Diagnosing and Prescribing Solutions for Perceived Amotivators in Engineering Education Using Learning Analytics

Abstract

Amotivation, as a construct of the Self-Determination Theory (SDT), represents a critical barrier to student engagement and success in engineering education. While a lot of study has concentrated on intrinsic and extrinsic motivators, there has been little focus on diagnosing and addressing amotivators within this context. This study explores the perceived amotivators among second-year (third-semester) engineering students registered in a formal program, utilizing learning analytics to derive actionable insights. A survey consisting of five statements related to amotivation was administered, with responses measured on a 5-point Likert scale.

Key analytical methods employed include Pearson’s correlation analysis to evaluate the relationships between survey statements, k-means clustering to identify patterns of amotivation based on survey responses, and further clustering analysis to figure out the association between mean amotivation scores and cumulative grade point averages (CGPA). The findings reveal distinct clusters of students with varying levels of amotivation and academic performance, providing a sophisticated comprehension of the interplay between perceived amotivators and student outcomes.

The current study contributes to the existing body of knowledge by diagnosing amotivators in engineering education and prescribing targeted interventions. The outcomes emphasize the significance of addressing both cognitive and emotional barriers to enhance student engagement, improve academic performance, and foster a more supportive learning environment. These revelations provide a basis for educators and administrators to develop data-driven strategies to mitigate amotivation and promote student success