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

Tempelaar, Dirk; Rienties, Bart and Nguyen, Quan (2018). Investigating learning strategies in a dispositional learning analytics context: the case of worked examples. In: Proceedings of the 8th International Conference on Learning Analytics and Knowledge, ACM, New York, pp. 201–205.

DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3170358.3170385
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Abstract

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.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 ACM
ISBN: 1-4503-6400-4, 978-1-4503-6400-3
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Research Group: Centre for Research in Education and Educational Technology (CREET)
Related URLs:
Item ID: 53925
Depositing User: Quan Nguyen
Date Deposited: 09 Apr 2018 09:11
Last Modified: 02 May 2018 14:39
URI: http://oro.open.ac.uk/id/eprint/53925
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