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Ouroboros: early identification of at-risk students without models based on legacy data

Hlosta, Martin; Zdrahal, Zdenek and Zendulka, Jaroslav (2017). Ouroboros: early identification of at-risk students without models based on legacy data. In: LAK17 - Seventh International Learning Analytics & Knowledge Conference, 13 - 17 March 2017, Vancouver, BC, Canada, pp. 6–15.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1145/3027385.3027449
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Abstract

This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. We present the concept of a "self-learner" that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models.

There are three main contributions of this paper: (1) the concept of training the models for identifying at-risk students using data from the current course, (2) specifying the problem as a classification task, and (3) tackling the challenge of imbalanced data, which appears both in training and testing data.

The results show the comparison with the traditional approach of learning the models from the legacy course data, validating the proposed concept.

Item Type: Conference or Workshop Item
Copyright Holders: 2017 The Authors
Keywords: Student Retention; Predictive Analytics; Self-Learning; Imbalanced data; Learning Analytics
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Item ID: 49731
Depositing User: Kay Dave
Date Deposited: 28 Jun 2017 13:36
Last Modified: 28 Jun 2017 13:36
URI: http://oro.open.ac.uk/id/eprint/49731
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