1.6.

How to teach and promote self-regulated learning in our students? ................................ 15

2.

Implementing self-regulated learning: main barriers and stimuli ................................................ 16

2.1.

Teacher determinants ......................................................................................................... 16

2.2.

School determinants ........................................................................................................... 19

2.3.

The role of background variables ........................................................................................ 22

2.4.

What are the most successful SRL interventions? .............................................................. 24

2.5.

Teacher training on SRL ...................................................................................................... 26

3.

Teacher Professional Development ............................................................................................. 29

3.1.

What is professional learning for teachers? ....................................................................... 29

3.2.

Professional learning and technology use by teachers ....................................................... 30

3.3.

Defining teacher learning .................................................................................................... 31

3.4.

Processes and contexts of teacher learning ....................................................................... 34

3.4.1. Processes ............................................................................................................................ 35 3.4.2. Contexts of teacher learning .............................................................................................. 37 3.5. 4.

Summary of professional learning ...................................................................................... 40

Mobile technologies and professional development ................................................................... 42


5 / 78

4.1.

Introduction ........................................................................................................................ 42

4.2.

What is mobile learning? .................................................................................................... 43

4.3.

The iPAC mobile learning framework ................................................................................. 44

4.4.

The use of mobile technologies in the context of professional development .................... 47

5.

Self-regulated Learning and the use of Mobile Technologies ...................................................... 50

6.

Learning analytics as a means to support self-regulated learning ............................................... 52

6.1.

What is learning analytics? ................................................................................................. 52

6.2.

Definitions and conditions for learning analytics ................................................................ 53

6.3.

Prediction, semantics, quantified self, and reflection amplifiers ........................................ 56

6.4.

Emerging standards and codes of practice ......................................................................... 57

6.5.

Visualisation of learning analytics information ................................................................... 59

7. Conclusions: Implications for the development of a training course on self-regulated learning using mobile technologies .................................................................................................................... 61 8.

References .................................................................................................................................... 63


6 / 78

Foreword tMAIL [Teacher Mobile Application for Innovative Learning] aims to develop and test a mobile application supporting policy, teacher education, and primary school teachers in implementing classroom practices that stimulate students’ self-regulated learning (SRL). There is a large body of evidence on the importance of SRL, due to its positive impact on student success within and outside schools. Still, the evidence-based support for these essential learning-to-learn skills remains to be fully integrated within primary school practices. Different barriers exist, from policy, teacher education to teacher level. These multi-leveled challenges impede the accurate implementation of related SRL policies. Policy lacks the respective tools to enable translation and impact monitoring into practice. Teacher educators struggle with differentiating their instruction towards teachers’ needs, whilst effectively integrating digital learning practices. Lastly, teachers lack the necessary skills and tools to accurately support students’ SRL. tMAIL aims to address the needs of these different target groups by designing activities to support the development and testing of a mobile app. It delivers a personalized training course on SRL for in-service primary school teachers. Data generated through the mobile app will be processed through learning analytics and semantics. This approach, in support of data-driven teacher education, will enable the personalization of teachers’ and students’ learning, ultimately facilitating evidence-based policy making pathways.

In order to theoretically underpin the mobile application and teacher-training course, this report summarizes the state of the art and most recent empirical evidence in the following core domains:

-

Self- and socially shared regulation of learning

-

Teacher professional development

-

Mobile learning


7 / 78


8 / 78

1. Self-regulated Learning

1.1. Introduction Self-regulated learning skills involve different strategies students can use to reach their educational goals. These strategies rank from cognitive and metacognitive ones, more related to the content of the task and content-based strategies, to strategies to uplift or maintain their motivation and positive emotions towards learning. The final purpose of self-regulated learning (SRL) is that these strategies would allow students to effectively plan, monitor and evaluate their work. Research evidence indicates overwhelmingly the importance of self-regulation during learning with a positive impact on students’ cognitive and social competence, academic achievement, motivation, well-being and engagement in lifelong learning (Zimmerman & Schunk, 2011). Due to its high relevance for success in and outside school, self-regulated learning skills are identified as one of the key competences of contemporary education (e.g. Rethinking Education Framework). Therefore, creating more student-centered classroom practices and developing selfregulated learners has been high on the European as well as many national policy agendas for several years already (Dumont et al., 2010). However, different barriers at policy, teacher education, and teacher level still impede accurate translation of policies into practice. For this reason, this project aspires to develop and test an innovative tool that supports the implementation of this specific educational reform and which, moreover, has the potential to support other types of educational reform on a longer term basis.

1.2. What is self-regulated learning? Definitions and state of the art Self-regulation is considered one of the most studied topics in the field of psychology and can be defined as “the ability to monitor and modulate one's own cognition, behavior, and emotion in order to achieve a goal or meet the demands of a situation” (Dent, 2013, p.4). The concept was


9 / 78

first introduced by the work of Albert Bandura in the 1970s who introduced the concept of human agency postulating that people are agents of their own life rather than executors of their brain mechanisms (Bandura, 1999). He first developed the Social Learning Theory (Bandura, 1971), which he renamed to Social Cognitive Theory in 1986 (Bandura, 1986). Closely following Bandura’s work, researchers such as Zimmerman and Schunk started applying self-regulation to the academic field of learning after which self-regulated learning became far more studied as compared to the concept it originated from, namely self-regulation. Due to the suggested nested structure of both constructs, some authors therefore describe SRL as a special case of self- regulation (Dinsmore, Alexander, & Loughlin, 2008). More specifically, the concept of self-regulated learning (SRL) refers to self-generated thoughts, feelings, and actions that are planned and cyclically adapted to the attainment of personal goals (Zimmerman, 2000). Self-regulated learners are considered proactive agents who select and apply specific strategies to attain self-set goals and adjust their approach based on various sources of feedback. SRL skills involve all kinds of strategies students can use to uplift or maintain their motivation and positive emotions towards learning, as well as strategies that enable students to effectively plan, monitor, and evaluate their work (Pintrich, 2000). Most of the theoretical SRL models therefore highlight the cyclical nature referring to a forethought, performance control, and self-control phase (Panadero & Alonso-Tapia, 2014; Zimmerman, 1989). As has been studied, SRL covers a wide range type of strategies: behavioral, cognitive, metacognitive, emotional and motivational. The most wide spread SRL model is the Cyclical Phases model by Zimmerman (2000; Panadero & Alonso-Tapia, 2014). As can be seen in Figure 1, SRL is considered to be cyclical and to run through different phases (Zimmerman, 2002). First, in the forethought phase, self-regulated students analyze the task by setting goals and planning appropriate strategies to reach it. Moreover, students employ strategies that direct their selfmotivation. Motivational self- regulation implies the awareness and regulation of motivational and affective beliefs such as having interest in the task or acknowledging its value and coping with perceived task difficulty. It also includes self-awareness of students’ learning goal-orientation, which can vary from learning for one’s own interest versus learning to meet others’ expectations or to avoid failure. Learners can apply different strategies to adjust their motivation, such as engaging in mastery self-talk, risk taking, self-consequating, and attempting to enhance personal relevance of learning. Second, during the performance stage, self-regulated students control their performance


10 / 78

by, for instance, focusing their attention or engaging in self-instruction. Students can select and apply different strategies, such as seeking help and regulating one’s efforts. Moreover, students engage in self-observation and self-monitoring by tracking one’s performance. Third, in the selfreflection phase students evaluate their performance to pre-set standards and attribute their successes and failures to specific causes. Students can subsequently react in various affective, and adaptive or defensive ways (Pintrich, 2000; Wolters, Pintrich, & Karabenick, 2003; Zimmerman, 2002).

The use of adequate self-regulatory learning strategies is fundamental for students to have academic success in primary (Dignath, Büttner, & Langfeldt, 2008), secondary (Dignath & Büttner, 2008) and higher education (Sitzmann & Ely, 2011). Due to this importance of self-regulation in academic performance, it is crucial to explore which of the different self-regulatory theories is better adapted to cover the pedagogic needs faced by students in classrooms (Dignath & Büttner, 2008; Heikkiläa & Lonka, 2006). Therefore, SRL is probably the most comprehensive framework to


11 / 78

understand students’ learning. As we have presented above, it covers a very wide range of learning strategies and processes that explain a large proportion of learning variables.

1.3. What is Socially Shared Regulated Learning and why is it important? As stated by Panadero and Järvelä (2015, p. 190-191) in a recent review exploring what the main conclusions of the empirical evidence on the field are: Recently, the concept of socially shared regulation of learning (SSRL) has emerged, which occurs when groups regulate together as a collective, such as when they construct shared task perceptions or shared goals. When groups co-construct plans or align monitoring perceptions to establish a shared evaluation of progress, they are engaged in shared regulation (Järvelä, Järvenoja, Malmberg, & Hadwin, 2013). SSRL involves interdependent or collectively shared regulatory processes, beliefs, and knowledge (e.g., strategies, monitoring, evaluation, goal setting, motivation, metacognitive decision making) orchestrated in the service of a co-constructed or shared outcome (Winne, Hadwin, & Perry, 2013; Järvelä & Hadwin, 2013). Why is then SSRL important? First, evidence demonstrates that collaborative learning among students not only improves student engagement and achievement, but also facilitates the development of students’ self- and co-regulated learning strategies. Self-regulated learning does not equal independent learning. Rather, it is embedded in students’ social context and environment. When approaching self-regulated learning through collaborative student work, we touch upon the concept of socially shared regulation of learning (Panadero & Järvelä, 2015). Socially shared regulation of learning refers to students’ shared efforts to regulate their (learning and collaborative) behaviour, metacognition, motivation and emotions in order to develop a shared or co-constructed outcome. Whereas these learning strategies help students become effective collaborative learners, the collaborative learning climate is especially suitable to guide students’ self-regulated learning strategies as it is a more scaffold situation and students in collaboration can model each other.


12 / 78

Hence, when teachers want to support their students in developing effective learning strategies, the facilitation and monitoring of student collaborative work will be a crucial element. Therefore, the teacher-training course will incorporate instructions, good practices, and monitoring instruments to foster effective learning strategies through student collaboration. The creation of cooperative learning environments depends largely on the motivation and engagement of teachers and their colleagues. Teachers need the knowledge, skills, and tools to create collaboration among learners; the project provides tools to enhance teachers’ competences to foster meaningful and effective student collaboration.

1.4. How are self- and socially shared regulation measured? A recent publication has revised what is the current approach to SRL and SSRL measurement. According to Panadero, Klug and Järvelä (2015) there have been three waves of SRL measurement. “The first wave of SRL measurement is characterized by a more static conceptualization of SRL assessment. Emphasis is placed on the use of self-reporting (questionnaires, surveys, and interviews), relying heavily on students’ perspectives and beliefs. Well-known representatives of this phase are questionnaires such as the Motivated Strategies for Learning Questionnaire (MSLQ) (Pintrich, Smith, Garcia, & Mckeachie, 1993) or the Learning And Study Strategies Inventory (LASSI) (Weinstein, Schulte, & Palmer, 1987).” (Panadero et al., 2015 p. 2). “Second wave: the irruption of online measures At the end of the 1990s, and especially with the publication of the 2000 SRL Handbook (Boekaerts, Pintrich, & Zeidner, 2000), there was a switch in the conceptualization of SRL to a dynamic series of behavioral, cognitive, metacognitive, motivational, and emotional events, a change that was led by some of the previously mentioned authors through the


13 / 78

introduction of more advanced and complete versions of their proposed models (Pintrich, 2000; Zimmerman, 2000). Changing the definition of SRL from a trait-based to a processbased perspective affected the types of measurements required: to capture the phenomenon from a process perspective, measures that go along with the process itself (online measures) were needed (Winne & Perry, 2000). This switch in measurement in order to capture processes is what we call the “second wave” of SRL measurement.” (Panadero et al., 2015 p. 2). According to Panadero and colleagues (2015) we are currently in the third wave which is basically the combination of tools that intervene (promoting SRL in the participants) while at the same time collecting data for the researchers to analyze the SRL traces. A good example of this is the current trend in the use of learning diaries. Additionally, the field of SSRL has been leading the research in the third wave, especially by the work of Sanna Järvelä’s work. This type of measurement tools are based on the reactivity effect defined as changes that occur in an individual when s/he is aware of particular aspects of her/his behavior due to metacognitive monitoring (Panadero et al., 2014 p. 3). Many SRL interventions are based on tools that enhance students’ awareness of their own actions (metacognitive monitoring) as this is a crucial step towards SRL. What is innovative in this third wave is that the measurement tools do not longer claim to be “objective” measures but rather the researchers assume that they impact in the students’ SRL, hence in the object of measurement. This current measurement wave has implications for forthcoming SRL interventions. If we want to intervene to enhance SRL through the use of mobile learning technology, it would be crucial to consider the recommendations included in Panadero and colleagues (2015). See table below.


14 / 78

Extracted from Panadero et al. (2015 p. 7).

1.5. Development of self-regulated learning skills Zimmerman described a multilevel sequence of self-regulatory development, which describes four different stages through which SRL develops in learners. Whereas the first two stages focus on the role of social support, the latter two stages rely more on individuals’ internal processes (Zimmerman, 2000, 2013). Alongside the four stages of SRL development, Zimmerman (2013) formulates instructional guidelines for supporting students’ SRL. First, students are introduced to an SRL skill through observation of a model’s behavior and associated descriptions. Consequently, modeling SRL skills is an important instructional strategy for introducing SRL. Second, during the emulation level, learners copy the essence of the modeled behavior and can improve their SRL experiments with support, feedback, and social reinforcement of the model. Third, in the self-


15 / 78

controlled stage learners exercise the SRL skills in structured settings without the direct presence of the model. Learners learn to master and self- observe their performance, which can be enhanced by comparing their performance to internalized standards. Fourth, the SRL skills become truly internalized in the self-regulation stage where learners become able to adjust their performance and strategy use to the changing conditions and outcomes. The SRL skills are used in naturalistic rather than practice settings. Importantly, whereas stage four self-regulated learners are capable of SRL, they may still choose not to self-regulate because of both motivational and affective conditions (Zimmerman, 2013).

1.6. How to teach and promote self-regulated learning in our students?

SRL in primary schools can be promoted in several ways. First, teachers can indirectly facilitate students’ SRL development by manipulating contextual conditions that pupils encounter during their learning process. Deliberate manipulation of classroom practices, learning contents, tasks, and teaching methods in favor of students’ SRL skills development allows teachers to create strong SRL environments (Kistner et al., 2010). Second, teachers can foster pupils’ SRL development through the direct instruction of self-regulation strategies in both implicit and explicit ways (Kistner et al., 2010; Paris & Paris, 2001). Implicitly, teachers can support SRL strategies by modeling, prompts, suggestions and encouragement (Lapan, Kardash, & Turner, 2002; Paris & Newman, 1990). Explicit direct instruction, on the contrary, includes elaboration on and verbalization of the conditions and significance of specific SRL strategies for student performance (Kistner et al., 2010; Peeters, 2015).


16 / 78

2. Implementing self-regulated learning: main barriers and stimuli

2.1. Teacher determinants A recent PhD thesis investigated the association of different teacher level factors with primary school teachers’ promotion of pupils’ self-regulated learning (Peeters, 2015). Throughout a series of studies, different teacher level factors were discussed in relation to teachers’ self-perceived SRL promotion, i.e. educational beliefs, teaching experiences, SRL knowledge, self-efficacy, motivation and affect, and teacher self-regulation. First, in line with previous research that demonstrated how teacher beliefs drive instructional preferences and behavior (Errington, 2004; Ertmer, 2005; Sosu & Gray, 2012), multiple studies reported teachers’ beliefs concerning the value of SRL for primary education as a potential associate of SRL promotion. Two studies revealed that two types of beliefs were positively related with teachers’ self-perceived SRL promotion: teachers’ more general constructivist beliefs about primary education, and teachers’ specific beliefs concerning the role of SRL in primary education (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013; Peeters, De Backer, Kindekens, Jacquet & Lombaerts, 2015). Moreover, it was found that teachers’ constructivist beliefs were most decisive. The findings contradict an earlier study of Dignath-van Ewijk and van der Werf (2012) who only found teachers’ SRL beliefs to be related with SRL promotion. It is worth taking note in comparing nevertheless, that the Dignath-van Ewijk and van der Werf’s sample was considerably smaller and, contrary to the current study, only volunteering teachers participated in the study. Furthermore, the observation that the association of SRL beliefs was found significant even when accounting for constructivist beliefs might be explained by interpreting teachers’ beliefs about SRL as task value beliefs, which are a person’s beliefs about the usefulness of a particular task (Eccles & Wigfield, 2002). Recognizing the value of a task, in this case supporting students’ SRL, is relevant as it may subsequently facilitate changes in conceptual knowledge and activate teachers’ prior knowledge (Johnson & Sinatra, 2013). Fortunately, other studies disclose that teachers recognize and voice the benefits of SRL for students, confirming that most primary school teachers do acknowledge the value of SRL for primary education (Peeters, De Backer, Kindekens, Romero Reina, Buffel &


17 / 78

Lombaerts, 2013; Peeters, De Backer, Kindekens, Romero Reina, & Lombaerts, 2013). In sum, the thesis’ results imply that in order to increase teachers’ SRL promotion efforts, teachers need to display general constructivist beliefs as well as acknowledge the value of SRL for primary education. Second, also the role of teachers’ previous experiences with learning through self-regulation and with teaching practices that stimulate students’ SRL was investigated. Personally experiencing and acknowledging the potential benefits of SRL for students appeared to be an important indicator for increased levels of teacher attention to students’ SRL (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013; Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). Perception of positive impact on pupils’ performance and well-being appeared to enhance teachers’ motivation to further help their pupils to self-regulate. Third, primary school teachers described a lack of knowledge about SRL and a shortage of competencies to promote it as an important reason for low levels of SRL promotion (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013; Peeters, De Backer, Kindekens, Jacquet & Lombaerts, 2015). Respondents referred to all three sorts of metacognitive awareness (Wilson & Bai, 2010) as they expressed their need for more declarative knowledge (knowledge about SRL development), procedural knowledge (knowledge about how to promote SRL), and conditional knowledge (knowing why and when to promote SRL with specific pupils or in specific situations). Hence, when supporting teachers to increase their SRL promotion efforts, attention should be paid to their (metacognitive) knowledge of SRL. Fourth, the role of teacher self-efficacy for constructivist teaching was explored and showed a positive association with teachers’ self-reported SRL promotion (Peeters, De Backer, Kindekens, Jacquet & Lombaerts, 2015). The study, however, recommended a careful examination of the role of the different sub-facets of self-efficacy (i.e. classroom instruction, classroom management, and student engagement), because they each appeared to be related differently to SRL promotion. Both the study’s empirical findings and literature suggest that instructional scenario’s facilitating student autonomy and self-regulation require stronger classroom management skills as compared to more traditional classroom settings (Grant, 2003; Rimm-Kaufman, Storm, Sawyer, Pianta, & LaParo, 2006). The same observation was reported by teachers in another study with quite some teachers voicing to struggle with classroom management when allowing more student input (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). It might be that teachers who believe in the


18 / 78

value of constructivist and SRL learning environments and who experience the support of school policy may find it easier to teach the classroom. However, it may not necessarily help them to engage their students and, especially, to manage their classrooms. Further research remains mandatory. Fifth, also teachers’ affective and motivational states were described. Teachers for example stressed affective states such as the need to feel good and secure in experimenting with SRL promotion (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). Moreover, in line with Lewis (1999), teachers felt concerned about classroom management and loss of control when introducing SRL classroom practices. Carefully monitoring affective states of teachers’ starting to promote SRL is highly advocated as this group of teachers is more likely to display more defective coping mechanisms resulting in increased levels of stress (Lewis, 1999). Many large-scale reform initiatives are in conflict with teachers’ identity, even when teachers are eager supporters of the reform initiatives (Van Veen, Sleegers, & van de Ven, 2005). Hence, it is critical to apply an affective and motivational approach with teachers showing low as well as high levels of SRL beliefs in order to engage all teachers in successful educational change (Van Veen et al., 2005), such as the implementation of SRL promotion. Teachers can and do apply different strategies to self-regulate their motivational conditions during their professional career (Peeters, De Backer, Romero Reina, Kindekens, Buffel & Lombaerts, 2014). Sixth, different studies suggested looking at the role of teacher self-regulation in supporting students’ SRL. One study specifically focused on the role of teacher self-regulation by means of asking teachers for their own perspective (Peeters, De Backer, Romero Reina, Kindekens, Buffel & Lombaerts, 2014). Furthermore, teacher self-regulation was questioned in relation to both teaching practice in general, and supporting SRL strategies in specific. First, the majority of respondents referred to the value of self-regulation strategies for the teaching profession. The teaching profession was experienced as conducive to teacher selfregulation. The following characteristics of the teaching profession were reported to require or motivate teachers to self-regulate their teaching: the (yearly) repetitive character of education, the multiple changes education endures, and high levels of job autonomy. However, a small group of teachers equally voiced not being able or stimulated to self-


19 / 78

regulate due to contextual difficulties such as time constraints, or because they simply chose not to self-regulate. Furthermore, due to the dynamic educational landscape and changing challenges, teachers were required to continue and self-regulate professional learning. Additionally, teachers also feel the need to self-regulate their motivation during their professional career. Teachers, for example, indicated to self-direct their motivation to teach by, for instance, choosing strategies to increase their interest in teaching once they feel it fading away. Through means of interest activation, defined as an important motivational SR strategy (Pintrich, 2000; Wolters, Pintrich, & Karabenick, 2003), teachers succeeded in uplifting their motivation and staying engaged in teaching practice. Second, teachers illustrated the value of their personal self-regulation when supporting their students’ SRL. Firstly, personal self-regulation skills were deemed necessary to model and verbally explain the use of SR strategies. Students indeed learn SRL from observing teachers’ strategies (Zimmerman, 2002), and teachers need the necessary knowledge to make SRL more visible for students, and to explain the use and value of the strategies being modeled (Paris & Winograd, 2003). However, despite the importance of metacognitive discussions, elucidating strategy use, in addition to mere modeling (Dignath, Büttner, & Langfeldt, 2008; Dignath & Büttner, 2008), only a limited amount of respondents spontaneously reported the necessity of such explicit explanation of SR strategies. Secondly, respondents reported to teach students SRL strategies teachers had past successful experiences with, which is in line with previous research (Dembo, 2001; Gordon et al., 2007). Thirdly, teachers reported that in comparison with transmission oriented teaching, the stimulation of their pupils’ SRL required them to increase the application of their own SR strategies. Examples put forward were the use of advanced planning strategies, increased monitoring, careful direction of teaching time and place, and increased risk taking.

2.2. School determinants The aforementioned thesis (Peeters, 2015) equally sheds light on the role of several specific school level mechanisms in understanding teachers’ different levels of SRL promotion.


20 / 78

First, the presence of a school policy concerning SRL promotion, which includes a shared school vision and teacher support for implementation, was studied. By means of multilevel modelling, the presence of SRL school policy appeared to be the only school level mechanism that was significantly associated with the successful introduction of SRL in primary school classrooms (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013). The direct association of SRL school policy with self-perceived SRL promotion was tested again by means of structural equation modeling and again showed the significance of SRL school policy in understanding differences in teachers’ level of SRL promotion (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013). However, the direct association of SRL school policy turned insignificant when checking for two indirect associations of SRL school policy with SRL promotion. In addition, qualitative analyses also described a shared and clear school vision as indispensible to SRL promotion, since it was considered a precondition of a pre-set vertical policy plan that gradually introduces SRL in primary education (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). However, data equally showed that policy could become counterproductive when too many strict procedures decrease teacher flexibility. Indeed, reform policy regularly stimulates one approach of teaching only, hereby limiting teachers’ options (Van Veen & Sleegers, 2009). Whereas in SRL frameworks, it is considered important to provide sufficient space for teachers to co- and self-regulate their way to implement SRL classroom practices and find strategies that fit within their personal preferences and expertise. Second, teacher collaboration was indicated as a powerful support system (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). Peers were considered valuable sources of knowledge. Especially hesitant teachers expressed that peers could support and assist them to promote SRL, and could encourage them to question and possibly adjust their own SRL related convictions. Schunk (2012), indeed, explains how learning through peer observations provides instructional information and enhances motivation as the discovery of similarities enables teachers to apply the newly acquired information to their own classroom practice. In the context of ICT integration, for example, peer socialization seemed to be more effective as compared to professional development (Ertmer, 2005). As a consequence, Ertmer (2005) recommends providing sufficient time for teacher collaboration.


21 / 78

Third, teachers necessitated the involvement of the school principal as the main person responsible for introducing, monitoring, and evaluating SRL classroom practice (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). Furthermore, the school principal was expected to create supportive school mechanisms (e.g., flexible schedules, teacher collaboration, and professional development). Although principals can merely create conditions for cultural change, research confirms the important role of school principals in establishing successful schools (Darling-Hammond, Meyerson, Lapointe, & Orr, 2010; Moolenaar, Daly, & Sleegers, 2010). Including promoters of professional change, such as school principals, in steering and guiding innovation processes is therefore highly recommended (e.g., Butler & Schnellert, 2012; Fullan, 2007; Lapan, Kardash, & Turner, 2002; Nielsen, Barry, & Staab, 2008). Fourth, quantitative analyses showed no significant associations of task and performance oriented leadership, participative decision- making, and innovative school climate with self-reported SRL promotion (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013). Different explanations were presented, such as the small amount of school level variance to be explained, the potential more indirect relationship with SRL promotion, and the differences between quantitative and qualitative studies when investigating school context. Nevertheless, qualitative analyses suggested the above school level mechanisms to play a role (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). For example, teachers reported the need for clear guidelines and principals taking the responsibility for tasks and performance, in this case SRL promotion. Fifth, teacher reports listed several organizational and more structural school factors, such as time pressure, curriculum load, teaching material, and infrastructure (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). It is important to note, however, that removal of infrastructural barriers does not automatically result in changes in teachers’ pedagogical approach (Ertmer, 2005).


2.3. The role of background variables

22 / 78

Finally, the dissertation looked at and controlled for the role of so called ‘background variables’ in relation to teachers’ SRL promotion (Peeters, 2015). First, although respondents in a qualitative study did described how the number of pupils affected the extent to which they supported students’ SRL (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013), quantitative analyses did not find a correlation of class size with teachers’ self- perceived SRL promotion (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013). Whereas class size may facilitate certain conditions for SRL, such as easier classroom management or student monitoring, reduction in class size does not automatically lead to changes in teachers’ pedagogical approach (Harfitt, 2012). Second, higher school grades were consistently found positively related with teachers’ SRL promotion (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013; Peeters, De Backer, Kindekens, Jacquet & Lombaerts, 2015), which is in line with previous research (Lombaerts, Engels, & Vanderfaeillie, 2007). Qualitative results provide some more in-depth information: these results, for example, indicated to especially support SRL when teaching 11 and 12 year olds, since they feel the urge to promote SRL in order to prepare pupils for the transfer to secondary education (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013), Consequently, many teachers report that SRL in their school is promoted mostly in the final years of primary education. Furthermore, teachers were found to vary in their opinion on the most appropriate student age to start initiating students’ SRL (Peeters, De Backer, Kindekens, Romero Reina, & Lombaerts, 2013). Whereas some teachers strongly advocated starting in kindergarten already, others stated SRL could only be stimulated from 3rd or 4th grade onwards since 1st and 2nd grade children were believed to have too little self-regulation capabilities. Spruce and Bol (2014), equally found teachers doubting the SRL capacities of primary school children, irrespective of teachers’ general favorability towards SRL. Evidence, however, demonstrates that SRL can and should be supported in kindergarten (Bryce & Whitebread, 2012; Perels, Merget-Kullmann, Wende, Schmitz, & Buchbinder, 2009). Teachers acknowledging this need in chapter 3 therefore observed and criticized the trend that SRL promotion decreases as students move from pre-school over primary school to secondary education.


23 / 78

Consequently, many of the teachers stressed the need for a school-wide SRL policy plan with concrete guidelines and clear expectations for each stage of primary education. Third, it was shown how the support for SRL in primary school grew as the number of pupils from ethnic minority and lower socio-economic background increased (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013; Peeters, De Backer, Kindekens, Jacquet & Lombaerts, 2015). Although remarkable at first sight, the results corroborate previous findings (OECD, 2009) and may be explained by teachers’ efforts to especially engage these students being raised in non-academic environments (Vieluf, Kaplan, Klieme, & Bayer, 2012). SRL promotion may be considered one way of doing so. In addition to ethnicity and socio-economic status, chapter 2 also examined the role of the degree of urbanization of the school’s location. Teachers from schools located in urban areas were found more likely to promote their students’ SRL development. Although differences in ethnic and socio-economic student population background could explain this finding, the association of urbanization with SRL promotion remained significant after controlling for the socio-economic and ethnic school profile. Future research studying in more depth this particular finding was suggested. Finally, also teachers in a qualitative study reported students’ ethnic and socio-economic background as well as students’ shortage of skills in the language of instruction as influential for the extent to which they instruct SRL (Peeters, De Backer, Kindekens, Romero Reina, & Lombaerts, 2013). These qualitative findings, however, presented a more nuanced picture. One group of teachers was particularly stimulated to support these students’ SRL. However, another group of teachers felt especially demotivated for various reasons. Consequently, it was hypothesized that the association of student and school characteristics with self-reported levels of SRL promotion might be mediated by teacher level characteristics. Fourth, the dissertation shed light on the association of teacher age and gender with self- perceived SRL promotion. Whereas no evidence was found for gender differences in SRL promotion, studies did reveal a statistically significant relationship of teacher age with teachers’ beliefs about the value of SRL for primary education, and their self-reported SRL promotion (Peeters, De Backer, Kindekens, Jacquet, & Lombaerts, 2013; Peeters, De Backer, Kindekens, Jacquet & Lombaerts, 2015). However, older teachers did show higher rates of self-efficacy for teaching, which was found positively related to SRL promotion (Peeters, De Backer, Kindekens, Jacquet & Lombaerts, 2015)..Literature especially suggests the interaction of teacher age with their skills and self-efficacy to manage the classroom when introducing more student- centered teaching (Grant, 2003; Yeo, Ang,


24 / 78

Chong, Huan, & Quek, 2008). Qualitative results again offer a more nuanced view (Peeters, De Backer, Kindekens, Romero Reina, Buffel & Lombaerts, 2013). Although many respondents described age-related differences in SRL promotion, opinions on this matter were diverse. Respondents did not seem to agree whether an increase in teacher age affects SRL promotion positively or negatively. Older teachers need to remain up to date and may face more difficulties in changing their existing practice. Younger teachers may lack the necessary management skills, not yet master the learning content, and know the school guidelines regarding SRL, and may be more subject to the influence of parents who may prefer more traditional forms of education. The diversity of respondents’ opinions on the relationship of teacher age and SRL promotion, suggested that, rather than teacher age, underlying mechanisms were at stake. In sum, these teacher age related observations are important for educational practice and teacher education programs, and are often explained by younger teachers having followed different initial education and having learned a larger variety of instructional methods (Vieluf et al., 2012). Such information should be reckoned with when tailoring teacher support programs to the specific needs of younger and older teachers, and when aiming to design effective strategies to also engage older teachers in the implementation of SRL classroom practice.

2.4. What are the most successful SRL interventions? Dignath, Büttner and Langfeldt (2008) conducted a meta-analysis on the effectiveness of self-regulated learning interventions on primary school students’ academic performance, strategy use, and motivation. An overview of the most effective training characteristics: ●

Type of strategies: A training programme should be based on social-cognitive theories. Primary education students benefit most from socio-cognitive interventions because these are more comprehensive and include different aspects of learning: cognitive (elaboration and problem solving skills), metacognitive (e.g. planning strategies), and motivational aspects (which are crucial at that developmental age and educational level). Additionally, this strengthens our decision of anchoring this project to Zimmerman’s model as it is a


25 / 78

socio-cognitive one, which advocates the consideration of cognitive, metacognitive and motivational factors when investigating self-regulated learning. ●

Type of metacognitive reflection. Next to the instruction of metacognitive strategies, students should be provided with knowledge about strategy application and its benefits for their learning. Students need the skill and the will to engage in self-regulated learning.

●

Type of motivational strategies. Most effective training programmes provided students with feedback about their (strategic) learning.

●

Group work. Primary school students still need instructions about cooperation. It is not enough to let students sit around a table in small groups without providing them with any systematic instruction.

●

School subject. The highest effects on academic performance could be gained by interventions in the context of mathematics, followed by reading/writing and lastly by those conducted within the scope of other school subjects (similar findings regarding motivational outcomes, no differences between school subjects concerning the effects of strategy use). The results indicate that promoting self-regulated learning raises academic performance more in a well-structured subject than in a rather open field.

●

Duration of intervention. The length of interventions did not reveal significant differences in effectiveness of interventions.

●

Implementation of the programme. Students benefit more if researchers introduce strategies instead of their regular teachers.

●

Age of students. Although even at primary school age, students can profit from selfregulation intervention, there are differences in the way the intervention effects arise: younger students show greater effects in motivational aspects (younger children are more motivated to learn), they also achieve greater effects concerning the use of strategies than students in the upper grades of primary school. Older students already command a strategy repertoire, which is harder to change, while young students are more open to acquiring new strategies. However, these findings did not have an impact on the effects of the interventions regarding the academic performance of students.


26 / 78

2.5. Teacher training on SRL

Training teachers to promote self-regulated learning should be a practice consistent with the ideas we have explained so far. SRL entails a wide range of complex strategies by which students plan, monitor and evaluate their own work. Consequently, teacher training in SRL should include content about all the SRL skills within a training framework that promotes a strategic and constructive learning environment. Beginning with the content, the evolution of the concept of SRL shows itself how important is to pay attention not only to the cognitive and metacognitive elements of learning but also to the emotional and motivational aspects. In accordance with this evolution, it is necessary to ensure that all the components of self-regulated learning are covered in the training (Michalsky, 2012). Research on the topic of teachers’ SRL training has found that teachers tend to relate SRL to constructive learning environments, but they rarely consider the link between SRL and explicit strategy instruction (Dignath-van Ewijk & van der Werf, 2012). On the contrary, they do connect the ideas of strategy instruction when they think about lifelong learning and learning to learn (Dignathvan Ewijk & van der Werf, 2012). For this reason, it is important to highlight contents about strategy instruction for SRL and to provide materials for this aim (Dignath-van Ewijk, Dichhäuser & Büttner, 2013). In addition to the content that should be trained, it is crucial to think about the instructional design of training programmes to encourage teachers to promote SRL in their classrooms. Next, two general principles for training design will be presented along with an explanation about specific strategies to achieve each of them. First, a growing body of research and policy on teacher training in different issues argues that teacher professional development should be based on collaboration and networking (see European Commission, 2015, chapter 4). By using collaboration it is more likely that teacher training and professional development become stable activities maintained over long periods of time and, therefore, produce higher impact and innovation in the schools (Dignath-van Ewijk, Dickhaüer & Büttner, 2013). Besides, a collaborative model can help to fight time constraints by promoting longterm networks and coalitions to co-design and share materials (Peeters, 2015). In order to promote collaborative work among teachers it is useful to recruit groups of teachers (e.g., a critical mass of teachers from the same school) to participate in the training courses (Finsterwald et al., 2013), and


27 / 78

to foster meetings and teacher groups for mutual support (Dignath-van Ewijk, Dickhaüer & Büttner, 2013). This type of collaboration will be additionally making use of the socially shared regulated learning concept that was explained earlier in this theoretical framework. Second, most SRL researchers agree with the fact that teacher training in SRL should indeed be based in the principles of self-regulated learning (e.g., Butler et al., 2004; Kramarski & Michalsky, 2009; Peeters, 2015). As a result, teacher training in SRL has to encourage teachers to develop a personal approach, to avoid the transmission of fixed procedures (Butler et al., 2013; Michalsky, 2012) and to help them to become aware of their own learning processes (Kramarski & Michalsky, 2009). If teachers interact with the content in ways that they expect their students will do, it is more likely that they will engage in those practices (Finsterwald et al., 2013). In a similar fashion, Butler et al. (2004) suggested to frame teacher training in SRL as an action to help teachers to work on new decision making criteria. Teachers should be encouraged to develop a reflective inquiry, to think and to experiment with ideas and teaching skills (Vrieling, Bastiaens & Stijnen, 2010). Keeping this general ideas on mind, we present three specific strategies to organize teacher training using SRL based on the ideas extracted from the desk research: First, Michalsky & Schecter (2013) suggests that it is beneficial that teacher training includes both successful and problematic experiences. Compared to a group of teachers that focused solely in the reflection about problematic experiences, teachers that were shown both kind of situations obtained greater improvement on pedagogical content and self-efficacy measures. Second, several scaffolding strategies can be used to gradually move from teachers’ trainer control to teacher control. These strategies are: modelling metacognitive skills, ask process and metacognitive questions, to include self-assessment strategies and to present lesson feedback in terms of SRL (Perry, Hutchinson & Thauberger, 2008; Vrieling, Bastiaens & Stijnen, 2010). Additionally, the use of reflective prompts about pedagogical contents is also very useful during teachers training and with pre-service teachers, as the work by Bracha Kramarski and colleagues has shown (e.g. Kramarski & Revach, 2009). This type of metacognitive prompts are questions that teachers need to ask to themselves as, by answering to them, their awareness and use of strategies it is enhanced.


28 / 78

Finally, following Vrieling, Bastiaens & Stijnen (2010), there are other 3 SRL skills that should be applied to teachers training in SRL. (a) By providing attributional feedback, it is possible to stress factors under teacher’s control. (b) Making schedules for time management and (c) emphasizing task value are also valuable strategies that teachers could learn and then implement with their students.


29 / 78

3. Teacher Professional Development

3.1. What is professional learning for teachers? In a complex, uncertain and rapidly changing global landscape, career long professional learning for teachers has been identified as an essential component of the education agenda where change is regarded as a constant rather than a one off event (Fisher et al., 2006; Fullan & Hargreaves, 1992; Fullan, 2001). Therefore it is important for policy makers, school leaders and teachers to have a strong grasp and understanding of the theoretical perspectives, processes and contexts that facilitate how teachers learn (Ashton & Newman, 2006; Day, 2006; Grundy & Robinson, 2004). These changes imply a commitment from teachers to continuous, lifelong learning, rather than sporadic or episodic events such as external courses which have traditionally characterized teachers’ in-service training and professional development (Aubusson et al., 2009). Changes of this magnitude are likely to require considerable learning and re-learning on the part of teachers which will be difficult to make unaided and without external support and guidance (Putnam & Borko, 2000; Wilson & Berne, 1999). Teachers work in a complex and ill-structured domain where there are few simple or unproblematic issues or challenges (Borko, Whitcomb, & Liston, 2009; Mishra & Koehler, 2009; Shulman & Shulman, 2004). Problems are often multidimensional in nature and are resistant to simplistic or formulaic solutions. These have been labelled ‘wicked problems’ and they typify the challenges facing teachers on a daily basis (Bore & Wright, 2009; Borko et al., 2009). Teaching is therefore characterized as messy and cluttered leaving practitioners to work out for themselves their own solutions to problems which are unpredictable and complex (Yadav & Koehler, 2007). Under prevailing conditions of this nature teachers are constantly asked to shift and modify their knowledge bases, reappraising what they are required to teach and how it should be taught. This requires a continuous process of learning, demanding considerable mental dexterity and flexibility on the part of teachers (Ertmer, 2005; Yadav & Koehler, 2007). Teachers utilize a wide range of cognitive resources and knowledge domains in order to function effectively in this dynamic and unpredictable setting, but they also need to be cognizant of the learning opportunities which occur


30 / 78

in social settings such as their own workplace which can provide opportunities for individuals to participate as part of a community (Putnam & Borko, 2000; Shulman & Shulman, 2004).

3.2. Professional learning and technology use by teachers

In addition to these challenges and demands there is also a growing expectation surrounding

the use of technology by teachers in schools. Policy makers around the globe are mandating the adoption and use of technology as a tool for learning and teachers are expected to assimilate and develop the necessary skills and understanding to ensure this is evident in their practice and in their classrooms (Hennessy, Ruthven, & Brindley, 2005; Kouzma, 2005; UNESCO, 2013). From this we can conclude there is likely to be a growing expectation that teachers will be digitally literate and will understand how to use technology to support both their pedagogical practices, and their own learning. PISA (Programme for International Students Assessment) 2015 report, considers the presence of computers in classrooms. Examining students’ access to ICT (Information and Communication Technologies) between 2012 and 2015, PISA explores the integration of digital devices in education. Discussing the digital evolution reported in recent years, PISA’s report highlights the importance of strengthening students’ literacy and numeracy skills to interact efficiently with digital contents. In this sense technology offers both an opportunity to complement various processes of teacher learning – as will be shown below – and also a threat which many teachers may feel illequipped to tackle: The aim of many policy makers in the UK and around the world is to encourage evolution into a learning society for the [21st] century: one in which all people are responsible for their own learning throughout their lives. Access to information and learning will often depend on new technologies as well as on an approach to teaching which supports collaborative professional development (Somekh & Davis, 1997, p. 3). The issue of technology in particular exerts a powerful and unvarying pressure on teachers to be fluent learners themselves with digital technologies which has a discernable impact on their learning


31 / 78

which needs to be considered. Whether it be for their own use or for use with students in a classroom context, learning how to be fluent users of digital technologies represents a particularly complex undertaking for teachers, a factor which has encouraged some to rank technological pedagogical knowledge (TPACK) alongside that of pedagogical content knowledge in terms of importance and significance (Mishra & Koehler, 2009; Schmidt et al., 2009).

Unlike analogue technologies such as the pencil, which is relatively stable, transparent in

design and unchanging in its format, digital technologies are Protean since they can be used in a wide variety of different ways; they are unstable in the sense that they change and evolve rapidly over a short period of time; and they are opaque making it difficult to discern their inner workings (Bruce & Hogan, 1998). Taken collectively these characteristics or ‘affordances’ (Gibson, 1979) of digital technologies impose particular challenges for teachers meaning that now, more than ever before, there is an imperative for teachers to be fully aware of how they learn with digital technologies. Research-based literature suggests technology itself can assist teachers in these goals (Burden, 2010; Burden, 2012; Fisher et al., 2006), however, for this to be effective educators at all levels need to know and understand more about how teachers learn with technology and under what conditions and in what contexts teacher learning with digital technologies is likely to be effective: If we want to encourage different approaches to teaching and learning, and new relationships between pupils and teachers, we need to understand the ways in which teachers come to learn, adapt and make such new approaches a reality (Fisher et al., 2006: 2).

3.3. Defining teacher learning The nature of teacher learning is contested and problematic with some viewing it as an essentially individualistic phenomena, undertaken mainly at a cognitive level within the mind (Hoban, 2002; Leinhardt & Greeno, 1986) whilst others argue for a more socio-cultural and situated awareness which conceptualises it as a more social, collaborative and context specific undertaking


32 / 78

(Brown, Collins, & Duguid, 1989; Putnam & Borko, 2000). The cognitive model of teacher learning is close to that described by Jack Mezirow in his theory of Transformative Learning which posits that adults learn by critically reflecting upon their epistemic and psychological frames of reference following a disorientating dilemma which forces them to see the world in a different light (Mezirow, 1978; 1991). This is an essentially individual process but alternative definitions of teacher learning would seek to expand beyond this cognitive interpretation by introducing both a social and a situated perspective often referred to as a ‘situative’ perspective on learning (Lave & Wenger, 1991; Putnam & Borko, 2000). Teachers also learn through working with others within their workplace, by asking questions, sharing information, seeking help, experimenting with innovative actions and seeking feedback (Eraut, 2002). This definition shifts the focus away from a purely individualist definition of cognition, and alludes to non-formal elements, such as discussion and discourse within the context of the staff room, which constitute the more tacit and elusive elements of teacher learning. For the purposes of this project a hybrid definition of teacher learning is adopted which acknowledges the complexity of teacher learning as being both individual and cognitive in nature, whilst also rooted within particular contexts and situations which involve a significant element of social activity. This hybrid definition of teacher learning is captured as follows: Accordingly, teacher learning is shaped through a combination of reciprocity between the context of the particular school setting, and an individual teacher’s interest and disposition to learn about practice…(Wilson & Demetriou, 2007: 214) Additionally it is also important to recognise a further point of demarcation which separates formal and informal perspectives of teacher learning. Many accounts of teacher learning are couched in terms such as Continual Professional Development (CPD), In Service Training (INSET) or just simply training, all of which tends to describe the formal, planned and prescribed nature of the phenomenon. However, there is also a perspective which recognises how much of what teachers learn occurs tacitly or accidentally in an informal and emergent manner which is more difficult to predict, but equally important to recognise (Williams, Karousou, & Mackness, 2011).


33 / 78

The significance of these additional layers or perspectives on teacher learning cannot be

under-estimated but they prove to be something of a conundrum in this respect. Many teachers recognise how their learning is complex and multifaceted in ways which are not necessarily suited to formalized, decontextualized external training events, characterizing many forms of professional development teachers are required to undertake. But much of the tacit, day-to-day instances of teacher learning such as discussion in the staffroom or the exchange of ideas around the photocopier, are deemed to be important but ephemeral, ‘happenstance, random and unpredictable’ (Wilson & Berne, 1999: 174). The realization that much of what is termed teacher learning may be hidden away from public view or even the awareness of teachers themselves is beginning to broaden the scope of investigations into this phenomenon. For some, the really interesting spaces to investigate are precisely the same spaces others would refer to as ‘happenstance’ since it is here that teacher learning is likely to be at its most authentic, contextualized and relevant for practitioners themselves (Wilson & Demetriou, 2007: 214). The concept of ‘emergent’ rather than ‘prescribed’ learning captures what may be a significant element in how teachers learn, whilst it is recognised that these constructs are artificial polarities held in tension against each other requiring much greater investigation and research: [Teacher] learning which arises out of the interaction between a number of people and resources, in which the learners organise and determine both the process and to some extent the learning destinations, both of which are unpredictable. The interaction is in many senses self-organised, but it nevertheless requires some constraint and structure. It may include virtual or physical networks, or both (Williams et al., 2011: 3). In recognising the tension and balance between prescribed and emergent, and formal and informal elements of learning Williams et al also acknowledge how the existing perspectives on teacher learning are shifting. There is a discernable move away from predetermined courses and events which have characterized teacher development for many years towards more ‘emergent learning frameworks’ which broaden the previously exclusive focus on cognition and the individual with newer theoretical perspectives such as Connectivism, Complexity Theory and Communities of


34 / 78

Practice (Kelly, 2006). In this respect the potential of mobile technologies, and indeed technology generally to enhance teacher learning is immense and largely unexplored (Fisher et al., 2006; Williams et al., 2011).

3.4. Processes and contexts of teacher learning The previous subsection examined the subject of teacher knowledge and explained how this is closely associated with teacher learning, the focus of this subsection. It demonstrated how teacher knowledge can be categorized by its degree of structure and codification with tacit or informal knowledge being particularly resistant to standardization and definition. This section progresses this narrative by examining how teachers learn. It consists of two separate but related themes: the process and the contexts that support professional learning. These are shown in Figure 1 based on the work of Burden (2010 and 2012) who has identified these processes (the inner circle) and the contexts (the outer circle) as core elements of teachers’ professional learning:

A model of teacher learning (based on Burden, 2010 & 2012)


35 / 78

The first theme, consists of the specific processes which underpin how teachers learn and these are defined as: (a) Experience. (b) Reflection. (c) Knowledge construction. The second theme refers to the contexts or settings which support teacher learning and these consist of: (d) Situation. (e) Collaboration. (f) Mediation.

3.4.1. Processes a. Experience

It seems a truism that teachers will learn from experience (Eraut, 1994) but how such

experiences are transacted as learning by teachers is still contested and unclear (Luckmann, 1996). Many consider experience, or learning by doing, to be the precursor to learning through reflection (Kolb, 1984) but there is little consensus in the literature on this topic. Teachers’ practical wisdom is often regarded as the starting point for much of their professional learning and in this sense learning from experience is seen to be learning to participate, a largely iterative and cyclical process which links into the notion of participation as a form of learning (Kelly, 2006; Wenger, 1998). b. Reflection

Reflective practice is widely recognised as a powerful form of professional learning for

teachers consisting of ‘a state of doubt, hesitation, perplexity, or mental difficulty, in which thinking originates’ (Dewey, 1896, p. 359). Reflection provides an opportunity to transform tacit knowledge,


36 / 78

often garnered through experience, into something more tangible and explicit which other professionals can learn from (Schön, 1995; Sternberg & Horvath, 1999). This is a deeply thoughtful and purposeful activity that does not come naturally to all teachers. Critical reflection is more than just technical description of teaching activities: Reflection is inquiry into pedagogy and curriculum, the underlying assumptions and consequences of these actions, and the moral implications of these actions in the structure of schooling (Zeichner & Liston, 1987: 23) However, recent commentators have criticised the original concept of reflection arguing it places too much reliance on the role of the individual at the expense of the group or team they are working within (Boud & Hager, 2010; Boud, 2010). Boud has developed the concept of ‘productive reflection’ which addresses some of these concerns and ‘engages with the context and purpose of work and, most importantly, with the imperative that reflection in such settings cannot be an individual act if it is to influence work that takes place with others’ (2010: 33). These arguments resonant with many of the processes of professional learning, self-regulated learning and the affordances of mobile technologies, the subjects of this project. They suggest critical reflection may have the potential to lead to significant learning by teachers when it is augmented by the observations of colleagues and mentors and supported through the appropriate use of collaborative technologies (Leach & Moon, 2000; Pollard, 2002). c. Knowledge construction Teacher learning involves a disposition towards action, rather than a passive process of learning, and the co-construction of knowledge is one of the processes where this in enacted by teachers (Burbank & Kauchak, 2003; Dalgarno, 2001; Kelly, 2006). ‘Technical rationality’, is the antithesis of this, a term adopted by Schön to describe an impoverished model of professionalism in which the role of the teacher is essentially instrumental in nature, concerned to implement knowledge constructed by others (Schön, 1995). In this two tiered hierarchy teachers are defined as knowledge implementers rather than knowledge generators, a role reserved for researchers and


37 / 78

academics (Elliott, 1991). This model of teacher learning privileges a hierarchy of knowledge in which theoretical and propositional knowledge is superior and more important than knowledge which is created by teachers themselves. In constructing their own learning teachers develop a variety of different knowledge domains to supplement their existing content knowledge. This process starts when individual teachers tinker with a new technique or modify an existing approach within their own teaching context and then share the outcomes with colleagues where it ‘becomes more systematic, more collective and explicitly managed…and transformed into knowledge creation’ (Hargreaves, 2000: 231).

3.4.2. Contexts of teacher learning a. Situated

The situated perspective on teacher learning is rooted in socio-cultural traditions

emphasizing the importance of context or situation in relation to teacher learning (Putnam & Borko, 2000). The situated perspective holds that learning is rooted in particular contexts although these are not necessarily associated with geographical notions of place, but rather they can include social structures, situations and settings (Brown et al., 1989). This perspective contrasts sharply with the traditional cognitive approach in which learning is fundamentally linked to the manipulation of symbols and other representational artefacts (e.g. language) largely in the mind of the individual. Contexts for teacher learning vary according to the nature of the learning taking place. In some instances the ideal context will be work-based, but it has also been noted that teachers need to be removed from their workplaces in order to encourage thinking and learning that is not constrained by the dominant ‘discourse communities’ (e.g. staff rooms) into which teachers are enculturated through their practices: Teachers’ knowledge is situated, but this truism creates a puzzle for reform. Through what activities and situations do teachers learn new practices that may not be routinely reinforced in the work setting? (Sykes & Bird, 1992: 501).


38 / 78

It is likely different contexts or settings will be more conducive to certain types of teacher learning than others. Correlating these two variables should result in more effective teacher learning. For example, removing teachers from their working contexts might be effective if the purpose is to facilitate deep and critical reflective learning, unhindered by the presence of their existing discourse communities, such as the staff room. But the working context might be ideal if the purpose is to simulate authentic task based learning in an experiential environment. Under the banner of the situated perspective work based learning is gaining considerable popularity as a means of understanding the importance and role of tacit learning which takes place on a day-to-day basis, although it has received surprisingly little attention in terms of teacher learning (Boud & Hager, 2010). The potential value of work based learning for teachers is hard to deny but it is seriously under researched and difficult to grasp, given the tacit, almost hidden nature of what takes place in schools and particularly within the sanctity of the classroom. According to Boud (2010) learning is a normal and natural part of work, not a separate activity which is undertaken elsewhere or at specific intervals outside of the workplace. It arises on a day-to-day basis as teachers address problems and challenges in their classrooms rather than through formalized activities alone which are often artificial and inauthentic. In this sense Boud does not conceptualise theory and practice as entirely separate but rather, like Schön, as flip sides of the same coin. Again, this perspective reinforces the emerging consensus amongst the socio-cultural theorists that learning is an outcome of participating in specific practices, within particular communities (Kelly, 2006; Wenger, 1998). b. Collaboration

Membership of specific discourse communities and enculturation into Communities of

Practice (Lave & Wenger, 1991; Wenger, White, & Smith, 2009) are both powerful forms of social learning for teachers. These entail more than just encouragement from other colleagues and recognise the role other individuals and groups play, both in what is learned and how it is learned (Putnam & Borko, 2000). Rogoff describes the process as one of ‘participatory appropriation’ in which both the member and the community are transformed by the individual's participation that dissolves the boundary separating participants from their context (1993: 153).


39 / 78

But teachers are often nomadic, isolated individuals, frequently working alone in their own classrooms rather than as part of a team and this mitigates against their membership of such groups. Discourse communities and Communities of Practice are recognised as having both the influence to generate radical alternative perspectives for their members or to maintain the status quo by enculturating new members into ‘traditional school activities and ways of thinking’ (Putnam & Borko, 2000: 8). The ethos and culture of these communities are therefore vital barometers in determining whether teacher learning will be progressive and outward looking, or essentially conservative and resistant to change. c. Mediation Drawing heavily upon the socio-cultural traditions of learning, the situated perspective also identifies learning as distributed between people, groups/systems and artefacts or objects (Wertsch, 1991). Whilst schools tend to focus heavily on the individual conception of cognition, mobile technologies with their networking affordances promise to offer extended support for a distributed view of cognition, particularly through the mediating impact of tools and artefacts such as collaborative and creative editing tools: Tools are critical artefacts for enhancing and transforming individual cognition and also distributing it across a system, enhancing capacity for innovation and invention (Wertsch, 1991).

Artefacts are defined as tools and symbols which human beings have developed over time

enabling them to undertake complex tasks in ways which would not otherwise be possible. They are tools that liberate humans from working entirely in their own mind and in doing so they enable humans to off-load and share some of their cognitive load. This kind of learning is less likely to be concentrated in single individuals and more likely to be dispersed across individuals, groups and nodes (Siemens, 2009). Examination of such distributed patterns of cognition reveal that learning can occur in highly dispersed ways yet the results from these disparate learning opportunities can be amassed quickly and become the basis for informed action (Wenger, White, & Smith, 2009).


40 / 78

3.5. Summary of professional learning

Teacher learning has been defined as a complex, multifaceted phenomena, resistant to simple formulas, descriptions or standardization (Fisher et al., 2006). Many of the tacit and informal elements of teacher learning remain largely under researched and under-theorised whilst attention has focused on what has been traditionally defined and recognised as a largely formal phenomenon centered around training, professional development events and courses often occurring outside of the context where teaching occurs (Boud & Hager, 2010; Day, 1999; Grundy & Robinson, 2004). More recent discourses have promoted alternative models of teacher learning located in less formal spaces including the workplace, and these discourses recognise how social processes have a significant role to play in understanding the phenomenon of teacher learning which is also rooted in particular contexts and situations (Putnam & Borko, 2000; Resnick, Levine, & Teasley, 1996). This section has illustrated the significance of this socio-cultural and situated perspective in the context of this project which features a variety of workplace settings in schools. We adopt a hybrid perspective on the phenomenon of teacher learning which recognises how learning can be both a cognitive process which occurs in the mind of individual learners and also a highly collaborative and social one which can be distributed across individuals and artefacts and is highly contingent upon particular circumstances and contexts. Teacher knowledge takes many different forms and it is important to understand these in order to understand how teachers learn. The most explicit forms of teacher knowledge are readily available and often codified, to such an extent they can be easily transmitted and transferred. Teachers also access and construct other forms of knowledge which are more likely to be tacit and difficult to identify. These forms of knowledge are often constructed through participation in particular groups and communities. They are also likely to be highly contextualized and specific to the settings in which they were created. These differences can be summarized as knowing as a state of mind (cognition) and knowing as a social and situated process (socio-cultural). In the final analysis the important aspect in all of this, for the purposes of this particular project, is that teacher knowledge is conceptualised as dynamic and open to change rather than fixed and preordained. It is constructed by teachers in a process which is active and participatory (Fisher et al., 2006).


41 / 78

Teacher learning itself has been defined as consisting of two associated themes which are referred to here as the processes and the contexts of teacher learning. The contexts are defined as the settings or wrappings which support and enable particular process of learning and these were identified as being situated, collaborative and mediated in nature. In turn the processes of teacher learning were defined as including experience (learning experientially), knowledge construction and reflection. Various aspects of teacher learning discussed in this section are likely to be supported and enhanced through the appropriate application of technology and mobile technologies in particular. Technology presents opportunities to support both the processes and contexts of teacher learning which have been outlined above and this is the theme for the next section. In particular the next section explores how mobile technologies are underpinned by a variety of different affordances or features which act as catalysts in supporting particular forms of learning activity. In this case the focus is on the learning which teachers undertake to support their professional practice.


42 / 78

4. Mobile technologies and professional development 4.1. Introduction

Mobile learning (m-learning) has been described in multiple ways but most commonly

descriptions centre around the notion of learning that is mediated by a mobile device. This in turn heralds a new stage in the evolution of technology-enhanced learning (TEL) that foregrounds new mobile learning spaces and opportunities to extend and continue learning experiences across different scenarios or contexts (Clough et al., 2008; Frohberg et al., 2009; Looi et al., 2010, 2011; Sha, Looi, Chen & Zhang, 2011; Zhang et al., 2010). Over the past five years mobile and portable handheld devices have emerged as powerful multipurpose mini computers in their own right, incorporating increasingly sophisticated multimedia, social networking, communication and geolocation (GPS) capabilities. Consequently the learning that is associated with these devices (referred to in this paper as ‘m-learning’) offers numerous opportunities for educators as well as serious challenges and barriers for education as a whole. Early projects and interventions with mobile technologies focused largely on the capabilities of the device alone, rather than the perspective of learners or its potential value as a pedagogical tool in educational contexts (Traxler, 2007). Building upon the definition of learning outlined in the previous section of this framework, however, this section explores mobile learning and its potential to support professional learning and self-regulated learning from a socio-cultural perspective. This posits that learning is shaped and modified by the cultural tools and artefacts created by humans (e.g. language and writing) and that, reciprocally, such tools are themselves modified by the ways in which they are used for learning (Kearney, Schuck, Burden & Aubusson, 2012). Learning is therefore conceptualised as a highly situated and social activity that is mediated and developed by interactions between individuals and groups, using cultural tools such as language, writing and more recently computers and mobiles technologies (Bakhtin 1981; Vygotsky 1978; Wertsch, 1991).


4.2. What is mobile learning?

43 / 78

Numerous attempts have been made to define and categorise the phenomenon of mobile

learning and many of these are complementary and overlapping in nature. Most of the existing frameworks and models of mobile learning highlight its collaborative and social nature, drawing attention to the affordances of mobile devices that allow learners greater opportunities to deepen the contextualisation and meaningfulness of their learning (cf. Kearney, et al., 2012; Koole, 2009; Sharples, & Vavoula, 2007). Others have chosen to focus on the highly personalised nature of learning associated with a personal device like a mobile phone or tablet computer (Klopfer, Squire, & Jenkins, 2002; Pachler, Cook, & Bachmair, 2010; Traxler, 2009), along with the ability of the device to customise the learning experience to the individual, for example by use of context sensitive capabilities. Another common theme in the research literature is portability both of the device and the learner who is using it with greater opportunities to undertake ‘seamless’ learning whereby a task might be initiated in one context (e.g. school), conducted in another place (e.g. on the bus or train home) and completed in yet another setting (e.g. the home or coffee shop). Communication and conversation mediated by a mobile device is also a popular theme in many projects and research interventions, focusing on how mobile devices support and enhance different layers of conversation and exchange of ideas, particularly in language settings (cf. Pegrum, 2014; Kearney, et al., 2012).

All of these themes foreground the importance of context in relation to mobile learning. Not

only the physical or geographical contexts that typify traditional learning settings, but also the social, cultural, spatial and temporal contexts that make mobile learning so flexible, and potentially different from traditional learning (Cook et al., 2008). Time and space are key considerations in this respect when attempts are made to define the unique characteristics of mobile learning. In traditional learning contexts learning is rigidly bounded by two fixed and immutable considerations: time and space. Teaching and learning tends to occupy fixed timeslots (the school day and the timetable) located within bounded spaces such as the classroom and the school (Traxler, 2009). Mobile learning has the potential to transcend these fixed certainties since geographical space is no longer the only place learning can occur (e.g. watching a video tutorial on a device on a plane) and rigid time constraints are no longer as restrictive when learners have the freedom to access resources and learning materials (e.g. online MOOCS) at their own leisure and pace. The implications


44 / 78

for learning are considerable, even if they have not yet been fully realised, as Kearney, et al. (2012) have noted: In blurring the physical and scheduled personality of institutional-based learning, time-space implications of m-learning open up opportunities for a wide variety of pedagogical patterns. Mobile technologies thus enable learning to occur in a multiplicity of more informal (physical and virtual) settings situated in the context about which the learning is occurring (Kearney, et al, 2012, p. 4)

4.3. The iPAC mobile learning framework In this section we describe a framework for the pedagogical use of mobile technologies which has been developed and validated by researchers in the UK and Australia based on three distinctive features or ‘signature pedagogies’ which include: Personalisation; Authenticity and Collaboration (PAC). The framework was originally developed in 2012 based on previous pilots and individual projects and it has since been used in numerous mobile learning projects as the pedagogical framework for research and further study (Kearney, & Mather, 2013; Viberg, & Gronlund, 2013)


45 / 78

Figure 2: the iPAC framework (Kearney, Schuck, Burden and Aubusson, 2012) The iPAC framework as represented in Figure 2 above shows how each of the three distinctive pedagogies associated with the use of mobile technologies is also affected by the timespace issues described above. Each of the three pedagogies is described in further detail below. a. Personalisation Personalisation is a well-established feature and proclaimed benefit of e-learning where it is associated with greater learner choice, agency, self-regulation and customisation. These learning benefits are also features of mobile learning where learners have considerable control or agency in respect to where they learn, the pace and time at which learning takes place and the objectives of the learning. This is especially evident in games based learning activities which are now commonly undertaken on and through mobile devices.


46 / 78

Mobile devices can also be used to provide a more customised or individual learning experience in which learning content is tailored to the individual learner rather than a full cohort or class. Context aware apps and tools such as maps and some learning applications (e.g. Map my Walk) are able to acquire information that is specific to the learner using the device (e.g. where they are located or what they are doing) and this can be used to make a uniquely personalised learning experience. Given these various features or elements the Personalisation construct is sub-divided into two substrands which are (a) agency and (b) customisation. Each can be measured or gauged separately using a survey tool which has been designed specifically for this purpose as part of an Erasmus+ project (see www.mttep.eu). b. Authenticity It is widely recognised that learners are more engaged and motivated when they are presented with, or challenged by, authentic tasks and activities that have real world relevance and personal significance (cf. Burden, & Kearney, 2016; Radinsky, 2001). Authenticity has many different aspects but in terms of mobile technologies the iPAC framework identifies three separate elements associated which make learning more authentic. These include (a) the tools that learners can access through the mobile device (b) the tasks that teachers can set learners using the mobile device and (c) the location or setting that is mediated through the device. Tool authenticity refers to how learners can use a mobile device to access tools or apps that approximate to those that might be used by a professional undertaking a task such as data logging tools, health monitoring apps (e.g. blood pressure and heart rate) or scientific tools such as an oscilloscope or light meter. Task authenticity refers to the opportunities to participate in the practice undertaken in real world communities of practice. Some of these may be simulations which take place in the classroom which is used as a practice field but others may involve activities where students join real groups of professionals working on an actual task such as citizen science projects. Finally it is possible to use mobile device is actual settings such as during a museum visit or a fieldwork trip and this forms another authentic use of the device: a situated model of authenticity. Situated learning of this


47 / 78

nature is often considered to be more realistic and significant for the learner since it is rooted in the actual context and setting that is being studied. c. Collaboration Collaboration is a central feature of socio-cultural activity which places considerable emphasis on scaffolding and learning interactions between peers, some of whom will be more capable (experts) than others (novices). From a sociocultural perspective conversation and dialogue are fundamental tools that are used to negotiate meaning (Vygotsky, 1978) and the iPAC framework foregrounds these elements from the perspective of mobile learning. Mobile devices enable learners to engage in high levels of collaboration by conversing with other people (both orally and in other non-oral formats) and my sharing resources (data exchange). These features are integral to social networking and social media and the immediacy of mobile devices mean learners can engage in conversation and communication spontaneously (e.g. through applications like Twitter).

4.4. The use of mobile technologies in the context of professional development Despite the growing popularity and interest in using mobile technologies in educational settings, especially formal education such as schools, their use to support professional development and learning for teachers is surprisingly limited given the obvious benefits and opportunities which have been explored in previous sections of this review. In this section we examine how mobile technologies have been used to support the development of professionals in a diverse range of professional settings such as nursing, caring, industry and to a lesser extent, teaching, before considering implications for the design of a mobile learning app to support teachers in their use of self-regulated learning.


48 / 78

A number of studies have investigated the use of mobile technologies to support the professional development of individuals and groups and many of these studies exemplify the three central constructs identified in the iPAC framework. Ally, Samaka, Impagliazzo, Mohamed, and Robinson (2014) explored how mobile technologies were used in the petroleum industry to develop a range of practical and communication skills of gas and oil workers. An app was developed which enabled workers to access training materials on their mobile phone. This included tools such as a scanner which enabled them to read a bar-code on particular items of machinery which then linked them to short video clips explaining a particular maintenance procedure. Time and geographical restraints prevented the use of traditional professional development activities (e.g. a full day course at a central venue) and so the company developed an app which could be used by workers in situ. This proved to be extremely popular since it provided access to the training materials on a ‘just in time’ basis when it was needed (customisation). The situated nature of the experience also enabled the workers to apply what they had learned almost immediately they had learned it and this was also considered to be a distinct advantage over traditional training approaches. Additionally learning was personalised since every worker was able to proceed at their own pace using the app only when it was appropriate or necessary. An incidental bonus of using the app, which was not considered by the developers, was the increased level of interaction that took place between workers using the app. They often shared what they had learned with each other using a communication challenge built into the app (collaboration ⇒ data sharing).

Similar findings have been reported in various caring professions such as healthcare and

nursing where there is growing interest in the use of work-based professional learning that does not remove the practitioner from front line activities to undertake professional development (Marsick, Watkins, & Lovin, 2011). In Canada it is expected nurses will regularly maintain their own competences through the use of self-directed learning and mobile technologies are seen as a means to achieve this in a work-based context. A recent study by Fahlman (2014) echoes those of the previous study in the petroleum industry since nurses also used a mobile device to access training materials such as podcasts, video clips and journal articles which they read or watched in the workplace. They liked the ‘just in time’ nature of this kind of learning which they contrasted with the monolithic approach to professional development they had previously experienced. Ownership and self-pacing both emerged as features of this kind of learning that mobile technologies provided.


49 / 78

Despite these findings and generally positive attitudes towards the use of mobile

technologies to support professional development, especially in work-based contexts, there is only limited evidence to suggest this approach has yet been widely adopted by teachers and educators. A few studies have theorised about the application of mobile technologies for the professional development of educators (cf. Ally, 2009; Wishart, 2009; Leroux, Roobroeck, Dhoedt, Demeester & De Turck, 2013) but only one study that we are aware of details the use of mobile technologies for professional development based on actual empirical data. This study, undertaken in 2009, found evidence to show that teachers used their own mobile devices to capture instances of activities in classrooms (e.g group work by students) which they then reflected on at their leisure after the event. This was not common amongst teachers and was certainly not considered an embedded practice despite its value to those who did use it in this way. The authors suggest that teachers are largely unaware of the potential value of mobile technologies as tools to support both reflection on action (away from the classroom) but also reflection in action which takes places almost as soon as the activity has finished (Aubusson, Schuck, & Burden, 2009). Despite this they urge teacher educators and those responsible for working with teachers on their professional development to preserve with this approach since ‘the value of harnessing the power of mobile technologies lies in their capacity to generate collaborative professional learning involving reflection, production, synthesis and analysis (Aubusson, et al., 2009, p. 235).


50 / 78

5. Self-regulated Learning and the use of Mobile Technologies Information and communication technologies are widely applied in education. The use of digital learning has become popular and could fundamentally change the provision and characteristics of learning opportunities, as well as the teaching and learning strategies in the education sector. However, digital learning raises several challenges for institutions, teachers, learners and policy makers, especially in primary education, where the student population is much more heterogeneous than in other educational levels. Digital learning environments are seen as potentially promising not only to increase knowledge but also to foster autonomy among students. Despite the potential added value underlying the use of digital devices, research has shown how still little learning effects are reached when students work in such environments (Azevedo, 2005). It is suggested that whereas some students foster knowledge development through for example mobile learning applications (Jacobson & Archididou, 2000), other students experience difficulties to develop knowledge (Moos, 2009) and this because of for instance the higher degrees of freedom and less guidance the students receive when learning. Moreover, general usability problems like cognitive overload and distraction were noted that can put a burden on the learning processes with digital media (Azevedo, 2005; Scheiter & Gerjets, 2007). In order to overcome these burdens when learning with digital technology, a clear understanding of learning in these environments is necessary so one can bridge the pitfalls that are described as endangering the potential learning outcomes. Not only are cognitive processes demanded but these need to be delineated with the appliance of metacognitive as well as motivational skills (Land, 2000). A strong focus on selfregulated learning can capture this reciprocal interaction between the student and the digital learning environment. Despite the tremendous high number of digital applications for mobile technology designed for educational purposes or contexts, not a single one addresses and integrates the complex processes inherent to the development of self-regulatory skills of primary school children and teachers. The novelty and added value of the tMAIL project consists in the holistic approach to mobile learning in teacher education. Insights of different research fields (mobile learning, educational design, self-regulated learning environments, teacher education and assessment, professional development of teachers and learning analytics) are combined and translated to the


51 / 78

field of teacher education. The project investigates these processes occurring within the triangle of learners, teachers and school policies consolidated into one single mobile learning app.

Mobile learning assumes learners will be in continual motion (Sharples et al., 2007) but this

has ramifications that extend beyond the physical and geographical freedoms that continual motion implies. In particular mobile devices hand considerable autonomy and agency to the learner to decide when, where, and how they learn, but this also raises serious questions about who is responsible for what learners chose to learn, the strategies they employ to learn and where this all takes place? Is this still the teacher, as in traditional models of learning; is it the student, or is it the instructional designer who designs the apps that learners use on their mobile device? (Looi et al., 2011). These questions are particularly pertinent for younger learners who will feature in the TMAIL project. In this respect designing an app that aims to satisfy learners’ needs will fulfil the necessity of having a ubiquitous access to training material developing consequently new customised models of learning.


52 / 78

6. Learning analytics as a means to support self-regulated learning 6.1. What is learning analytics? Learning analytics is a rather young field in educational technology research. It parallels socalled “Big Data” developments in other sectors such as industry and commerce, where business intelligence has been used for years to better understand customers and to improve services and efficiency. Learning analytics approaches, similarly, are based on the abundance of (digital) data available in modern databases. Early antecedents of educational technology used website analytics and statistical approaches to extract useful information from digital systems and platforms. From their earliest version on, practically all major online learning platforms (Moodle, Blackboard, etc.) contained some sort of usage statistics module, where mostly system administrators could see the activities of learners in the virtual learning environment (VLE/LMS). Later it was found that this information would be more useful in the hands of teachers or even learners, so, in an early paper, Retalis et al. (2006) conceptualised this into learning analytics and also included a perspective on network learning scenarios. In a parallel development to the emergence of learning analytics, educational data mining (EDM) formed as a scientific discipline. Its promoters too approached large datasets with the goal to identify patterns and extract useful information from learner data. For some time, this led to a discussion between two emergent scientific communities and their understanding of the differences (Siemens & Baker, 2012) and goals (Drachsler & Greller, 2012). In the first ever massive open online course on learning and knowledge analytics (LAK11 MOOC)1, therefore, the two disciplines were equally represented. When the New Media Consortium in its 2012 Horizon Report (Johnson et al., 2012) anticipated learning analytics as a prospective game changer for education, the attention quickly turned to learning analytics, as was evident in the Educause Analytics Sprint in summer 20122 and other events that year. Figure 1 below illustrates the split in attention over time between learning analytics (blue) and EDM (red), the latter still having a dedicated membership.

1 2

https://learninganalytics.net/syllabus.html http://www.educause.edu/events/educause-sprint-2012


53 / 78

Figure 1. Google Trends interest graph on LA (blue) vs. EDM (red) over time.

The discussion between LA and EDM has now settled on a distinction between the “soft” learner oriented scope for learning analytics, and a “hard” data mining back-end approach for educational data mining.

6.2. Definitions and conditions for learning analytics There is no universally accepted definition for learning analytics. Rather, the scope of learning analytics is captured in different ways and encompasses various aspects of using learner data. The following now widely used definition was given by George Siemens in the call for papers for the Learning Analytics and Knowledge conference in 2011 (LAK11): “Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” (Siemens & Long, 2011). A somewhat more learner-centred definition is the following from van Harmelen & Workman (2012): “Learning analytics is the application of analytic techniques to analyse educational data, including data about learner and teacher activities, to identify patterns of behaviour and provide actionable information to improve learning and learning related activities.” This latter wording includes not only the usage of learner data, but defines the information as “actionable”, meaning that instead of gaining some abstract knowledge about learning and its environment, some consequences (direct benefits) should be derived from analytics for the learners that are realistic to be implemented in practice. “Data about learners” raises another important issue for learning analytics. What data are we talking about? Different types can be distinguished, such as personal data of learners, which


54 / 78

describes a learner’s background, e.g. personal disposition, preferences, origin, ethic and social background, previous knowledge, assessment data, age, gender, etc. Much of this data is typically held in Student Record Systems of educational institutions. On the other hand, while engaging in learning activities, learners also produce educational data directly and indirectly, which manifests itself in behavioural traces, learning paths, as well as in learning artefacts. Behavioural data, including socially motivated data traces (e.g. connecting to someone, liking, sharing, etc.), can be summed up as “contextual attention metadata” (cf. Schmitz et al., 2011; Ochoa & Duval, 2006). This type of data can be used for recommender systems and adaptive learning environments as it reflects what people do and the way they proceed, rather than who they are. In a learning analytics context, such data traces can also be used to compare indicators against benchmarks as a tool to measure the learning trajectories and progression of a learner. On the basis of the above data types, some people have argued in favour of differentiating between academic analytics and learning analytics – the former used largely for institutional management, the latter for improved learning (Siemens et al., 2011). Fergusson (2012), on the other hand, sees learning analytics as emerging out of academic analytics in an evolutionary development. Academic analytics can best be understood in contexts where learner and teacher data is used by policy makers independently from the personal learning process, for example, for funding purposes or to create more efficient and effective organisational processes and structures. Naturally, also academic analytics is focussed on student success, but more with respect to efficient use of resources within the institution rather than optimised pedagogy. More recently, however, the two domains have not been separated strongly in the minds of educational data scientists, and, with no distinct borderline, academic analytics is nowadays regularly subsumed under the term learning analytics. In the traditional institutional data landscape, it seems best to distinguish between purpose and beneficiaries of data services. To illustrate purpose and data clients of institutional data services, Greller & Drachsler (2012) use the following hierarchical pyramid to show the key beneficiaries (data clients) and usage areas of learning analytics services, see figure 2:


55 / 78

Figure 2. Data clients and data subjects in HEIs (Greller & Drachsler, 2012)

The diagram simplifies the use of student data by teachers (to support student learning)

with the option of self-reflection in case of self-regulated learning scenarios. Teachers can also benefit from their own performance and action data to reflect or to improve their didactic approaches, but they themselves are data subjects to the institutional regulatory processes and objectives. Data across and between institutions can then effectively be used to influence government policy towards the sector as a whole. It should be noted that in a SRL context, students can reflect on their own performance data or ask for teacher support within the classroom environment (Greller & Drachsler, 2012). From the above presented definitions, we can derive some conditions that encircle the common understanding of learning analytics. (1) Learning analytics uses digital (or digitised) datasets of large proportions (big data). This is a distinguishing feature from empirical research in the way that it does not use a pre-selected sample of users as a basis for generalisations. Instead it is applied across an entire learner population. (2) Learning analytics is automatised. Learning analytics data collection and algorithms run largely in the background by tracking learner behaviour in digital environments. On some occasions, nevertheless, direct user input may be required. (3) Learning analytics is continuous. Again, this makes it different to traditional empirical approaches, which are typically based on a one-off data collection, or, in some cases, repetitive/cyclical data gathering. In learning analytics, continuous data gathering is connected to a just-in-time and on-demand approach, where students or other data clients can spontaneously interrogate the system to find their “position”.


56 / 78

6.3. Prediction, semantics, quantified self, and reflection amplifiers

Learning analytics is used in two major ways: reflection and prediction (Greller & Drachsler, 2012). Prediction refers to anticipating future learner behaviour from past tracking data as well as profile data. This may lead to adaptive learning environments taking decisions on behalf of learners on how to best progress in their learning paths. It may also highlight learners that are in danger of falling behind or dropping out of a course, so timely remedial actions can be taken. However, predictive decision algorithms are not very conducive for a SRL environment, which emphasises learner independence and ownership of their own learning processes. Nevertheless, together with a learning semantics approach (cf. Bordes et al. 2014), which supports the wider use of learning resources, including open educational resources (OER), highly personalised recommendations can be built to enable learners to take better informed decisions in their choice of learning activities and learning objects. The meaningful combination in tMail of learning analytics supporting process decisions and learning semantics for learning materials constitutes a highly innovative support service for personalisation in SRL. For self-regulated learning two strands from learning analytics are particularly relevant: the “quantified self” approach and reflection amplifiers. Both are actually two sides of the same coin, i.e. a didactical approach based on self-reflection and self-regulation. The “quantified self” (Wolf, 2009; Duval, 2011) follows the trend to use personal sensors (e.g. smart watches and other wearables) to monitor one’s own sleep, diet, movements, and other behaviours in order to gain self-knowledge and new insights into one’s own wellbeing. The analytics used here is primarily directed towards behavioural learning, with the potential of changing a behaviour according to the information received from previous actions. In a computerised learning environment or a mobile learning app, the engagement and interactions with applications, learning resources, locations or peers can be measured. From it, the learners themselves can determine appropriate actions to improve engagement or performance (e.g. read more on something). In tMail, a dashboard visualisation and recommendation service models direct in-time feedback to learners (i.e. in-service and pre-service teachers) supporting them in making sense of their own action data by recommending packages of courses and explaining why they are recommended to them. Reflection amplifiers are based on the principle that providing people with feedback loops on their behaviour helps them reflect and potentially self-correct any perceived shortcomings (Verpoorten, 2012). Reflection in a learning analytics context is seen as the critical self-evaluation of


57 / 78

a learner as indicated by their own datasets in order to obtain self-knowledge (Greller & Drachsler, 2012). However, reflection may also be stimulated by critical self-evaluation based on other people’s datasets (e.g. comparing yourself to others, or teachers reflecting their actions through the performance of their students). The deliberate use of reflection amplifiers in educational settings, therefore, can help the SRL process by providing additional guidance as well as possibilities to reflect on and position the learner’s performance (e.g. as compared to peers). To maximise the positive impact, indicators are needed that guide the continuous progression and meet learners at every level. Arrigo et al. (2015) propose a task - interaction framework for mobile learning using learning analytics techniques based on the relationship of different types of mobile learning factors and the pedagogic value and relevance they present to support educational decision making. The tMail mobile app takes note of this framework in the design and development process by using mobile factors such as context of the learner (alone or in class) or the subject area (previous knowledge / attitude of the learner) and the recommendations or feedback it provides through adaptive in-time analytics.

6.4. Emerging standards and codes of practice There are a number of issues with learning analytics that led to some initial attempts of establishing a common set of agreed norms surrounding the collection and processing of data in an educational context. One particularly pressing problem is that of ethics and privacy, another that we will mention here is interoperability. There is much concern about ethical use and privacy wherever big data services are employed. Often, already the collection of personal data is seen as an infringement of rights to privacy, as people generally become more sensitive to observation and surveillance practices on the Internet. To alleviate the random exploitation of data by large companies that indiscriminately collect and sell user data, the European Commission has just recently released the new General Data Protection Regulation (GDPR)3, which updated their previous Data Protection Directive from 1995. Several frameworks for learning analytics have been put forward to deal with ethics and privacy (Prinsloo & Slade, 2013 and 2015; Pardo & Siemens, 2014; Hoel & Chen, 2015; Steiner et al., 2015). In spite of some ongoing hesitations, there are safe ways to establish trusted learning analytics by 3

http://www.consilium.europa.eu/en/policies/data-protection-reform/data-protection-regulation/


58 / 78

following a common set of recommendations (Drachsler & Greller, 2016). This also incorporates the widely recognised policy for learning analytics at the Open University of the UK (Open University, 2014), that has been followed by the UK JISC (Sclater & Bailey, 2015). A similar code of practice was released in the Netherlands by SURF (Engelfriet et al., 2015). The guiding principles for trusted learning analytics according to Drachsler & Greller (2016) are (summarised): -

Transparency about the purpose: Make it clear to the users what the purpose of data collection is and who will be able to access it. Let users know that data collection is limited to fulfil only the intended purpose effectively.

-

Informed consent: Get users to agree to data collection and processing, by telling them what data you are collecting, for how long data will be stored, and provide reassurance that none of the data will be open for repurposing or use by third parties. According to the GDPR, approval can be revoked and data of individual users must then be deleted from the store – this is called “the right to be forgotten”.

-

Anonymise: Replace any identifiable personal information and make the individual not retrievable. In collective settings data can be aggregated to generate abstract metadata models.

-

Data security: Store data, ideally encrypted, in a (physically) safe server environment. Monitor regularly who has access to the data.

Another, less prominent issue for learning analytics is interoperability of data and applications. It was recognised that the traditional analytics approach mentioned above in section 6.1. leads to data silos and fragmentation of learning analytics information that show only the limited part of the learning process that happens in the institutional platform. Nowadays, however, learners are most often not restricted to only a single learning environment (VLE/LMS), but interact in a seamless learning landscape using mobile and desktop computers, a range of institutional and cloud based services or apps, formal and informal learning resources. Efforts have, therefore, been undertaken to overcome this situation by establishing a common interoperability standard specification called Tin-Can4 or xAPI (Cooper, 2014; Kevan & Ryan, 2016). It aims to capture learning across online and offline learning experiences and provides the data in an interoperable format, 4

https://tincanapi.com/overview/


59 / 78

which can also be stored externally to the application it uses, for example, in a learning record store, that allows personal ownership and self-management of an individual’s learner data. This makes the data reusable and transferable, thus allowing for a more complete picture of learning as it happens. xAPI is now being implemented in various tools, especially where the intention is one of seamless learning across different services or with transferability and user ownership in mind. A good example is the Connected Learning Analytics Toolkit developed by the Queensland University of Technology in Australia (Kitto et al., 2015).

6.5. Visualisation of learning analytics information For self-regulated learning, either in an independent informal learning scenario or with teacher support and guidance, the “actionable” information derived from algorithmic analysis has to be presented to the data client in an easy-to-understand way. Keim (2002) states: “for data mining to be effective, it is important to include the human in the data exploration process”. As has been illustrated above in the data client pyramid (fig.2 above), there are different audiences for this kind of information, driven by different roles and purposes. Very little scientific literature exists as yet regarding to what visualisations are pedagogically supportive and what competences are required for interpreting them and turning them into interventions. A good theoretical overview about the visual language of data is given by Berinato (2016) and Casilli (2010). For learning analytics based on CAM data (see above 6.1), a web-based visualisation platform GLASS (Gradient’s Learning Analytics System) has been proposed by Leony et al. (2012). It consists of script modules that provide visualisations to a dashboard and claims extendibility to multiple data stores and different modules. Another approach is that of Hernández-García et al. (2015) which is more focussed on social network analysis. An important aspect in this area is that for visualisations to be constructive and useful to learners and teachers in SRL, it has to be relevant, meaningful and connected to the expected learning outcomes. Kump et al. (2012) introduce Knowledge Indicating Events (KIE) as part of an open learner model combined with information visualisation as a method to measure user knowledge levels rather than merely behaviour. Govaerts et al. (2010) as well as Verbert et al. (2014) stress the value of visualising learner activities for self-reflection. Dashboards are a useful method to show a variety of visually represented information to the learner. It typically consists of declarative information on what the user has done or where they stand in a conceptual framework. Here are some examples from mobile applications (fig.3):


60 / 78

Figure 3. Dashboard visualisation on mobile interfaces

In a (teacher-led) SRL scenario, two levels of views are conceivable: Firstly, the reflective view for the learners themselves, and, secondly, a level of view for the support person (teacher). Considering a classroom environment for SRL, as is the intention in the tMail project, the teacher view can involve two perspectives: the individual learner and the group. In order to take privacy and data protection into account, we see two possibilities for implementation: (a) learners (i.e. in-service teachers or trainee teachers in tMail) can by themselves provide views of their learning status and progress to a support person of their choice as a snapshot via the “share” feature. This is unproblematic in terms of data protection, even though it is obvious who the data is about, because the users themselves are in control what they share and who they share it with. (b) An aggregated perspective can be presented to the teacher (i.e. teacher educators in tMail), showing anonymously the status and progress of the entire group – allowing them to perhaps measure the general impact of the learning application. Because we can assume that in a formal environment there exists a learner – teacher contract of trust, it is possible to share personal information with the consent of the learner, so pedagogues are able to support and help the further learning process.


61 / 78

7. Conclusions: Implications for the development of a training course on self-regulated learning using mobile technologies

The desk research – reported in this paper – did not find any previous research combining

SRL teacher training and the use of mobile technologies. Therefore, the tMAIL project will be a pioneer in the combination of SRL, teachers’ professional development and the use of mobile learning technologies. To conduct a successful intervention, the research presented in this report will be used as a main resource for the mobile app and course development. First, regarding SRL, as has been presented in the first part of this theoretical framework, the tMAIL intervention will consider the teacher and school determinants, the features that make SRL interventions more successful – especially in primary education – by following the finding of Dignath et al. (2008), while considering the latest advances in SRL measurement (i.e. the third wave that combines intervention and measurement). Second, taking into account that the ubiquitous access to learning material has given rise to a hybrid conception of teacher learning, tMAIL will work on the understanding of contemporary cognitive processes. It follows that the project will design a more customised and individualised way of learning including the use of mobile technology, which reshapes the traditional notion of SRL along with the advancements of mobile technologies. Additionally, by bringing in entirely new approaches and technologies, tMail works to innovate the pedagogic application of mobile learning with advanced personalisation in SRL. The joint use of analytics and semantics will aid the personalisation and adaptability of all types of learning paths, addressing directly each learner's needs, preferences and interests. tMail will, however, balance self-control with an intelligent recommender system to leave ownership of the learning with the learners and to enable meaningful interactions and interventions by teacher educators. The understanding of processes and contexts in which teachers learn will support the development of the mobile application able to satisfy new approaches and uses of mobile learning. Particularly, this development will take into account the element of mobility afforded by new technologies that allows alternative and innovative models of learning which are not limited by time,


62 / 78

space or situation. They are rather built upon a personalised and collaborative idea of professional development through mobile learning.


63 / 78

8. References Arrigo, M., Fulantelli, G., & Taibi, D. (2015). Challenges of Using Learning Analytics Techniques to Support Mobile Learning. International Association for Development of the Information Society. Ashton, J., & Newman, L. (2006). An unfinished symphony: 21st century teacher education using knowledge creating heutagogies. British Journal of Educational Technology, 37(6), 825–840. doi:10.1111/j.1467-8535.2006.00662.x Aubusson, P., Ewing, R., & Hoban, G. (2009). Action learning in schools : reframing teachers’ professional learning and development. New York: Routledge. Aubusson, P., Schuck, S., & Burden, K. (2009). Mobile learning for teacher professional learning: benefits, obstacles and issues, ALT-J, Research in Learning Technology Vol. 17, No. 3, 233– 247. Baeten, M., Kyndt, E., Struyven, K., & Dochy, F. (2010). Using student-centered learning environments to stimulate deep approaches to learning: Factors encouraging or discouraging their effectiveness. Educational Research Review, 5(3), 243–260. Bakhtin M. M. (1981): The dialogic imagination: Four essays. Bakhtin Perspectives on language, literacy and learning. Cambridge: Cambridge University Press. Bandura, A. (1971). Social Learning Theory. New York: General Learning Press. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, N.J.: Prentice-Hall. Bandura, A. (1999). Social cognitive theory: An agentic perspective. Asian Journal of Social Psychology, 2(1), 21–41. Berinato, S. (2016). Visualizations that really work. Harvard business review, 94(6), 18. Boekaerts, M., Pintrich, P. R., & Zeidner, M. (2000). Handbook of self-regulation. San Diego: Academic Press.


64 / 78

Bordes, A., Bottou, L., Collobert, R., Roth, D., Weston, J., & Zettlemoyer, L. (2014). Introduction to the special issue on learning semantics. Machine Learning, 94(2), 127. Bore A., & Wright N. (2009): The wicked and complex in education: developing a transdisciplinary perspective for policy formulation, implementation and professional practice. Journal of Education for Teaching, 35(3), 241-256. Borko H. Whitcomb J., & Liston D. (2009): Wicked problems and other thoughts on issues of technology and teacher learning. Journal of Teacher Education, 60(1), 3-7. doi: 10.1177/0022487108328488. Burden K. (2012): Crossing the Transformation Horizon: Conceptualising Teachers’ Learning and Transformations with the Affordances of Web 2.0. Unpublished PhD Thesis. University of Hull. Burden K. (2010): Conceptualizing teachers’ professional learning with Web 2.0. CampusWide Information Systems, 27(3), 148-161. doi:10.1108/106507411011054456. Butler, D. L., & Schnellert, L. (2012) Collaborative inquiry in teacher professional development. Teaching and Teacher Education, 28(8), 1206–1220. Bruce B. C., & Hogan M. (1998): The disappearance of technology: toward an ecological model of literacy. In D. Reinking, M. McKenna, L. Labbo, & R. C. Kieffer (Eds.), Handbook of literacy and technology. Transformations in a post-typographic world (pp. 269-281). Mahwah, N. J.: Erlbaum. Bryce, D., & Whitebread, D. (2012) The development of metacognitive skills: evidence from observational analysis of young children’s behavior during problem-solving. Metacognition and Learning, 7(3), 197–217. Butler, D. L., Lauscher, H. N., Jarvis-Selinger, S., & Beckingham, B. (2004). Collaboration and self-regulation in teachers' professional development. Teaching & Teacher Education: An International Journal of Research and Studies, 20(5), 435-455. Casilli, C. (2010). Information Visualization: an Overlooked Field; submitted as MA Media Psychology Capstone at Fielding Graduate University; unpublished. Cook J., Pachler N., & Bradley C. (2008): Bridging the gap? Mobile phones at the interface between informal and formal learning. Journal of the Research Center for Educational Technology (RCET), 4(1), Spring 2008.


65 / 78

Cooper, A. (2014). Learning Analytics Interoperability - The Big Picture In Brief. Learning Analytics Community Exchange. Available at: http://laceproject.eu/publications/briefing-01.pdf Day, C. (2006). Variations in the work and lives of teachers: relative and relational effectiveness. Teachers & Teaching, 12(2), 169–192. doi:10.1080/13450600500467381 Darling-Hammond, L., Meyerson, D., Lapointe, M., & Orr, M. T. (2010). Preparing principals for a changing world: Lessons from effective school leadership programs. San Francisco, CA: JosseyBass. Dembo, M. H. (2001). Learning to teach is not enough: future teachers also need to learn how to learn. Teacher Education Quarterly, 28(4), 23–35. Dent, A. L. (2013). The relation between self-regulation and academic achievement: A metaanalysis exploring variation in the way constructs are labeled, defined, and measured. [Dissertation, Duke University, 2013]. Durham: Duke University. De Corte, E., & Masui, C. (2004). The CLIA-model : A framework for designing powerful learning environments for thinking and problem solving. European Journal of Psychology of Education, 14(4), 365–384. Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level. Metacognition and Learning, 3(3), 231–264. Dignath, C., Büttner, G., & Langfeldt, H. (2008). How can primary school students learn selfregulated learning strategies most effectively? A meta-analysis on self-regulation training programmes. Educational Research Review, 3(2), 101-129. doi:10.1016/j.edurev.2008.02.003 Dignath-van Ewijk, C., Dickhäuser, O., & Büttner, G. (2013). Assessing how teachers enhance self-regulated learning: A multiperspective approach. Journal of Cognitive Education and Psychology, 12(3), 338-358. doi: 10.1891/1945-8959.12.3.338 Dignath-van Ewijk, C., & van der Werf, G. (2012). What teachers think about self-regulated learning: Investigating teacher beliefs and teacher behavior of enhancing students’ self-regulation Education Research International, 2012(Article ID 741713).


66 / 78

Dinsmore, D. L., Alexander, P. A., & Loughlin, S. (2008). Focusing the conceptual lens on metacognition, self-regulation, and self-regulated Learning. Educational Psychology Review, 20(4), 391-409. doi:10.1007/s10648-008-9083-6 Drachsler, H., & Greller, W. (2012). The pulse of learning analytics understandings and expectations from the stakeholders. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 120-129). ACM. Drachsler, H., & Greller, W. (2016). Privacy and analytics: it's a DELICATE issue a checklist for trusted learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge (pp. 89-98). ACM. Dumont, H., Istance, D., & Benavides, F. (eds.) (2010). The Nature of Learning: Using Research to Inspire Practice. Paris: OECD Publishing, Educational Research and Innovation. doi:10.1787/9789264086487-en Duval, E. (2011). Attention please!: learning analytics for visualization and recommendation. In Proceedings of the 1st International Conference on Learning Analytics and Knowledge (pp. 9-17). ACM. Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132. Engelfriet, E., Jeunink, E., Maderveld, J. (2015). Handreiking Learning analytics onder de Wet bescherming persoonsgegevens. SURF report. https://www.surf.nl/kennisbank/2015/learninganalytics-onder-de-wet-bescherming-persoonsgegevens.html Errington, E. (2004) The impact of teacher beliefs on flexible learning innovation: some practices and possibilities for academic developers. Innovations in Education and Teaching International, 41(1), 39–47. Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25–39. Eshel, Y., & Kohavi, R. (2003). Perceived classroom control, self-regulated learning strategies, and academic achievement. Educational Psychology, 23(3), 249–260.


67 / 78

European Commission (2015). Shaping career-long perspectives on teaching. A guide on policies to improve initial teacher education. Luxemburg: Publications office of the European Union. Ferguson, R. (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5-6), 304-317. Finsterwald, M., Wagner, P., Schober, B., Lüftenegger, M., & Spiel, C. (2013). Fostering lifelong learning – evaluation of a teacher education program for professional teachers. Teaching and Teacher Education, 29, 144-155. Fisher, T., Higgins, C., & Loveless, A. (2006). Teachers learning with digital technologies ( No. 14).

Bristol:

Futurelab.

Retrieved

3rd

June

2008

from

http://www.futurelab.org.uk/resources/publications-reports-articles/literature-reviews/LiteratureReview129 Fullan, M. (2001). The new meaning of educational change (3rd ed.). New York: Teachers College Press. Fullan, M. (2007). The new meaning of educational change (4th ed.). New York: Teachers College Press. Fullan, M., & Hargreaves, A. (1992). Teacher development and educational change. London: Falmer Press. Gibson J. J. (1979): The ecological approach to visual perception. Boston, Mass: Houghton Mifflin. Gordon, S. C., Dembo, M. H., & Hocevar, D. (2007). Do teachers’ own learning behaviors influence their classroom goal orientation and control ideology? Teaching and Teacher Education, 23(1), 36–46. Govaerts, S., Verbert, K., Klerkx, J., & Duval, E. (2010). Visualizing activities for self-reflection and awareness. In International Conference on Web-Based Learning (pp. 91-100). Springer Berlin Heidelberg. Grant, M. M. (2003). Finding your place in a student-centered classroom as a teacher facilitator? The Agricultural Education Magazine, 76(2), 18–20.


68 / 78

Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational technology & society, 15(3), 42-57. Grundy, S., & Robinson, J. (2004). Teacher professional development: themes and trends in the recent Australian experience. In C Day & J. Sachs (Eds.), International handbook on continuing professional development of teachers (pp. 146–66). Maidenhead: Open University Press. Harfitt, G. J. (2012). An examination of teachers’ perceptions and practice when teaching large and reduced-size classes: Do teachers really teach them in the same way? Teaching and Teacher Education, 28(1), 132–140. Heikkiläa, A., & Lonka, K. (2006). Studying in higher education: Students' approaches to learning, self-regulation, and cognitive strategies. Studies in Higher Education, 31, 99-117. doi:10.1080/03075070500392433 Hernández-García, Á., González-González, I., Jiménez-Zarco, A. I., & Chaparro-Peláez, J. (2016). Visualizations of Online Course Interactions for Social Network Learning Analytics. International Journal of Emerging Technologies in Learning (iJET), 11(07), 6-15. Hoban, G. F. (2002). Teacher learning for educational change: A systems thinking approach. Open University Press. Hoel, T. & Chen, W. (2015). Privacy in Learning Analytics – Implications for System Architecture. In: Watanabe, T. and Seta, K. (Eds.) (2015). Proceedings of the 11th International Conference on Knowledge Management. Jacobson M., & Archodidou A. (2000): The design of hypermedia tools for learning: Fostering conceptual change and transfer of complex scientific knowledge. Journal of Learning Science, 9, 145199. Järvelä, S., Järvenoja, H., Malmberg, J., & Hadwin, A. F. (2013). Exploring socially-shared regulation in the context of collaboration. Journal of Cognitive Education and Psychology, 12(3), 267286. doi:10.1891/1945-8959.12.3.267 Järvelä, S., & Hadwin, A. F. (2013). New frontiers: Regulating learning in CSCL. Educational Psychologist, 48(1), 25-39. doi:10.1080/00461520.2012.748006


69 / 78

Johnson, K. E. (1994). The emerging beliefs and instructional practices of pre-service English as a second language teachers. Teaching and Teacher Education, 10(4), 439–452. Johnson, L., Adams, S. and Cummins, M. (2012). The NMC Horizon Report: 2012 Higher Education

Edition,

The

New

Media

Consortium,

Austin,

Texas.

Available

at:

http://www.nmc.org/pdf/2012-horizon-report-HE.pdf Kearney, M., Aubusson, P., and Schuck, S. & Burden, K. (2012) Viewing Mobile Learning from a Pedagogical Perspective. ALT-J Research in Learning Technology. Vol 20 Kearney, M., & Maher, D. (2013). Mobile learning in math teacher education: Using iPads to support pre-service teachers’ professional development. Australian Educational Computing, 27(3), 76-84. Keim, D. A. (2002). Information visualization and visual data mining. IEEE transactions on Visualization and Computer Graphics, 8(1), 1-8. Kennewell, S., Parkinson, J., & Tanner, H. (2000). Developing the ICT capable school. London: RouteledgeFalmer. Kevan, J. M., & Ryan, P. R. (2016). Experience API: Flexible, decentralized and activity-centric data collection. Technology, Knowledge and Learning, 21(1), 143-149. Kistner, S., Rakoczy, K., Otto, B., Dignath-van Ewijk, C., Büttner, G., & Klieme, E. (2010). Promotion of self-regulated learning in classrooms: Investigating frequency, quality, and consequences for student performance. Metacognition and Learning, 5(2), 157–171. Kitto, K., Cross, S., Waters, Z., & Lupton, M. (2015). Learning analytics beyond the LMS: the connected learning analytics toolkit. In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge (pp. 11-15). ACM. Kouzma R. (2005): National policies that connect ICT-based education reform to economic and social development. An Interdisciplinary Journal on Humans in ICT Environments, 1(2), 117-156. Kramarski, B., & Michalsky, T. (2009). Investigating preservice teachers' professional growth in self-regulated learning environments. Journal of Educational Psychology, 101(1), 161-175.


70 / 78

Kump, B., Seifert, C., Beham, G., Lindstaedt, S. N., & Ley, T. (2012). Seeing what the system thinks you know: visualizing evidence in an open learner model. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 153-157). ACM. Land, S. (2000): Cognitive requirements for learning with open-ended learning environment. ETR&D, 48(3), 61-78. Lapan, R. T., Kardash, C. M., & Turner, S. (2002). Empowering students to become selfregulated learners. Professional School Counseling, 5(4), 257 – 265. Leony, D., Pardo, A., de la Fuente Valentín, L., de Castro, D. S., & Kloos, C. D. (2012). GLASS: a learning analytics visualization tool. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 162-163). ACM. Lewis, R. (1999). Teachers coping with the stress of classroom discipline. Social Psychology of Education, 3, 155–171. Ley, K., & Young, D. B. (2001). Instructional principles for self-regulation. Educational Technology Research and Development, 49(2), 93–103. Lombaerts, K., Engels, N., & Vanderfaeillie, J. (2007). Exploring teachers’ actions to promote self-regulated learning practices in primary school. The Australian Educational and Developmental Psychologist, 24(1), 4–24. Michalsky, T. (2012). Shaping self-regulation in science teachers' professional growth: Inquiry skills. Science Education, 96(6), 1106-1133. doi: 10.1002/sce.21029 Michalsky, T., & Schechter, C. (2013). Preservice teachers' capacity to teach self-regulated learning: Integrating learning from problems and learning from successes. Teaching and Teacher Education, 30, 60-73. doi: 10.1016/j.tate.2012.10.009. Mishra P., & Koehler M. J. (2009): Technology pedagogical content knowledge: a framework for teacher knowledge. Contemporary Issues in Technology and Teacher Education, 91(6), 60-70. doi:10.1111/j.1467-9620.2006.00600684.x. Moolenaar, N. M., Daly, a. J., & Sleegers, P. J. C. (2010). Occupying the principal position: Examining relationships between transformational Leadership, social network position, and


71 / 78

sSchools’

innovative

climate.

Educational

Administration

Quarterly,

46(5),

623–670.

doi:10.1177/0013161X10378689 Moos, D.C. (2009). Note-taking while learning hypermedia: Cognitive and motivational considerations. Computers in Human Behavior, 25(5), 1120-1128 Mykkänen, A., Perry, N., & Järvelä, S. (2015). Finnish students’ reasons for their achievement in classroom activities: Focus on features that support self-regulated learning. Education 3-13: International Journal of Primary, Elementary and Early Years Education, 1–16. Nielsen, D. C., Barry, A. L., & Staab, P. T. (2008). Teachers’ reflections of professional change during a literacy-reform initiative. Teaching and Teacher Education, 24(5), 1288–1303. Ochoa, X., & Duval, E. (2006). Use of contextualized attention metadata for ranking and recommending learning objects. In Proceedings of the 1st international workshop on Contextualized attention metadata: collecting, managing and exploiting of rich usage information (pp. 9-16). ACM. OECD. (2009). Creating effective teaching and learning environments: First results from TALIS. Paris: OECD Publishing. Open University UK (2014). Policy on Ethical use of Student Data for Learning Analytics. Available: http://www.open.ac.uk/students/charter/sites/www.open.ac.uk.students.charter/files/files/ecms/w eb-content/ethical-use-of-student-data-policy.pdf Panadero, E., & Alonso-Tapia, J. (2014). How do students self-regulate? Review of Zimmerman’s cyclical model of self-regulated learning. Anales De Psicologia, 30(2), 450-462. doi: 10.6018/analesps.30.2.167221 Panadero, E., & Järvelä, S. (2015). Socially shared regulation of learning: A review. European Psychologist, 20(3), 190-203. doi: 10.1027/1016-9040/a000226 Panadero, E., Klug, J., & Järvelä, S. (2015). Third wave of measurement in the self-regulated learning field: When measurement and intervention come hand in hand. Scandinavian Journal of Educational Research. doi:10.1080/00313831.2015.1066436 Pardo, A. & Siemens, G. (2014). Ethical and privacy principles for Learning Analytics. British Journal of Educational Technology, 45(3), 438–450. DOI: 10.1111/bjet.12152.


72 / 78

Perels, F., Merget-Kullmann, M., Wende, M., Schmitz, B., & Buchbinder, C. (2009). Improving self-regulated learning of preschool children: evaluation of training for kindergarten teachers. The British Journal of Educational Psychology, 79, 311–27. doi:10.1348/000709908X322875 Paris, S. G., & Newman, R. S. (1990). Developmental aspects of self-regulated learning. Educational Psychologist, 25(1), 87–102. Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36(2), 89–101. Paris, S. G., & Winograd, P. (2003). The role of self-regulated learning in contextual teaching: principles for teacher preparation. Washington, DC. Peeters, J. (2015). Teacher and school characteristics associated with self-regulated learning practices in primary education: a multidimensional approach (Unpublished doctoral dissertation). Vrije Universiteit Brussel, Brussels, Belgium. Peeters, J., De Backer, F., Kindekens, A., Jacquet, W., & Lombaerts, K. (2013). The impact of teacher and school determinants on self- regulated learning practices. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Peeters, J., De Backer, F., Kindekens, A., Jacquet, W., & Lombaerts, K. (2015). Mechanisms underlying primary school teachers’ promotion of self-regulated learning. Paper presented at the annual meeting of the American Educational Research Association, Chicago. Peeters, J., De Backer, F., Kindekens, A., Romero Reina, V., Buffel, T., & Lombaerts, K. (2013). Primary teachers’ personal beliefs about teacher and school influences affecting the promotion of self- regulated learning. Paper presented at the biannual meeting of the European Association of Learning and Instruction, Munich. Peeters, J., De Backer, F., Kindekens, A., Romero Reina, V., & Lombaerts, K. (2013). Primary teachers’ beliefs about the promotion of self-regulated learning with at risk students. Paper presented at the biannual meeting of the European Association for Learning and Instruction, Munich.


73 / 78

Peeters, J., De Backer, F., Romero Reina, V., Kindekens, A., Buffel, T., & Lombaerts, K. (2014). The role of teachers’ self-regulatory capacities in the implementation of self-regulated learning practices. Procedia – Social and Behavioral Sciences, 116, 1963-1970. Pegrum M. (2014): Mobile learning: Languages, literacies, and Cultures. Palgrave Macmillan: London. Perry, N. E., Hutchinson, L., & Thauberger, C. (2008). Talking about teaching self-regulated learning: Scaffolding student teachers’ development and use of practices that promote selfregulated learning. International Journal of Educational Research, 47(2), 97–108. Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 452-502). San Diego, CA: Academic Press. Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801-813. doi:10.1177/0013164493053003024 Prinsloo, P. & Slade, S. (2013). An evaluation of policy frameworks for addressing ethical considerations in Learning Analytics. In Proceedings of the 3rd Learning Analytics and Knowledge conference, Leuven, Belgium. DOI: 10.1145/2460296.2460344 Prinsloo, P. & Slade, S. (2015). Student privacy self-management: implications for Learning Analytics. In Proceedings of the 5th international conference on learning analytics and knowledge Pooghkeepsie, New York, USA. DOI: 10.1145/2723576.2723585 Putnam, R. T., & Borko, H. (2000). What do new views of knowledge and thinking have to say about

research

on

teacher

learning?

Educational

Researcher,

29(1),

4–15.

doi:10.3102/0013189X029001004 Retalis, S., Papasalouros, A., Psaromiligkos, Y., Siscos, S., & Kargidis, T. (2006). Towards networked learning analytics – A concept and a tool. In Proceedings of the fifth international conference on networked learning. Reynolds, D. (2007). School effectiveness and school improvement: Links with the international standards/accountability agenda. In T. Townsend (Ed.), International Handbook of


74 / 78

School Effectiveness and Improvement: Review, Reflection and Reframing (pp. 471–484). Dordrecht: Springer. Rimm-Kaufman, S. E., Storm, M. D., Sawyer, B. E., Pianta, R. C., & LaParo, K. M. (2006). The Teacher Belief Q-Sort: A measure of teachers’ priorities in relation to disciplinary practices, teaching practices, and beliefs about children. Journal of School Psychology, 44(2), 141–165. doi:10.1016/j.jsp.2006.01.003 Schmidt D. A., Baran E., Thompson A. D., Mishra P., Koehler M. J., & Shin T. S. (2009): Technological pedagogical content knowledge (TPCK): The development and validation of an assessment instrument for preservice teachers. Journal of Research on Technology in Education, 42(2), 27. Schmitz, H. C., Wolpers, M., Kirschenmann, U., & Niemann, K. (2011). Contextualized attention metadata. Human attention in digital environments, 186-209. Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science, 26, 113–125. Schunk, D. H. (2012). Learning theories: An educational perspective (Sixth Edit.). Boston: Pearson Education, Inc. Sclater, N., & Bailey, P. (2015). Code of practice for learning analytics. Available at: https://www.jisc.ac.uk/guides/code-of-practice-for-learning-analytics Sheiter K., & Gerjets P. (2007): Learner control in hypermedia environment. Educational Psychology Review, 19, 285-307. doi:10.1007/s10648-007-9046-3. Shulman L. S., & Shulman J. H. (2004): How and what teachers learn: a shifting perspective. Journal of Curriculum Studies, 26(2), 257-271. doi: 10.1080/0022027032000148298. Siemens, G., & Long, P. (2011). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE review, 46(5), 30. Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S. B., Ferguson, R., ... & Baker, R. S. J. D. (2011). Open Learning Analytics: an integrated & modularized platform (Doctoral dissertation, Open University Press). Available at http://www.elearnspace.org/blog/wpcontent/uploads/2016/02/ProposalLearningAnalyticsModel_SoLAR.pdf


75 / 78

Siemens, G., & d Baker, R. S. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM. Sitzmann, T., & Ely, K. (2011). A meta-analysis of self-regulated learning in work-related training and educational attainment: What we know and where we need to go. Psychological Bulletin, 137(3), 421-442. doi:10.1037/a0022777. Somekh B., & Devis N. (Eds.). (1997): Using information technology effectively in teaching and learning: studies in pre-service and in-service teacher education. London: Routledge. Sosu, E. M., & Gray, D. S. (2012). Investigating change in epistemic beliefs: An evaluation of the impact of student teachers’ beliefs on instructional preference and teaching competence. International Journal of Educational Research, 53, 80–92. Spruce, R., & Bol, L. (2015). Teacher beliefs, knowledge, and practice of self-regulated learning. Metacognition and Learning, 10(2), 245–277. doi:10.1007/s11409-014-9124-0 Steiner, C., Kickmeier-Rust, M.D., and Albert, D. (2015). LEA in Private: A Privacy and Data Protection Framework for a Learning Analytics Toolbox, Journal of Learning Analytics, Special Issue on Ethics and Privacy. Traxler, J. (2007) ‘Current state of mobile learning’, International Review on Research in Open and Distance learning, 8, (2), 1-10. UNESCO (2013): Policy Guidelines for Mobile Learning. Paris: UNESCO. van Harmelen, M., & Workman, D. (2012). Analytics for learning and teaching. CETIS Analytics Series, 1(3), 1-40. Van Veen, K., Sleegers, P., & van de Ven, P.-H. (2005). One teacher’s identity, emotions, and commitment to change: A case study into the cognitive–affective processes of a secondary school teacher in the context of reforms. Teaching and Teacher Education, 21(8), 917–934. doi:10.1016/j.tate.2005.06.004 Van Veen, K., & Sleegers, P. (2009). Teachers’ emotions in a context of reforms: To a deeper understanding of teachers and reforms. In P. Schutz & M. Zembylas (Eds.), Advances in Teacher


76 / 78

Emotion Research: The Impact on Teachers’ Lives (pp. 233–252). Dordrecht: Springer Science+Business Media LLC. Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: an overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499-1514. Verpoorten, D. (2012). Reflection amplifiers in self-regulated learning (doctoral thesis). Centre for Learning Sciences and Technologies, Open University of the Netherlands. Viberg, O., & Grönlund, Å. (2013). Cross-cultural analysis of users' attitudes toward the use of mobile devices in second and foreign language learning in higher education: A case from Sweden and China. Computers & Education, 69, 169-180. Vieluf, S., Kaplan, D., Klieme, E., & Bayer, S. (2012). Teaching practices and pedagogical innovation: Evidence from TALIS. Paris: OECD Publishing. Vrieling, E. M., Bastiaens, T. J., & Stijnen, S. (2010). Process-oriented design principles for promoting self-regulated learning in primary teacher education. International Journal of Educational Research, 49, 141-150. Vygotsky, L. S. (1978) Mind in society. MIT Press, Cambridge, MA Weinstein, C. E., Schulte, A. C., & Palmer, D. R. (1987). Learning And Study Strategies Inventory. Clearwater, FL: H & H Publishing. Wertsch, J. V. (1991) Voices of the mind: a socio-cultural approach to mediated action. Harvard University Press, Cambridge, MA. Wilson, N. S., & Bai, H. (2010). The relationships and impact of teachers’ metacognitive knowledge and pedagogical understandings of metacognition. Metacognition and Learning, 5(3), 269–288. doi:10.1007/s11409-010-9062-4 Wilson, S., & Berne, J. (1999). Teacher learning and the acquisition of professional knowledge: an examination of research on contemporary professional development. Review of Research in Education, 24, 173–209.


77 / 78

Winne, P. H., Hadwin, A. F., & Perry, N. E. (2013). Metacognition and computer-supported collaborative learning. In C. E. Hmelo-Silver, C. A. Chinn, C. Chan, & A. M. O'Donnell (Eds.), The international handbook of collaborative learning (pp. 462-479): Routledge. Winne, P. H., & Perry, N. E. (2000). Measuring self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 531-566). Orlando, FL: Academic Press. Wolf, G. (2009, July 17). Know thyself: Tracking every facet of life, from sleep to mood to pain, 24/7/365. Wired Magazine. Retrieved from http://www.wired.com/2009/06/lbnpknowthyself/ Wolters, C. A., Pintrich, P. R., & Karabenick, S. A. (2003). Assessing academic self-regulated learning. Paper prepared for the Conference on Indicators of Positive Development: Definitions, Measures, and Prospective Validity. Yadav A., & Koehler M. (2007): The role of epistemological beliefs in pre-service teachers’ interpretation of video cases of early-grade literacy instruction. Journal of Technology and Teacher Education, 15(3), 335. Yeo, L. S., Ang, R. P., Chong, W. H., Huan, V. S., & Quek, C. L. (2008). Teacher Efficacy In the Context of Teaching Low Achieving Students. Current Psychology, 27(3), 192–204. doi:10.1007/s12144-008-9034-x Zhao, Y., Pugh, K., Sheldon, S., & Byers, J. L. (2002). Conditions for classroom technology innovations. Teachers College Record, 104(3), 482–515. Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. doi:10.1037/0022-0663.81.3.329 Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13-40). San Diego, California: Academic Press. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice,

41(2),

64-70.

Retrieved

from

http://www.informaworld.com/10.1207/s15430421tip4102_2


78 / 78

Zimmerman, B. J. (2013). From cognitive modeling to self-regulation: A social cognitive career path. Educational Psychologist, 48(3), 135-147. doi:10.1080/00461520.2013.794676 Zimmerman, B. J., & Schunk, D. H. (2011). Handbook of self-regulation of learning and performance. New York: Routledge.


Theoretical framework Deliverable D3.1.

Theoretical framework Deliverable D3.1.

Suggest Documents

Theoretical Framework

Deliverable D 4.2 FRAMEWORK FOR MONITORING

Theoretical Framework

Theoretical-conceptual framework

Chapter 2: Theoretical Framework

Basic theoretical framework

A Theoretical Framework

Technology and Theoretical Framework

Conceptual/Theoretical Framework

theoretical framework - CiteSeerX

A theoretical framework

Chapter 1: Theoretical Framework

Chapter 1: Theoretical Framework

A Theoretical Framework

An e-Learning Theoretical Framework

Theoretical and Methodological Framework - WOSCAP

A THEORETICAL FRAMEWORK MAPPING ...

Basic theoretical framework - OpenArchive@CBS

a theoretical framework for advertising

THEORETICAL FRAMEWORK AND EMPIRICAL MODELS

A Theoretical Framework - Science Direct

THEORETICAL FRAMEWORK AND EMPIRICAL MODELS

A THEORETICAL FRAMEWORK FOR THE

Theoretical and Methodological Framework - WOSCAP