A Learning Analytics Approach to Correlate the Academic Achievements of Students with Interaction Data from an Education
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 studen
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DITEN - Universit` a degli Studi di Genova, 16145 Genoa, Italy [email protected], [email protected] 2 DIBRIS - Universit` a degli Studi di Genova, 16145 Genoa, Italy [email protected] Department of Industrial Design, Eindhoven University of Technology, 5612AZ Eindhoven, The Netherlands {m.funk,g.w.m.rauterberg}@tue.nl
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. Keywords: Learning analytics · Educational data mining · Technology Enhanced Learning · Process mining · Complexity · Interaction data · Educational simulator
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Introduction
Learning Analytics (LA) and Educational Data Mining (EDM) have raised a lot of attention among researchers and practitioners of Technology Enhanced Learning (TEL) in the last decade. The aim is to gain more insight into the behavior of learners by building models based on data collected from learning c Springer International Publishing Switzerland 2015 G. Conole et al. (Eds.): EC-TEL 2015, LNCS 9307, pp. 352–366, 2015. DOI: 10.1007/978-3-319-24258-3 26
A Learning Analytics Approach to Correlate the Academic Achievements
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tools [1]. Consequently LA and EDM applied in educational settings enhance the understanding of various stakeholders (students, teachers, administrators, etc.) about the way people learn in a data-driven way. As a result, the TEL systems can be improved to be more personalized and adaptive, and learning can be optimized as one of the main goals [2–5]. In the environments where inquiry-guided learning is applied rather than traditional methods, e-tools can play an important role in providing guidance and principles to optimize learning. Inquiry-guided learning focuses on contexts where learners are meant to discover knowledge rather than passively memorizing the concepts [6–8]. In this way, the lesson begins with a set of observations to interpret, and the learner tries to analyze the data or solve the problem by the help of the guiding principles [9], resulting in a more effective educational approach [10]. In this context, simulation-base
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