Detecting Incomplete Learners in a Blended Learning Environment Among Japanese University Students

Minoru Nakayama, H. Kanazawa, H. Yamamoto

Abstract


To examine the feasibility of identifying incomplete participants who had not eventually completed a course in a blended learning environment using current learning behavioral data, access log data of complete and incomplete participants were analyzed. There is a significant difference between the two sets, and the number of accesses correlates with the final test score. Discrimination analysis was conducted using several variables across the learning process, and the ratio of those taking part in online tests was significant. Discrimination performance improved in relation to the number of accesses. The estimation performance was determined for two disparate courses in order to detect incomplete participants.

Keywords


Blended learning, Incomplete participants, Access log, Discrimination

Full Text:

PDF


Copyright (c) 2017 Minoru Nakayama, H. Kanazawa, H. Yamamoto


International Journal of Emerging Technologies in Learning. ISSN: 1863-0383
Creative Commons License SPARC Europe Seal
Indexing:
Web of Science ESCI logo Engineering Information logo INSPEC logo DBLP logo ELSEVIER Scopus logo EDiTLib logo EBSCO logo Ulrich's logo Google Scholar logo Microsoft® Academic SearchDOAJ logo