Learning analytics to determine profile dimensions of students associated with their academic performance
File(s)
Published version
Author(s)
Gonzalez-Nucamendi, Andres
Noguez, Julieta
Neri, Luis
Robledo-Rella, Victor
Guadalupe Garcia-Castelan, Rosa Maria
Type
Journal Article
Abstract
With the recent advancements of learning analytics techniques, it is possible to build predictive models of student academic performance at an early stage of a course, using student’s self-regulation learning and affective strategies (SRLAS), and their multiple intelligences (MI). This process can be conducted to determine the most important factors that lead to good academic performance. A quasi-experimental study on 618 undergraduate students was performed to determine student profiles based on these two constructs: MI and SRLAS. After calibrating the students’ profiles, learning analytics techniques were used to study the relationships among the dimensions defined by these constructs and student academic performance using principal component analysis, clustering patterns, and regression and correlation analyses. The results indicate that the logical-mathematical intelligence, intrinsic motivation, and self-regulation have a positive impact on academic performance. In contrast, anxiety and dependence on external motivation have a negative effect on academic performance. A priori knowledge of the characteristics of a student sample and its likely behavior predicted by the models may provide both students and teachers with an early-awareness alert that can help the teachers in designing enhanced proactive and strategic decisions aimed to improve academic performance and reduce dropout rates. From the student side, knowledge about their main academic profile will sharpen their metacognition, which may improve their academic performance.
Date Issued
2022-10
Date Acceptance
2022-10-10
Citation
Applied Sciences, 2022, 12 (20)
ISSN
2076-3417
Publisher
MDPI AG
Journal / Book Title
Applied Sciences
Volume
12
Issue
20
Copyright Statement
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000872150800001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
academic performance
ACHIEVEMENT
affective strategies
ANXIETY
Chemistry
Chemistry, Multidisciplinary
DESIGN
educational innovation
Engineering
Engineering, Multidisciplinary
FACULTY
higher education
INTELLIGENCE
Materials Science
Materials Science, Multidisciplinary
multiple intelligence
Physical Sciences
Physics
Physics, Applied
PREDICTION
Science & Technology
self-regulation skills
Technology
WORK
Publication Status
Published
Article Number
10560
Date Publish Online
2022-10-19