Rafael Molina-Carmona
Faraón Llorens-Largo
ABSTRACT
Learning Analytics1 is a powerful tool that provides rich information for students, teachers and academic authorities. There is a wide range of possible applications, and one of them is leveraging the information to improve the instructional design of a course. In this research, we introduce the results of a Learning Analytics engine to improve all the stages of an Action Research experience. We have carried out three iterations: iteration 1, devoted to design an instructional course using an automated learning platform that collects data from the students; iteration 2, focused on the analysis of the individual and aggregated data collected from the students to obtain group behaviors; and iteration 3, currently under way, devoted to improve the structure of the course using the results of the previous iterations. We have made use of some graphical representations of the data that help to understand the aggregated data and to detect important events and moments of intervention. We have detected that the behavior of the students is strongly conditioned by the deadline structure of the course and that there is usually a crucial moment, by halfway of the course, where the situation is tending to stabilize and which is a good moment to reinforce the supervision on the student. Finally, we have stated that low performance students are usually recoverable to the last moment.
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Index Terms
- Analytics-driven redesign of an instructional course
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