A Dynamic Personalization in e-Learning Process Based on Triple-Factor Architecture Sfenrianto
Heru Suhartanto
Zainal A. Hasibuan
Faculty of Computer Science University of Indonesia Jakarta, Indonesia
[email protected]
Faculty of Computer Science University of Indonesia Jakarta, Indonesia
[email protected]
Faculty of Computer Science University of Indonesia Jakarta, Indonesia
[email protected].
al. in [2], By providing personalization based on the indentification of student’ learning style, less study time to achieve on average same grade (e.g., Graf and Kinshuk, 2007) and higher student satisfaction (Popescu, 2008) have been demonstrated. Then, knowing the information students’ learning style can be used to provide student with learning material/activities and personalized recomendations than fit with their learning style [3]. In relation to research on motivation, log activity has also been considered as a source of information for assessing students’ motivation [4]. Hence, many researchers agreed that adopting personalization based on learning styles and motivation in elearning will increases knowledge ability and makes learning easier for students. However, previous studies conducted using different factor. We also do not see a comprehensive approach identification that reflects the relationship between learning style, motivation, and knowledge ability for personalization learning materials. In this study, we propose a dynamic personalization in elearning process based on triple-factor arcitecture. The paper is structured in the next sections as follows: triple-factor in elearning process and preliminary study are described; subsequently, triple-factor architecture; last section concludes our study.
Abstract—Providing personalization in e-learning process by considering the existence learning style, motivation and knowledge ability (triple-factor) can affect students performance and makes learning easier for students. The result of our preliminary study indicates that there is an impact triple-factor on learning activities in Student Centered E-Learning Environment (SCELE). It shows that the triple-factor: learning style, motivation and knowledge ability identification needs personalization approach in e-learning. The identification are predicted from log, forum, and score test. In this paper, we propose a dynamic personalization in e-learning process based on triple-factor architecture. The architecture consists of five main layers: learning strategy layer, learning layer, activity layer, identification layer and personalization layer. Each layer will dynamically guide the student to achieve the goal of learning. Keywords-e-Learning; personalization; motivation; knowledge ability.
I.
learning
style;
INTRODUCTION
E-learning system has been developed by many education institutions to support the learning process. Most of e-learning systems are still applied as a media to enrich traditional learning system and do not really address the influences of inherent factors such as learning style, motivation, knowledge ability, etc. Very often the students do not receive learning materials that suit those factors. Thus, the learning effectiveness becomes less optimal. Meanwhile, to improve the effectiveness of their learning process in e-leaning system can be optimized use a concept to identify student’ learning style, motivation, knowledge ability (triple-factor) as the foundation of personalized learning materials. The personalization in e-learning process is as a strategy which very useful to adjusted students needs, so make they learn more effectively. Considering triple-factor in the e-learning system is studied by some previous researches who argue the importance of those factors in learning process. For instance: According to Khan et. al. in [1], affective states and learning styles tactics to provide personalize in e-learning system have a significant effect on student learning. According to Graf et.
II.
TRIPLE-FACTOR IN E-LEARNING PROCESS
In order to support personalization, the result of our previous study in [5], indicates that the existence of inherent structure that reflect relationship among learning style, motivation and knowledge ability. The study proposed a model for an e-learning system base on learning style, motivation, and knowledge ability called triple-characteristic model (TCM). The TCM model accommodates students' learning style, motivation and knowledge ability in theirs personalized learning activities Then in the previous study [6] as shown in Figure 1, we also proposed the influence factors of inherent structure in elearning process. Our approach integrates information about learning styles, motivation and knowledge ability factor, in
Sponsors by : Laboratory of Digital Library and Distance Learning (DL2) Faculty of Computer Science, University of Indonesia, and Global Development Learning Network (GDLN) Indonesia. - 69 -
motivation and learning style, whilst grade obtained indicate knowledge ability. In order to obtain the data in this study, one hundred and eighty-six (186) students were involved in the 15-weekscourse. As shown in Table I, from the total participants, we gathered total data using SCELE system for 15-weeks were 33308 activities.
order to enable e-learning system to identify and personalise the learning materials based on those factors.
TABLE I.
THE DATA ACTIVITIES OF SCELE FOR EACH WEEK
Figure 1. The Influence Factors of Inherent Structure in e-Learning Process
Thus, in the e-learning process, triple-factor must be identified for the purpose of personalized learning materials, recommendation to students and make students learn more effectively III.
PRELIMINARY STUDY
The identification and personalization in e-learning environment involves very complicated processes. Hence, to support the proposed architecture for identification and personalization based on triple-factor in e-learning process, we have held a preliminary study. This study is to analyze triplefactor that reflect the relationships among learning styles, motivation, and knowledge ability which impact on learning activities in e-learning process. Then we also analyze to see whether there is a relation between the activities of the students and their grade in each of the student categories and how strong is this relation. The expected results of this study can give us an argument, that the importance of triple-factor considered in e-learning process. Then, the triple-factor will be accommodated in the proposed architecture. This study is reviewed in the next section as fellow: the first section details about data are shown. Subsequently, present the scenario in which the experiments have been performed. Last section, summarizing the results obtained.
As shown in Table II, we also gathered the data of the learning activities of students that used SCELE system based on test score. TABLE II. THE DATA ACTIVITIES OF SCELE BASED ON TEST SCORE
A. Data Data for this study were extracted from a undergraduate course on Research Methodology & Scientific Writing. The course was taught to inter-university, and offers online learning that can be taken by any student of the participating universities. The name of the program is Global Development Learning Network (GDLN) Indonesia 2011. The participating universities are: University of Indonesia, Islamic University of Indonesia Yogyakarta, STMIK MDP Palembang, and STMIK Kharisma Makassar. The lecture delivered the course using dual-mode (combination of face-to-face and online) at Student Centered E-Learning Environment (SCELE), which was developed by e-learning team at Fasilkom UI [7]. SCELE main features supports course management, forum for discussion, assignment, quiz online, and also learning log. In addition, participation in the forum discussion and learning log indicates
. B. Experiment Scenario A quasi-experimental research method was used in this study. The experiment have been conducted to provide the relevant material of FSLSM learning style (see table IV in section IV). Those materials indicates the students’ learning
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indicated from the category of their knowledge ability: (85100) Excellent (15995 activities or 48,02%); (75-84) Good (9809 activities or 29,45%); (65-74) Average (9809 activities or 15,89%); and (0-64) poor (2212 activities or 6,64%). Thus, from data investigation and analysis results, shows that there is a tendency that the higher the frequencies of activitees in SCELE, the higher the test score the students will get. Thus, we also interpreting that identification of knowledge ability for the purpose of personalization recomendation can be indicated from test score In addition to strengthen our argument, we also analyzed how strong is this relation between studens activeties (X1) and test score (X2) in SCELE, we used corelation analysis.
styles on each dimension, namely: active/reflective, visual/verbal, sequential/global, and sensing/intuitive. We also used, six strategies (forum, assignment, quiz, feedback, bonus point, and multimedia) as trigger factors to motivate students, as a way to encourage students to engage actively in e-learning process. The number of students activities in SCELE indicates higher or lower their motivation state. We divide the object or subject of study into two groups: (1) a treatment group with respect to giving more the relevant materials, and trigger factors; and (2) a control group with giving less. Table III shows a treatment group for 10-weeks learning sessions (3, 4.5, 6.7, 9, 10, 11, 12, and 13). Whereas a control group only 5 weeks (1 ,2, 8, 14 and 15).
r X 1. X 2 =
TABLE III. A TREATMENT GROUP AND CONTROL GROUP IN SCELE
n(Σx1 x 2 ) − (Σx1 )(Σx 2 ) 2
2
{nΣx1 − (Σx1 ) 2 }{nΣx 2 − (Σx 2 ) 2 }
(1)
The respondents were 186 students (n). The total activities of students using SCELE system ( X1) were 33308 activities. The total of test score obtained by students ( X2) were 15092 actievities. From the calculation, we found that the activities of students using SCELE system (X1) has significant influence to test score (X2). It has been proven that the coefficient correlation between X1 and X2 is 0.32. Thus, SCELE has provided facilities: learning log, forum discussion and students assessment to support online courses activities. These facilities indicate the existence of inherent structure that reflect relationship among learning style, motivation and knowledge ability (triple-factor). But on the other hand, SCELE still treats all students equally the same in providing materials for the courses. This is due to, SCELE does not have facilities to identification and personalization based on triple-factor. Based on that preliminary study, in the next section we propose an architecture for identification students' learning style, motivation, and knowledge ability (triple-factor) in elearning process. The architecture aims to support personalise and recomnendations the learning materials based on triplefactor. C. Result There is the impact the relevant materials of FSLSM and trigger factors on learning activities in SCELE system. As show in table I and III, which indicated: from a treatment group, we gathered data of total activities (27370 activities or 82,18%), learning log (18697 activities or 56,14%), and forum (8673 activieties or 26,02%). Comparison with a control group total activities (5938 activities or 17,83%), learning log (3238 activities or 9,72%) and forum (2700 activities or 8,10%). Thus from data investigation and analysis results, we interpreting that identification of learning style and motivation for the purpose personalization learning can be indicated from activites of learning log and forum (activities of SCELE). In table I, we can see that there is relationsip between the number of learning activities and test score in SCELE system. It can be see from tests score obtained by students, which
IV.
TRIPLE-FACTOR ARCHITECTURE
In the previous study [5], we proposed triple characteristic model (TCM) to support e-Learning system. The TCM model accommodates students' learning style, motivation and knowledge ability in their personalized learning activities. It consists of three layers, i.e. learning layer, characteristic layer, and personalization layer. The relationship between the three layers are learning layer which provides learning behavior patterns to support identification of students’ characteristics on characteristic layer. Then, it provides the basis for personalization functionality on personalization layer. In this study we propose a dynamic personalization in elearning process based triple-factor arcitecture which inspired from TCM. As show figure 2, the architecture consists of five layers, i.e. learning strategy layer, learning layer, activity layer, identification layer and personalization layer.
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being presented but, they like to get an overview of the materials. Sensing students like to learn from concrete material (like examples). Whereas intuitive students, prefer to learn abstract material and like challenges. TABLE IV. LEARNING MATERIAL BASED ON FSLSM LEARNING STYLE FOR E-LEARNING
Figure 2. Triple-Factor Architecture
On the other hand, several approaches for motivation strategies in e-learning process have been used and suggested. Using forum participation and assessments as motivational tools in e-learning courses[11]. Then according to Keller & Suzuki in [12], motivational strategies used to increase student satisfaction, such as rewards, personal attention, feedback, etc. Whereas McCleskey [13], recommending a strategy to enhance students motivation in e-learning can be use visual interest (photographs, video, graphics, and animations). Thus the approach motivational strategies in elearning possibly might vary depending on the needs of the learning environment of institutions. In this study, we consider six strategies as trigger factors to motivate studens in e-learning process, namely: discussion forum, giving assignments, online quizzes, feedbacks, bonus points, and multimedia learning materials (see Figure 3). Those strategies are expected to trigger students to be actively involved in e-learning process. Thus, a student with high or low motivation state can be indicated from the number of learning activities in e-learning process. The forum is media discussion in e-learning to support interactive learning and discusses more about topic which will give rich learning experiences. According to Corich, et. al. in [14], the forum is viewed as one of a range of tools that enable online course participants to collaborate, share ideas and discuss domain related concepts. Related to trigger students’ motivation in e-learning process, professors or teachers should be able to motivate the students so that they see their
A. Learning Strategy Layer Learning strategy layer includes two component: the relevant materials of learning style, and trigger factors to motivate students. The relevant materials used to provide the student based on their learning style. Whereas, consider discussion forum, giving assignments, online quizzes, feedbacks, bonus points, and multimedia learning materials can be used as a trigger factors to motivated students Related to learning styles, educational psychologists have developed several models of learning styles, such as The Myers-Briggs Type Indicator (MBTI), Herrmann Brain Dominance Instrument (HBDI), model by Kolb, FelderSilverman Learning Style Model (FSLSM), etc. Particularly the FSLSM is used often in research related to learning styles in e-learning, as one of the adaptability than tailors to learning differences and individual needs [8] [9]. The FSLSM can be categorized into four dimensions for each student (active/reflective, visual/verbal, sequential/global, and sensing/intuitive) [10]. In table IV we have used learning material based on those dimentions for e-learning process. According to FSLSM, active students, tend to learn actively with working together with others. Whereas reflective students, learn by thinking things through learn alone. Visual students, remember best what they have seen (video, picture, table, etc.). Whereas verbal students, easy to remember the thing what they hear and written. Sequential students, tend to lean by exploring the material in sequence. Whereas global students, are not interested in obtaining details of the materials
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participation in forums as something useful for them [11]. Thus, discussion forums are a mechanism to increasing learning activities, and as tool interaction, not only for students but also the instructors in the online learning environment. Students who perform tasks tend to be more successful in studying and their motivation tends to increase (Deci & Ryan in [15]). In addition according to Qu et. al [16], a student's motivation in e-learning process can be seen from effort them performing tasks (time spent on tasks). The reason is associated with a strategy for motivate students to learn. Thus, giving assignments to students as a strategy to enhance learning activity in e-learning process. Students usually want to get a high score of their quiz results. They will attempt to provide more time to learn the learning materials to be tested. A study of motivation and
Figure 3. Six Strategies as Trigger Factors to Motivate Studens in e-Learning Process
B. Learning Layer Learning layer includes four components, they are students, LMS, learning objects, and assessment. The students will interact with the e-Learning (Learning Managemen System-LMS) through variety of features to support students in online course, in order to gain the learning materials that suit their needs, forum for discussion, take all the tests , etc. We focused on commonly used features, such as material, forum, assignment and test.
performance in a distance learning class, (by Chang and Lehman in [17]), found a significant improvement in students motivation and in scores on a comprehension test and suggest to improve the quality of quizzes as a motivational tool. In addition study Cocea & Weibelzahl , try to infer motivational states in e-learning process by student’s actions, such as reading a page, solving a quiz, etc [4]. Thus, quizzes not only provide a way to measure the performance of students but can also as a strategy to motivate them to learn in e-learning process. For many students, feedback on the results of assignments and quizzes will give them belief and self-confidence. Feedback has been considered as motivating the students to control and influence them in e-learning process [12][17] [19][20]. According to Getzlaf et. al. [19], feedback in elearning process was defined as information provided from teachers to students about courses activities, including assignments (feeding back information after task completion), etc. thus provide feedback on assignments and quizzes by teachers can be as a strategy to motivate students in e-learning pocess. Many students who concerned about their grades for various reasons, such as getting a decent job, to continue higher education, and so on. They will do what it takes to achieve that goal. According to Pagel & Reedy in [21], the bonus point (bonus grades) as motivation strategy, played an important role in increasing the number of students accessing the materia in e-learning process. Thus, a strategy of reward (bonus point) to make students become more motivated to engage actively in e-learning process. As we know, multimedia consists of text, image, audio, video and animation. A multimedia approach can be used to support a variety of learning setting, include to providing multimedia matrials to motivate students in e-learning process. Several studies have suggested that student motivation is higher in courses that use multimedia materials [22][23]. Thus, providing multimedia learning can be as a strategy to motivate students in e-learning pocess.
C. Activity Layer The LMS (Moodle, Atutor, WebCity, Blackboard, Dekeos, Ilias, Sakai, etc) is e-learning software as well as an organizer of those features and a tool to provides information about learning behavior patterns in an online learning situation. The information of learning behavior patterns is stored and managed in a database (learning log, forum log, and test result). A Learning log contains learning activities, such as the number of content access, the time spent on content access. Then, the log of forum discussion consist of the number of visits to the forum, number of posting, how long to stay in the forum. Whereas scores (grade), comprises scores quizzes and assignments. D. Identification Layer Each data log (learning log, forum log, and score) gives an indication related to identification the influence triple-factor in e-learning process. The identification of those factors aims at inferring the learning styles, motivation and knowledge ability states. Then, it provides the basis for personalization functionality. In order to identify students’ learning styles, motivation and knowledge ability, it uses a data log as mentioned earlier. These data will indicate learning style and motivation using learnning log (LL1...LLn), and forum log (FL1...FLn), and whereas the student knowledge ability using scores or grade (ST1...STn). In Figure 4, we show how to identify triple-factor in elearning process. learning style (active/reflective, sensing/intuitive, visual/verbal, sequential/global), motivation (high/low), and knowledge ability which is classified based on test score: poor (0-64) / average (65-74) / good (75-84) / excellent(85-100).
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TABLE V.
TYPES OF RECOMENDATION BASED ON TRIPLEFACTOR
Figure 4. Design Identification for Triple-Factor
E. Personalization Layer The result from this identification can be used to generate personalization. The layer aims to support personalise the learning materials based on triple-factor, and/or personalized recommendations to students. We explore the personalization a hierarchy of learning materials that suit to student’s learning style, motivation, and knowledge ability (triple-factor). Each factor is defined as a combination of tree values according to the learning styles (LS), motivation (MV) and knowledge ability (KA). LS= {Active (LS1) / Reflective (LS2), Visual (LS3) / Verbal (LS4), (Sequential (LS5) / Global (LS6), Sensing (LS7) / Intuitive (LS8)}. Then M = {High (MV1) / Low (MV2)}. Whereas KA = {(poor (KA1) / average (KA2) / good (KA3) / excellent (KA4)}. There are 64 combinations beetween LS and MV can be associated with a KA: • (LS1/LS2, MV1) = (KA1...KA4) • (LS3/LS4, MV1) = (KA1...KA4) • (LS5/LS6, MV1) = (KA1...KA4) • (LS7/LS8, MV1) = (KA1...KA4) • (LS1/LS2, MV2) = (KA1...KA4) • (LS3/LS4, MV2) = (KA1...KA4) • (LS5/LS6, MV2) = (KA1...KA4) • (LS7/LS8, MV2) = (KA1...KA4).
In addition to personalization, to improve e-learning process and make students learn more effectively, we also propose a personalization recommendation for Quiz (see Figure 5).
In Table V, we show how several types of recommendation (R1...R64), which resulting from combination triple-factor in elearning process, for example: • Type of recommedation_1 (R1) is a learning style "active" (LS1), with the motivation "high" (MV1), and the ability of knowledge "poor" (KA1). • Type of recommedation_2 (R2) is a learning style "active" (LS1), with the motivation "low" (MV2), and the ability of knowledge "poor" (KA1). • Type of recommedation_3 (R3) is a learning style "reflective" (LS2), with the motivation "high" (MV1), and the ability of knowledge "poor" (KA1). ........... • Type of recommedation_64 (R64) is a learning style "intuitive" (LS8), with the motivation "low" (MV2), and the ability of knowledge "excellent" (KA4).
Figure 5. Design Personalized Recomendations for Quiz.
A student can access the quiz to measure his/her level of understanding of learning materials that has been learned. Then the student can answer the questions provided by the elearning system. The Number of correct answers will be calculated by the system to determine the category of knowledge ability, namely: poor (0-64) / average (65-74) / good (75-84) / excellent(85-100). The system will be recommending learning materials which must be learned (R1...Rn) via e-mail to student based on these category (except excellent catgory). Then at a set time, student is allowed to do the quiz again and expected to answer the questions better than ever before.
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Advanced Learning Technologies (ICALT 05), July 2005, Kaohsiung, Taiwan, IEEE Computer Society Press, pp 1026-1030.
V. CONCLUSION In this study, we have shown that there is an impact of triple-factor on learning activities in SCELE system. There is a relationship between the activities of the students and their grade in each of the student categories. This relationship has significant influence to relationship between the activities of students using SCELE system and test score (corelation coefficient is 0.32). However, SCELE does not yet support personalization learning materials and personalization recommendation. Thus we propose a dynamic personalization in e-learning process based on triple-factor arcitecture. The triple-factor support identification and personalization of learning material and recommedation in e-learning process. The architecture consists of five main layers: learning strategy layer, learning layer, activity layer, identification layer and personalization layer. Each layer will dynamically guide the student to achieve the goal of learning. Our future research is to implement the architecture in e-learning process in ordert to identify triple-factor and personalization learning materials and recommendation.
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