TY - GEN
T1 - How patterns of students dashboard use are related to their achievement and self-regulatory engagement
AU - Kia, Fatemeh Salehian
AU - Teasley, Stephanie D.
AU - Hatala, Marek
AU - Karabenick, Stuart A.
AU - Kay, Matthew
N1 - Funding Information:
We wish to acknowledge the help provided by Teaching and Learning IT services, especially Jennifer Love; and Carl Haynes. We would also like to thank the Center for Academic Innovation for providing a graduate fellowship to support the first author’s participation in this project.
Funding Information:
We wish to acknowledge the help provided by Teaching and Learning IT services, especially Jennifer Love; and Carl Haynes.Wewould also like to thank the Center for Academic Innovation for providing a graduate fellowship to support the first author's participation in this project.
Publisher Copyright:
© 2020 Copyright held by the owner/author(s).
PY - 2020/3/23
Y1 - 2020/3/23
N2 - The aim of student-facing dashboards is to support learning by providing students with actionable information and promoting selfregulated learning. We created a new dashboard design aligned with SRL theory, called MyLA, to better understand how students use a learning analytics tool. We conducted sequence analysis on students' interactions with three different visualizations in the dashboard, implemented in a LMS, for a large number of students (860) in ten courses representing different disciplines. To evaluate different students' experiences with the dashboard, we computed chi-squared tests of independence on dashboard users (52%) to find frequent patterns that discriminate students by their differences in academic achievement and self-regulated learning behaviors. The results revealed discriminating patterns in dashboard use among different levels of academic achievement and self-regulated learning, particularly for low achieving students and high self-regulated learners. Our findings highlight the importance of differences in students' experience with a student-facing dashboard, and emphasize that one size does not fit all in the design of learning analytics tools.
AB - The aim of student-facing dashboards is to support learning by providing students with actionable information and promoting selfregulated learning. We created a new dashboard design aligned with SRL theory, called MyLA, to better understand how students use a learning analytics tool. We conducted sequence analysis on students' interactions with three different visualizations in the dashboard, implemented in a LMS, for a large number of students (860) in ten courses representing different disciplines. To evaluate different students' experiences with the dashboard, we computed chi-squared tests of independence on dashboard users (52%) to find frequent patterns that discriminate students by their differences in academic achievement and self-regulated learning behaviors. The results revealed discriminating patterns in dashboard use among different levels of academic achievement and self-regulated learning, particularly for low achieving students and high self-regulated learners. Our findings highlight the importance of differences in students' experience with a student-facing dashboard, and emphasize that one size does not fit all in the design of learning analytics tools.
KW - Academic achievement
KW - Self-regulated learning
KW - Sequential pattern mining
KW - Student-facing dashboard
UR - http://www.scopus.com/inward/record.url?scp=85082384274&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082384274&partnerID=8YFLogxK
U2 - 10.1145/3375462.3375472
DO - 10.1145/3375462.3375472
M3 - Conference contribution
AN - SCOPUS:85082384274
T3 - ACM International Conference Proceeding Series
SP - 340
EP - 349
BT - LAK 2020 Conference Proceedings - Celebrating 10 years of LAK
PB - Association for Computing Machinery
T2 - 10th International Conference on Learning Analytics and Knowledge: Shaping the Future of the Field, LAK 2020
Y2 - 23 March 2020 through 27 March 2020
ER -