The prediction of academic performance using engineering student's profiles. (July 2021)

• Record Type: Journal Article
• Authors: Gonzalez-Nucamendi, Andres; Noguez, Julieta; Neri, Luis; Robledo-Rella, Víctor; García-Castelán, Rosa María Guadalupe; Escobar-Castillejos, David
• Is Part Of: Computers & electrical engineering. Volume 93(2021)
• Publication Date: 2021-07
• Subjects: Educational innovation -- Higher education -- Engineering students -- Academic performance -- Multiple intelligence -- Self-regulation skills -- Learning strategies -- Affective strategies -- Student profiles -- Learning analytics; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107288
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: This article describes the determination of student profiles based on the constructs of multiple intelligences and on learning and affective strategies, in order to identify the most important characteristics for ensuring the academic success of engineering students. The two constructs were organized in terms of eight dimensions each: the basis for developing two questionnaires that were completed by 618 undergraduate engineering students, in an attempt to define their student profile. Three alternative measures were designed to determine numerical values for each dimension, according to their capacity to predict academic performance in terms of final grades, using regression analysis. According to the study's findings, the logical/mathematical dimension plays an important role in student performance, while anxiety has a negative effect on final grades. The definition of appropriate measures to determine students' cognitive, affective, and self-regulatory profiles can provide instructors with timely information to implement appropriate teaching strategies in their groups. Graphical abstract: Highlights: MI and SRLAS can be used to define student profiles Learning analytics based on SRLAS can be used to predict student performance Logical Mathematic intelligence favors academic performance for engineering education Student anxiety may undermine student academic performance