Learners have different needs and abilities; teachers have the ambition to intervene before it is too late. How may e-learning systems support this? Learning Analytics may be the answer but there is not a general-purpose model to adopt. Many learning analytics tools examine data related to the activities of learners in on-line systems. Research efforts in learning analytics tried to examine data coming from LMS tracks in order to define predictive model of students’ performances and failure risks and to intervene to improve the learning outcomes. The analytical methods are widely used but no theoretical references are clear. In this paper, we tried to define a prediction model for learning analytics. In particular, we adopted a Moodle-based LMS in a blended course and collected all data of more than 400 undergraduate students in terms of resource accesses and exam performances. The model we defined was able to identify the learners at risk during their learning processes only by analysing their navigation paths among the contents. © 2019, Italian e-Learning Association. All rights reserved.

Learning analytics to support learners and teachers: the navigation among contents as a model to adopt

Miranda, Sergio;Vegliante, Rosa
2019

Abstract

Learners have different needs and abilities; teachers have the ambition to intervene before it is too late. How may e-learning systems support this? Learning Analytics may be the answer but there is not a general-purpose model to adopt. Many learning analytics tools examine data related to the activities of learners in on-line systems. Research efforts in learning analytics tried to examine data coming from LMS tracks in order to define predictive model of students’ performances and failure risks and to intervene to improve the learning outcomes. The analytical methods are widely used but no theoretical references are clear. In this paper, we tried to define a prediction model for learning analytics. In particular, we adopted a Moodle-based LMS in a blended course and collected all data of more than 400 undergraduate students in terms of resource accesses and exam performances. The model we defined was able to identify the learners at risk during their learning processes only by analysing their navigation paths among the contents. © 2019, Italian e-Learning Association. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4729400
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