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Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring

1
School of Engineering—Systems Engineering Program, Cooperative University of Colombia, Bogota 110231, Colombia
2
School of Engineering and Technology, Universidad Internacional de La Rioja, 26006 Logroño, Spain
3
Research Institute for Innovation & Technology in Education (ITED), Universidad Internacional de La Rioja, 26006 Logroño, Spain
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(3), 448; https://doi.org/10.3390/app9030448
Received: 23 December 2018 / Revised: 5 January 2019 / Accepted: 24 January 2019 / Published: 28 January 2019
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Abstract

Learning analytics (LA) has become a key area of study in educology, where it could assist in customising teaching and learning. Accordingly, it is precisely this data analysis technique that is used in a sensor—AnalyTIC—designed to identify students who are at risk of failing a course, and to prompt subsequent tutoring. This instrument provides the teacher and the student with the necessary information to evaluate academic performance by using a risk assessment matrix; the teacher can then customise any tutoring for a student having problems, as well as adapt the course contents. The sensor was validated in a study involving 39 students in the first term of the Environmental Engineering program at the Cooperative University of Colombia. Participants were all enrolled in an Algorithms course. Our findings led us to assert that it is vital to identify struggling students so that teachers can take corrective measures. The sensor was initially created based on the theoretical structure of the processes and/or phases of LA. A virtual classroom was built after these phases were identified, and the tool for applying the phases was then developed. After the tool was validated, it was established that students’ educational experiences are more dynamic when teachers have sufficient information for decision-making, and that tutoring and content adaptation boost the students’ academic performance. View Full-Text
Keywords: learning analytics; customised tutoring; learning adaptation; virtual classroom learning analytics; customised tutoring; learning adaptation; virtual classroom
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Simanca, F.; Gonzalez Crespo, R.; Rodríguez-Baena, L.; Burgos, D. Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring. Appl. Sci. 2019, 9, 448.

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