The proof of the pudding: Examining validity and reliability of the evaluation framework for learning analytics

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Abstract

While learning analytics (LA) is maturing from being a trend to being part of the institutional toolbox, the need for more empirical evidences about the effects for LA on the actual stakeholders, i.e. learners and teachers, is increasing. Within this paper we report about a further evaluation iteration of the Evaluation Framework for Learning Analytics (EFLA) that provides an efficient and effective measure to get insights into the application of LA in educational institutes. For this empirical study we have thus developed and implemented several LA widgets into a MOOC platform’s dashboard and evaluated these widgets using the EFLA as well as the framework itself using principal component and reliability analysis. The results show that the EFLA is able to measure differences between widget versions. Furthermore, they indicate that the framework is highly reliable after slightly adapting its dimensions.

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Scheffel, M., Drachsler, H., Toisoul, C., Ternier, S., & Specht, M. (2017). The proof of the pudding: Examining validity and reliability of the evaluation framework for learning analytics. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10474 LNCS, pp. 194–208). Springer Verlag. https://doi.org/10.1007/978-3-319-66610-5_15

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