Introducing meaning to clicks: Towards traced-measures of self-efficacy and cognitive load
Date
2019
Authors
Jovanović, J.
Dawson, S.
Gašević, D.
Whitelock-Wainwright, A.
Pardo, A.
Editors
Azcona, D.
Chung, R.
Chung, R.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
ACM International Conference Proceeding Series, 2019 / Azcona, D., Chung, R. (ed./s), pp.511-520
Statement of Responsibility
Conference Name
9th International Conference on Learning Analytics and Knowledge (LAK) (4 Mar 2019 - 8 Mar 2019 : Arizona State Univ, Tempe, AZ)
Abstract
The use of learning trace data together with various analytical methods has proven successful in detecting patterns in learning behaviour, identifying student profiles, and clustering learning resources. However, interpretation of the findings is often difficult and uncertain due to a lack of contextual data (e.g., data on student motivation, emotion or curriculum design). In this study we explored the integration of student self-reports about cognitive load and self-efficacy into the learning process and collection of relevant students' perceptions as learning traces. Our objective was to examine the association of traced measures of relevant learning constructs (cognitive load and self-efficacy) with i) indicators of the students' learning behaviour derived from trace data, and ii) the students' academic performance. The results indicated the presence of association between some indicators of students' engagement with learning activities and traced measures of cognitive load and self-efficacy. Correlational analysis demonstrated significant positive correlation between the students' course performance and traced measures of cognitive load and self-efficacy
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
Copyright 2019 Association for Computing Machinery