Using Data Mining for Predicting Relationships between Online Question Theme and Final Grade

• Published in: Educational technology & society 2012-07, Vol.15 (3), p.77-88
• Main Authors: Abdous, M'hammed, He, Wu, Yen, Cherng-Jyh
• Format: Article
• Language: English
• Subjects: Academic learning; Access control; Case Studies; College Instruction; College Students; Computer Managed Instruction; Computer Mediated Communication; Correlation analysis; Cybersecurity; Data; Data Analysis; Data mining; Decision analysis; Decision making; Distance learning; Education; Educational environment; Educational technology; Electronic Learning; Experiential learning; Grades (Scholastic); Graduate students; Higher education; Information Retrieval; Integrated Learning Systems; Interaction; Learning experiences; Learning Management Systems; Logistic regression; Management Systems; Mathematical analysis; Messages; Multivariate Analysis; Online Courses; Online learning; Prediction; Questioning Techniques; Regression analysis; Special Issue Articles; Statistical analysis; Streaming media; Student Records; Students; Synchronous Communication; Teachers; Text analytics; Video data; Video Technology; Video transmission; Virginia
• ISSN: 1176-3647; 1436-4522; 1436-4522

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.