Analysis of Online Behavior and Prediction of Learning Performance in Blended Learning Environments
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초록
A variety of studies to predict students’ performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students’ learning behaviors. However, it is still challenging to predict students’ learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students’ academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.
Abstract
A variety of studies to predict students’ performance have been conducted since educational data such as web-log files traced from Learning Management System (LMS) are increasingly used to analyze students’ learning behaviors. However, it is still challenging to predict students’ learning achievement in blended learning environment where online and offline learning are combined. In higher education, diverse cases of blended learning can be formed from simple use of LMS for administrative purposes to full usages of functions in LMS for online distance learning class. As a result, a generalized model to predict students’ academic success does not fulfill diverse cases of blended learning. This study compares two blended learning classes with each prediction model. The first blended class which involves online discussion-based learning revealed a linear regression model, which explained 70% of the variance in total score through six variables including total log-in time, log-in frequencies, log-in regularities, visits on boards, visits on repositories, and the number of postings. However, the second case, a lecture-based class providing regular basis online lecture notes in Moodle show weaker results from the same linear regression model mainly due to non-linearity of variables. To investigate the non-linear relations between online activities and total score, RF (Random Forest) was utilized. The results indicate that there are different set of important variables for the two distinctive types of blended learning cases. Results suggest that the prediction models and data-mining technique should be based on the considerations of diverse pedagogical characteristics of blended learning classes.
참고문헌 (22)
- Arnold, K. E., "Course signals at Purdue : Using learning analytics to increase student success", Paper presented at the Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (2012)
- Bayazit, A., "Educational data mining: Applications and trends" (2014), Springer
- Beck, J. E., "High-level student modeling with machine learning", Paper presented at the Intelligent tutoring systems (2000)
- Bishop, J. L., "The flipped classroom: A survey of the research", Paper presented at the ASEE National Conference Proceedings (2013)
- Breiman, L., "Random forests", Machine learning, 45(1), pp.5~32 (2001)
- Francis, R., "Blended learning landscapes", Brookes eJournal of learning and teaching, 1(3), pp.1~5 (2005)
- Garrison, D. R., "Blended learning : Uncovering its transformative potential in higher education", The internet and higher education, 7(2), pp.95~105 (2004)
- Garrison, D. R., "Institutional change and leadership associated with blended learning innovation : Two case studies", The internet and higher education, 18, pp.24~28 (2013)
- Graham, C. R., "A framework for institutional adoption and implementation of blended learning in higher education", The internet and higher education, 18, pp.4~14 (2013)
- Jo, I., "Analyzing the log patterns of adult learners in LMS using learning analytics", Paper presented at the The 4th International Conferecne on Learning Analytics Knowledge (2014)
- 조일현, "학습분석학을 활용한 e-러닝 학업성과 추정 모형의 통계적 유의성 확보 시점 규명", 교육공학연구, 29(2), pp.285~306 (2013), 한국교육공학회
- 조일현, "이러닝에서 학습자의 시간관리 전략이 학업성취도에 미치는 영향: 학습분석학적 접근", 교육정보미디어연구, 19(1), pp.83~107 (2013), 한국교육정보미디어학회
- Kim, Y., "Big data analytics in higher education: Usage status of virutal classroom and activities in LMS", Paper presented at the e-learning Korea 2014 (2014)
- Liebowitz, J., "Knowledge Management and E-learning" (2010), CRC Press
- Lykourentzou, I., "Dropout prediction in e-learning courses through the combination of machine learning techniques", Computers & Education, 53(3), pp.950~965 (2009)
- Macfadyen, L. P., "Mining LMS data to develop an"early warning system"for educators : A proof of concept", Computers & Education, 54(2), pp.588~599 (2010)
- Margulieux, L. E., "Hybrid, Blended, Flipped, and Inverted: Defining Terms in a Two Dimensional Taxonomy"
- Mostow, J., "An educational data mining tool to browse tutor-student interactions: Time will tell", Paper presented at the Proceedings of the Workshop on Educational Data Mining, National Conference on Artificial Intelligence (2005)
- Romero, C., "Web usage mining for predicting final marks of students that use Moodle courses", Computer Applications in Engineering Education, 21(1), pp.135~146 (2013)
- Romero, C., "Data mining in course management systems : Moodle case study and tutorial", Computers & Education, 51(1), pp.368~384 (2008)
- Singh, H., "Building effective blended learning programs", Educational Technology, 43(6), pp.51~54 (2003)
- Yu, T., "Educational technology approach toward learning analytics:Relationship between student online behavior and learning performance in higher education", Paper presented at the The 4th International Conferecne on Learning Analytics Knowledge (2014)