Investigating learning strategies in a dispositional learning analytics context: the case of worked examples

Dirk Tempelaar*, Bart Rienties, Quan Nguyen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


This study aims to contribute to recent developments in empirical studies on students’ learning strategies, whereby the use of trace data is combined with self-report data to distinguish profiles of learning strategy use [3–5]. We do so in the context of an application of dispositional learning analytics in a large introductory course mathematics and statistics, based on blended learning. Building on our previous work which showed marked differences in how students used worked examples as a learning strategy [7, 11], this study compares different profiles of learning strategies with learning approaches, learning outcomes, and learning dispositions. One of our key findings is that deep learners were less dependent on worked examples as a resource for learning, and that students who only sporadically used worked examples achieved higher test scores.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Learning Analytics and Knowledge
PublisherThe Association for Computing Machinery
Number of pages5
ISBN (Electronic)978-1-4503-6400-3
Publication statusPublished - 2018

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