Data mining in educational technology classroom research: Can it make a contribution?. (October 2017)

• Record Type: Journal Article
• Authors: Angeli, Charoula; Howard, Sarah K.; Ma, Jun; Yang, Jie; Kirschner, Paul A.
• Is Part Of: Computers & education. Volume 113(2017)
• Page Start: 226
• Page End: 242
• Publication Date: 2017-10
• Subjects: Educational data mining -- Educational technology research -- Association rules mining -- Fuzzy representations; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2017.05.021
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: The paper addresses and explains some of the key questions about the use of data mining in educational technology classroom research. Two examples of use of data mining techniques, namely, association rules mining and fuzzy representations are presented, from a study conducted in Europe and another in Australia. Both of these studies examine student learning, behaviors, and experiences within computer-supported classroom activities. In the first study, the technique of association rules mining was used to understand better how learners with different cognitive types interacted with a simulation to solve a problem. Association rules mining was found to be a useful method for obtaining reliable data about learners' use of the simulation and their performance with it. The study illustrates how data mining can be used to advance educational software evaluation practices in the field of educational technology. In the second study, the technique of fuzzy representations was employed to inductively explore questionnaire data. The study provides a good example of how educational technologists can use data mining for guiding and monitoring school-based technology integration efforts. Based on the outcomes, the implications of the study are discussed in terms of the need to develop educational data mining tools that can display results, information, explanations, comments, and recommendations in meaningful ways to non-expert users in data mining. Lastly, issues related to data privacy are addressed. Highlights: The use of data mining in educational technology research is addressed. Association rules mining and fuzzy representations are presented. The results provide adequate understanding of students' interactions with technology. The results reveal patterns demonstrating differences in students' learning experiences. Implications for learning design are addressed.