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A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator

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Design for Teaching and Learning in a Networked World (EC-TEL 2015)

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Abstract

This paper presents a Learning Analytics approach for understanding the learning behavior of students while interacting with Technology Enhanced Learning tools. In this work we show that it is possible to gain insight into the learning processes of students from their interaction data. We base our study on data collected through six laboratory sessions where first-year students of Computer Engineering at the University of Genoa were using a digital electronics simulator. We exploit Process Mining methods to investigate and compare the learning processes of students. For this purpose, we measure the understandability of their process models through a complexity metric. Then we compare the various clusters of students based on their academic achievements. The results show that the measured complexity has positive correlation with the final grades of students and negative correlation with the difficulty of the laboratory sessions. Consequently, complexity of process models can be used as an indicator of variations of student learning paths.

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Acknowledgments

This work was supported in part by the Erasmus Mundus Joint Doctorate in Interactive and Cognitive Environments, which is funded by the EACEA Agency of the European Commission under EMJD ICE FPA n 2010-0012. Also, we thank professors Domenico Ponta and Giuliano Donzellini for providing support and help to our experiment.

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Correspondence to Mehrnoosh Vahdat .

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Vahdat, M., Oneto, L., Anguita, D., Funk, M., Rauterberg, M. (2015). A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Educational Simulator. In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, E. (eds) Design for Teaching and Learning in a Networked World. EC-TEL 2015. Lecture Notes in Computer Science(), vol 9307. Springer, Cham. https://doi.org/10.1007/978-3-319-24258-3_26

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  • DOI: https://doi.org/10.1007/978-3-319-24258-3_26

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