TY - GEN
T1 - Reproducibility of findings from educational big data
T2 - 7th International Conference on Learning Analytics and Knowledge, LAK 2017
AU - Oi, Misato
AU - Yamada, Masanori
AU - Okubo, Fumiya
AU - Shimada, Atsushi
AU - Ogata, Hiroaki
N1 - Publisher Copyright:
© 2017 ACM.
PY - 2017/3/13
Y1 - 2017/3/13
N2 - In this paper, we examined whether previous findings on educational big data consisting of e-book logs from a given academic course can be reproduced with different data from other academic courses. The previous findings showed that (1) students who attained consistently good achievement more frequently browsed different e-books and their pages than low achievers and that (2) this difference was found only for logs of preparation for course sessions (preview), not for reviewing material (review). Preliminarily, we analyzed e-book logs from four courses. The results were reproduced in only one course and only partially, that is, (1) high achievers more frequently changed e-books than low achievers (2) for preview. This finding suggests that to allow effective usage of learning and teaching analyses, we need to carefully construct an educational environment to ensure reproducibility.
AB - In this paper, we examined whether previous findings on educational big data consisting of e-book logs from a given academic course can be reproduced with different data from other academic courses. The previous findings showed that (1) students who attained consistently good achievement more frequently browsed different e-books and their pages than low achievers and that (2) this difference was found only for logs of preparation for course sessions (preview), not for reviewing material (review). Preliminarily, we analyzed e-book logs from four courses. The results were reproduced in only one course and only partially, that is, (1) high achievers more frequently changed e-books than low achievers (2) for preview. This finding suggests that to allow effective usage of learning and teaching analyses, we need to carefully construct an educational environment to ensure reproducibility.
UR - http://www.scopus.com/inward/record.url?scp=85016477103&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85016477103&partnerID=8YFLogxK
U2 - 10.1145/3027385.3029445
DO - 10.1145/3027385.3029445
M3 - Conference contribution
AN - SCOPUS:85016477103
T3 - ACM International Conference Proceeding Series
SP - 536
EP - 537
BT - LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
PB - Association for Computing Machinery
Y2 - 13 March 2017 through 17 March 2017
ER -
