J. Kim & R. Kumar (eds). Proceedings of Workshop on Intelligent Support for Learning in Groups, ITS’12.
Helping Teachers Effectively Support Group Learning Susan Bull1, Barbara Wasson2,3, Matthew D Johnson1, Dean Petters1, Cecilie Hansen2 1
Electronic, Electrical and Computer Engineering, University of Birmingham, UK 2 UniHelse, UniResearch, Norway 3 Information Science & Media Studies, University of Bergen, Norway
[email protected],
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Abstract. This paper focuses on supporting teachers in their management of group interaction using an open learner model to support on-the-spot classroom decision-making, according to the specific needs of individuals and groups. Keywords: support for teachers, group interactions, open learner model.
1 Introduction Open learner models (OLM) are learner models that are 'open' to the user, displayed in a form they can understand [1]. Most OLMs are open to the student, but there have also been OLMs to support teachers (e.g. [2,3]). In addition, there are various approaches aimed at identifying and supporting collaboration in group work, for example: automatic detection of collaborative group interaction to show to teachers and learners [4]; and allowing students to view their own learner models alongside those of peers and the whole group, to facilitate peer communication and learning [5,6]. With the growing use of technologies inside and outside classrooms, OLMs are being developed that can draw on a variety of data sources (e.g. [7,8]). Technologies can substantially increase the amount, detail and speed with which user data can be collected and summarised, providing greater opportunities for timely teacher interventions and data-rich evidence-based decision-making [9]. We describe how OLMs can play an important role in helping teachers in their support of group learning according to the requirements of group members. Thus, the intelligent support comes mainly from the teacher, assisted by rich data from individual and group learner models.
2 Formative Assessment, Cognitive Density and OLM The Next-TELL1 OLM provides formative assessment support to students and teachers. Wiliam’s [10] theory of formative assessment and instructional processes relates several agents of the instructional process (teachers, peers, students) to the process dimension of: students’ current state of learning; the aims – where their learning is 1
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heading; and how to achieve this. Crawford et al. [11] define cognitive density as the aggregate level of students’ engagement, and is associated with teacher decisionmaking and diagnostic power. It relates to time on task, metacognition, accountability and production activity, and comprises: communication density (increasing bandwidth of communication, enabling simultaneous channels); content density (capturing work process and product, resource access and variety, improved feedback to students, feedback to teacher); temporal density (automate routine procedures, reduce downtime, timely feedback, differentiate task start/stop times). Effective practice will optimise (i.e. not maximise) cognitive density according to pedagogical objectives [11]. Classroom OLMs are expected to provide improvements to cognitive density as well facilitate formative assessment, because learner competences are easily and effectively visualised. Feedback is an important aspect of content density and formative assessment, and OLMs are a natural way of providing feedback and offer a focus for communication. They naturally impact temporal density as they can provide pointers as activities progress. These communicative, content and temporal dimensions to OLMs make them a strong tool in formative assessment within the cognitive density framework. We here focus on OLMs for teachers to facilitate group learning, by helping them make evidence-based decisions based on rich data from a variety of sources.
3 The Next-TELL Open Learner Model This section introduces the model content, data sources, visualisations and model use. Content: In principle, the OLM could use a variety of data types. The current version is based on competence frameworks. We illustrate with the Norwegian national competence goals and curriculum plan that is integrated into teaching and learning of English in schools2. This covers three main areas: language learning; communication; culture, society and literature. These are related to target competences for specific times during school education: following 2, 4, 7 and 10 years of learning English. For instance, after 2 years, students ought to be able to provide examples in the area of “language learning” for: situations in which it may be useful to know some English; some common words and phrases used by native English speakers; and words and phrases that are relevant to their own interests. By the time they have studied English for 10 years, they should be able to: use a range of situations and strategies in language learning; identify similarities and differences between Norwegian and English; use suitable basic grammatical and structural terms; critically and independently use a range of aids; and describe and self-assess their own English. Teachers can assign tasks and activities that reveal competences by selecting from a list (see Fig. 1), e.g. a student can give examples of English words and phrases based on their interests2. Sources of Data: There are various data sources for the model. Data may be explicitly entered by teachers (e.g. if students have verbally described their interests), or parents (based on out-of-school activities); Moodle quizzes, other learner models (e.g. [5]); or, currently being developed, self- and peer-assessment, data from Google docs, spreadsheets, Social Networks, Second Life, e-portfolio. This enables teachers to 2
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view information most relevant for their immediate classroom decision-making, from the current activity, or taking into account previously demonstrated competences.
Fig.1. Assigning competences to activities/tasks: expanded competences for language learning
Fig.2. Open learner model views for teachers
Visualisations: Fig. 2 shows existing learner model and activity visualisations, and views under development. (a) grouping using knowledge level; (b) communication (thickness of link on network diagram); (c) whether learners are working appropriate to their ability; (d) difficulties or misconceptions ranked by frequency; (e) evidence of 21st Century skills; (f) group progression over time. Interfaces such as the above aim to help teachers quickly gain an overview of the state of progress to enable them to support learners accordingly, with an approach that also incorporates new technologies such as social networking and online virtual environments. Combining this type of information in a ‘classroom view’ (see e.g. [12]), will be a focus of future work. OLM Use: The OLM helps bring greater flexibility to the classroom, enabling teachers to adapt their teaching, based on current classroom evidence and learner model data from previous activities, presented in a choice of formats. For example, the teacher may introduce some students to a task in the Second Life virtual environment, which will itself return data to the OLMs of those participating (e.g. with like buttons or movement of avatars); other students may be directed to peer feedback using each other’s learner models, again generating further learner model data; and yet others may become involved in alternative activities, perhaps including systems that themselves provide intelligent group support, allowing the teacher to concentrate on monitoring OLMs of the other groups. The teacher may quickly organise groups, matching or complementing knowledge; supporting collaboration or peer help. Thus, the OLM can help optimise cognitive density in a formative assessment context. Studies in schools will help identify how teachers use this information in practice, and what further support is required and/or requested.
4 Summary This paper has highlighted the potential to support teachers in classroom decisionmaking when students are using a range of applications that generate rich data. The Next-TELL OLM can take both manual and automatic data from a variety of people and applications, to visualise learning information to teachers. This can enable teachers to make evidence-based decisions on how to facilitate group interactions.
Acknowledgement This project is supported by the EC under the IST priority of the 7th Framework Programme for R&D under contract number 258114 NEXT-TELL. This document does not represent the opinion of the EC, and the EC is not responsible for any use that might be made of its content.
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