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Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade
M'hammed Abdous, Wu He and Cherng-Jyh Yen
Journal of Educational Technology & Society
Vol. 15, No. 3, Learning and Knowledge Analytics (July 2012), pp. 77-88
Published by: International Forum of Educational Technology & Society
Stable URL: http://www.jstor.org/stable/jeductechsoci.15.3.77
Page Count: 12
You can always find the topics here!Topics: Text analytics, Learning, Online learning, Students, Teachers, Graduate students, Learning experiences, College students, Higher education, Experiential learning
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ABSTRACT As higher education diversifies its delivery modes, our ability to use the predictive and analytical power of educational data mining (EDM) to understand students' learning experiences is a critical step forward. The adoption of EDM by higher education as an analytical and decision making tool is offering new opportunities to exploit the untapped data generated by various student information systems (SIS) and learning management systems (LMS). This paper describes a hybrid approach which uses EDM and regression analysis to analyse live video streaming (LVS) students' online learning behaviours and their performance in their courses. Students' participation and login frequency, as well as the number of chat messages and questions that they submit to their instructors, were analysed, along with students' final grades. Results of the study show a considerable variability in students' questions and chat messages. Unlike previous studies, this study suggests no correlation between students' number of questions / chat messages / login times and students' success. However, our case study reveals that combining EDM with traditional statistical analysis provides a strong and coherent analytical framework capable of enabling a deeper and richer understanding of students' learning behaviours and experiences.
Copyright 2012 by International Forum of Educational Technology & Society (IFETS)