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Title: How Does a Data-Informed Deliberate Change in Learning Design Impact Students’ Self-Regulated Learning Tactics?
The current study measures the extent to which students’ self-regulated learning tactics and learning outcomes change as the result of a deliberate, data-driven improvement in the learning design of mastery-based online learning modules. In the original design, students were required to attempt the assessment once before being allowed to access the learning material. The improved design gave students the choice to skip the first attempt and access the learning material directly. Student learning tactics were measured using a multi-level clustering and process mining algorithm, and a quasi-experiment design was implemented to remove or reduce differences in extraneous factors, including content being covered, time of implementation, and naturally occurring fluctuations in student learning tactics. The analysis suggests that most students who chose to skip the first attempt were effectively self-regulating their learning and were thus successful in learning from the instructional materials. Students who would have failed the first attempt were much more likely to skip it than those who would have passed the first attempt. The new design also resulted in a small improvement in learning outcome and median learning time. The study demonstrates the creation of a closed loop between learning design and learning analytics: first, using learning analytics to inform improvements to the learning design, then assessing the effectiveness and impact of the improvements.  more » « less
Award ID(s):
1845436
PAR ID:
10579994
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACM
Date Published:
Journal Name:
Journal of Learning Analytics
Volume:
11
Issue:
2
ISSN:
1929-7750
Page Range / eLocation ID:
174 to 196
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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