Learning Analytics to Determine Profile Dimensions of Students Associated with Their Academic Performance
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Online Learning and Analytics · Educational Innovations and Technology · E-Learning and Knowledge Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Tecnologico de Monterrey, School of Engineering and Science, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
Facultad de Ingeniería, Universidad Panamericana, Augusto Rodin 498, Mexico City 03920, Mexico
Funding
NOVUS
References (61)
Show source-provided references
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