PRELIMINARY DEVELOPMENT OF LEARNER SUPPORT PREDICTION MODEL FOR E-LEARNING BASED ON SELF-

REGULATED LEARNING FACTORS

Yoshiko Goda1, Masanori Yamada2, Hiroshi Kato3, Takeshi Matsuda4, Yutaka Saito5, Hiroyuki Miyagawa5

1Kumamoto University (JAPAN), 2Kanazawa University (JAPAN), 3The Open University of Japan (JAPAN), 4Yamagata University (JAPAN), 5Aoyama Gakuin University (JAPAN)

ygoda@ge.kumamoto-u.ac.jp, mark@mark-lab.net

Abstract The purpose of this research is to develop learner support prediction model for e-learning courses based on self-regulated learning (SRL) factors. The model aims to predict learners’ needs and typical behaviour to determine learner types at the forethought phase of Zimmerman’s three-phase model (1998) to help them complete e-learning courses. This report consists of two main parts: (1) exploration of factors of SRL for e-Learning and (2) development of preliminary model of the Factors on e-learning course completion. The standardized questionnaire for SRL skills developed by Wolter, Pintrich, & Karabenic (2003) were modified and shorten based on the pilot study results with exploratory and confirmatory factor analysis. The final inventory consists of 40 questions with high reliability (Overall Chronbach alpha = .931). 857 college students’ data were analyzed and there found four SRL factors including (1) affective, (2) cognitive, (3) help-seek, and (4) self-directed factors. Then, a preliminary model to determine learner types at the forethought phase of Zimmerman’s three-phase model (1998) students e-learning classes without any face-to-face instruction. In order to develop a preliminary learner support model with the four SRL factors to predict learners’ e-learning course completion, 224 students’ data were examined with path analysis. The model showed an excellent fit (χ2

(4) = 2.725, p=.605, RMSEA=.000, GFI=.995, AGFI=.982). The significance of this research is to provide information on learners’ needs priori to a semester, which helps instructors and mentors to plan learner support and estimate necessary efforts to help students complete a course.

Keywords: Self-Regulated Learning, e-Learning, Online Education, Factor Analysis, Path Model, Higher Education.

1 INTRODUCTION e-Learning, especially, the one which allows students learn at any time and any place, requires their self-regulated learning skills. We have been researching on learner support for e-learning in order to increase their course completion rate focusing on mentoring. Currently, the number of e-learning courses provided at universities or companies increase and burdens, expectations, and requirements to mentors have been mounted up. Then, it became one of issues how to maintain the quality support for students within the budget and time limitations. Some solutions to decrease and diversify burdens of mentors should be applied to keep providing students quality leaner supports for e-learning.

The purpose of this research is to develop learner support prediction model for e-learning courses based on self-regulated learning (SRL) factors. The model aims to predict learners’ behaviour and tendencies of learning based on their needs at the forethought phase of Zimmerman’s three-phase model (1998) to help them complete e-learning courses. This report consists of two main parts: (1) exploration of factors of SRL for e-Learning and (2) development of preliminary model of the Factors on e-learning course completion. The significance of this research is to provide information to instructors, course managers, mentors, and e-learning staff on learners’ needs priori to a semester, which helps them to plan learner support and estimate necessary efforts to help students complete a course.

Proceedings of ICERI2010 Conference. 15th-17th November 2010, Madrid, Spain.

ISBN: 978-84-614-2439-9001960

Figure 1. SRL Cycle (Zimmerman, 1888)

1.1 Self-Regulated Learning and Development of Learner Support System for e-Learning

1.1.1 Self-Regulated Learning (SRL)

Self-regulation refers to the degree to which students are “metacognitively, motivationally, and behaviorally active participants of their own learning process” (Zimmerman, 1989). Successful performance in higher education is related to one’s self-regulation (Zimmerman & Schunk, 2008).Self-monitoring and control are fundamental categories of metacognition and consciousness (Kihlstrom, 1984) and can be causally efficacious for learning (Nelson, et al., 1996). To enhance self-regulated learning (SRL) skills, metacognition should be utilized properly.

Zimmerman (1998) proposed a three-phase model: forethought, performance control, and self-reflection phases (Fig. 1). The forethought phase refers to planning to precede and set the stage of performance in learning. This process would include goal-setting, attribution, self-efficacy, and intrinsic motivation for learning and so on. At the performance control phase, self-monitoring and adjustment of performance is encouraged. During this process, SRL skills such as time management, task strategies, and help seeking is involved. The self-reflection phase indicates self-evaluation for one’s learning, and sometimes it requires social comparisons. The higher goal for this research is to decrease and relief mentors’ burdens from predicting students’ learning during a course at the stage of “forethought.”

1.1.2 Framework of SRL Wolter, Pintrich, and Karabenick (2003) listed four common assumptions of various SRL models: (1) active, constructive, (2) potential for control, (3) goal, criterion, or standard, and (4) mediator between personal and contextual characteristics and actual achievement or performance. Reflecting these four general assumptions, sample scales for a self-regulated learning assessment were developed. Table 1 shows their areas of SRL and their subcategories for assessment items.

Table 1. Categories for the SRL Scales ( Wolter, Pintrich, & Karabenick, 2003)

Note. *(#): number of items

2 TWO PARTS OF THE STUDY There are two main parts of this research as mentioned before: (1) Exploration of factors of SRL for e-learning and (2) Development of preliminary model of the SRL factors on e-learning course completion. This research was conducted at a private university at Kobe, Japan in fall semester, 2009. It is a small liberal art university and started to provide e-learning courses with credits in 2008.

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2.1 Exploration of Factors of SRL for e-Learning First, we tried to investigate what factor(s) would affect SRL for e-learning. In order to find factors, an inventory had been developed based on the assessment of Wolter, et al. (2003) introduced on the section 1.1.2. First, the scale items were translated into Japanese by one researcher and two other members checked the accuracy of the translations. Then, twenty items were eliminated from the final version with the agreements of all research members. The eliminated items were regarded as irrelative to e-learning or research interests. Thus, the SRL inventory consists of total 83 items in Japanese. This inventory was utilized to collect data for factor analysis.

2.1.1 Features of e-Learning

A semester includes 15 week lessons in a course in Japan. A 2-week span for each lesson as a time indication for learning one lesson was set prior to the semester. After the each span, students could still learn the materials on the LMS, but if they don’t complete learning materials in the span, they would be treated as tardiness of the lesson. Flexibility of time causes learners’ postpones of learning and which is one of problems with self-regulated e-learning. To solve the problem, the time-span is employed. This short span may force students experience self-regulated learning cycle; plan, monitoring, self-evaluation of their learning and students have 15 chances to practice self-regulated learning skills.

During implementation, mentors help students’ learning progress weekdays and send messages to individuals who do not have learning progress for a few weeks via LMS and/or email besides regular mentoring activities based on the mentoring guidelines created before the semester begins. Mentors work at Learner Support Center regularly, so when students don’t solve the problems regarding to the system, they can stop by the center and ask for help to the mentors. Some students don’t regularly check their email or LMS, in such a case, mentors manually create some posters with important messages and put on the bulletin boards at the school.

2.1.2 Subjects and Data Collection Total of 1212 students registering one of eight e-learning courses at a private university were asked to take the inventory in fall semester, 2009. The university was located in Kobe, Japan. The inventory was provided to students with a questionnaire function of the LMS. The e-learning courses offered online learning materials and learning activities via LMS with no face-to-face instructions. Data of 857 students who completed all items of the inventory were analyzed for this research.

2.1.3 Data Analysis Data were analyzed as followed: (1) examine ceiling and floor effects, (2) factor analysis with items after removing items of ceiling or floor effects, and (3) check reliability of each factor.

There are no items of ceiling effect and floor effects, so all 83 items were analyzed further. The factor analysis with a promax rotation and principal component analysis was employed to select main factors.

2.1.4 Results

Table 2 shows factor loadings of each question item for four SRL factors with 40 items. Factor 1 includes 16 items, and there has 11 items in Factor 2, 9 items in Factor 3, and 4 in Factor 4. Table 3 summarizes Chronbach’s alpha for each factor with the question item number. Except for Factor 4, Factors 1 to 3 have relatively high reliability with over .9 alpha. Overall reliability for 40 items was also high (Chronbach alpha = .931).

2.1.5 Four Factors Considering the question items for each factor (Appendix A), we carefully named four factors respectively: (1) affective, (2) cognitive, (3) help-seek, and (4) self-directed factor.

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Table 2. Factor Loadings of Question Items for Four SRL Factors

Table 3. Chronbach’s Alpha for Each Factor

2.2 Development of Preliminary Model of the SRL Factors on e-Learning Course Completion

After determining four SRL factors at the first stage of the research, we would like to investigate relationship among factors and learners’ course completion. Then, we challenged to develop a preliminary model of the SRL factors on e-learning.

2.2.1 Course Completion Rate As introduced in Section 2.1.1, the 2-week span for each lesson was set for all 15 lessons. When a student finished learning materials and assignments for one lesson within the designated time span, it would be marked as an on-time lesson completion. Our research interest was to discriminate self-regulated learners from learners who need more supports from mentors. Thus, on-time completion rate was calculated for each student with the formula of “on-time lesson completion / 15.” For the preliminary model development with four factors, 224 students’ data were examined with path analysis.

2.2.2 Path Model

The path model indicates Factor 2 “Cognitive” mediated between other three factors and “Completion Rate, which is negative effect with statistical significance” (Fig 2.). Factor 1 “Affective” had direct effects on “Cognitive” and “Help-Seek.” “Help-Seek” had a significant positive direct-effect on “Cognitive.” This model showed an excellent fit (χ2

(4) = 2.725, p=.605, RMSEA=.000, GFI=.995, AGFI=.982).

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Figure 2. Path Model of Four SRL Factors on e-Learning Course Completion Rate

Note: *p<.05

3 CONCLUSION AND DISCUSSION Four factors ((1) Affective, (2) Cognitive, (3) Help-Seek, (4) Self-Directed) were found for SRL on e-Learning as a results of factor analysis. The preliminary model of the factors and e-learning completion rate was developed based on the 224 students’ data. Our ultimate goal is to develop a mentor support system to minimize mentors’ burden with predicting learners’ learning behaviour prior to a semester. To accomplish our goal, we would continuously research on relation between learners’ SRL factors and learning. For future research, how degree levels of learners’ SRL factors might affect their learning would be investigated. In details, the learner types would be categorized as a result of a combination of three levels (positive, neutral, negative) for four SRL factors. Among 81 learner types (3*3*3*3), the types with the upper 27% and lower 27% on a completion rate of a course would be selected. Then using the selected data, the multiple regression correlation and path analysis would be performed for SRL factors on the completion rate. Furthermore, the fitness of the current model of SRL on completion rate for e-learning would be examined based on the data from other students in other situation.

ACKNOWLEDGEMENT This work was partially supported by KAKENHI, Grant-in-Aid for Scientific Research(B), (21300312). This study was conducted at Otemae University with help of Digital Educational Support, Co., LTD. in Japan.

REFERENCES

[1] Kihlstrom, J. (1984). Conscious, subconscious, unconscious: A cognitive perspective. In K. S. Bowers & D. Meichenbaum (Eds.), The unconscious reconsidered. NY: Wiley.

[2] Nelson, T. O., Dunlosky, J., Graf, A., & Narens, L. (1994). Utilization of metacognitive judgments in the allocation of study during multitrial learning. Psychological Science, 5(4), 207-213.

[3] Wolter, C. A., Pintrich, P. R., & Karabenic, S. A.. (2003). Assessing Academic Self-regulated Learning. Paper presented for the conference on Indicators of Positive Development: Definitions, Measures, and Prospective Validity. Sponsored by Child Trends, National Institute of Health.

[4] Zimmerman, B.J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81, 307-313.

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[5] Zimmerman, B. J. (1998). Academic studying and the development of personal skill: A self-regulatory perspective. Educational Psychologist, 33, 73-86.

[6] Zimmerman, B. J., & Schunk, D. H. (2008). Motivation: An essential dimension of self-regulated learning. In D.H. Schunk & B. J. Zimmerman (Eds.), Motivation and self-regulated learning: Theory, research and applications (pp.1-30). NJ: Lawrence Erlbaum Associates.

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Appendix A

Table 3_a. Question Items for Factor 1 “Affective Factor” & Factor 2 “Cognitive Factor”

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Table 3_a. Question Items for Factor 3 “Help-Seeking Factor” & Factor 4 “Self-Directed Factor”

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