Nowadays learning analytics has been growing as a science, and at the University of Turin we are interested in its potential to enhance both the teaching and the learning experience. In the last few years we have gathered data from two projects: Orient@mente, and start@unito, with the latter offering open online university courses in various disciplines. In addition, we have also studied and analysed the results of the teacher training experience carried out for the start@unito project, as well as those obtained from a survey involving secondary school teachers and the possible employment of the start@unito OERs in their everyday teaching. Our sources of data are students’ activity online, the results of formative automatic assessment, and the questionnaires given to the learners; the types of questions range from Likert scale evaluations to multiple choice, yes/no and a few open questions. In this paper we discuss the different tasks we completed in our projects and evaluate their adherence with the learning analytics techniques in terms of structure, availability, statistics, outcomes, interventions and, in general, their usefulness and effectiveness. In this way, the insights gained from both usage tracking and questionnaires can be used whenever possible to make interventions to improve the teaching and the learning experience; at the same time, when such interventions were not possible, we reflected on why this happened and how we can change and improve our approach.

Boosting up data collection and analysis to learning analytics in open online contexts: an assessment methodology

Marina Marchisio;Sergio Rabellino;Fabio Roman;Matteo Sacchet;Daniela Salusso
2019

Abstract

Nowadays learning analytics has been growing as a science, and at the University of Turin we are interested in its potential to enhance both the teaching and the learning experience. In the last few years we have gathered data from two projects: Orient@mente, and start@unito, with the latter offering open online university courses in various disciplines. In addition, we have also studied and analysed the results of the teacher training experience carried out for the start@unito project, as well as those obtained from a survey involving secondary school teachers and the possible employment of the start@unito OERs in their everyday teaching. Our sources of data are students’ activity online, the results of formative automatic assessment, and the questionnaires given to the learners; the types of questions range from Likert scale evaluations to multiple choice, yes/no and a few open questions. In this paper we discuss the different tasks we completed in our projects and evaluate their adherence with the learning analytics techniques in terms of structure, availability, statistics, outcomes, interventions and, in general, their usefulness and effectiveness. In this way, the insights gained from both usage tracking and questionnaires can be used whenever possible to make interventions to improve the teaching and the learning experience; at the same time, when such interventions were not possible, we reflected on why this happened and how we can change and improve our approach.
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Data analysis, learning analytics, learning management system, open online courses, start@unito
Marina Marchisio, Sergio Rabellino, Fabio Roman, Matteo Sacchet, Daniela Salusso
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1714140
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