Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12494/41579
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Title: Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring
Author: Simanca Herrera, Fredys Alberto
Gonzalez Crespo, Ruben
Rodriguez Baena, Luis
Burgos, Daniel
Email autor: fredys.simanca@campusucc.edu.co
Issue Date: 2019
Keywords: Customised tutoring
Learning adaptation
learning analytics
Virtual classroom
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. © 2019 by the authors.
Type: Artículo
Citation: SIMANCA F,GONZALEZ R,RODRIGUEZ L,BURGOS D. Identifying Students at Risk of Failing a Subject by Using Learning Analytics for Subsequent Customised Tutoring. Appl Sci (Basel). 2019. 9. (3):p. 1-17. .
Other Identifiers: https://doi.org/10.1109/TLA.2019.8896815
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061493372&doi=10.5944%2freop.vol.29.num.3.2018.23319&partnerID=40&md5=6aae43f70fef5afbc7937aef32d304f2
Appears in Collections:Artículos Científicos

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