Authors:
Dirk T. Tempelaar
1
;
Bart Rienties
2
and
Quan Nguyen
2
Affiliations:
1
Maastricht University and School of Business and Economics, Netherlands
;
2
Open University U.K. and Institute of Educational Technology, United Kingdom
Keyword(s):
Blended Learning, Dispositional Learning Analytics, Learning Strategies, Multi-modal Data, Prediction Models, Tutored Problem-Solving, Untutored Problem-Solving, Worked Examples.
Related
Ontology
Subjects/Areas/Topics:
Computer-Supported Education
;
e-Learning
;
e-Learning Hardware and Software
;
e-Learning Platforms
;
Information Technologies Supporting Learning
;
Learning Analytics
;
Learning/Teaching Methodologies and Assessment
;
Simulation and Modeling
;
Simulation Tools and Platforms
Abstract:
The identification of students’ learning strategies by using multi-modal data that combine trace data with self-report data is the prime aim of this study. Our context is an application of dispositional learning analytics in a large introductory course mathematics and statistics, based on blended learning. Building on previous studies in which we found marked differences in how students use worked examples as a learning strategy, we compare different profiles of learning strategies on learning dispositions and learning outcome. Our results cast a new light on the issue of efficiency of learning by worked examples, tutored and untutored problem-solving: in contexts where students can apply their own preferred learning strategy, we find that learning strategies depend on learning dispositions. As a result, learning dispositions will have a confounding effect when studying the efficiency of worked examples as a learning strategy in an ecologically valid context.