Towards tailored online learning: Predicting learner profiles in an online learning environment with perceived needs satisfaction

Abstract

This study examined the relationship of learner profiles and the satisfaction of basic psychological needs among 109 undergraduate physics students participating in an online algebra training program. The study utilized latent profile analysis to identify groups of learner profiles and explored the role of students’ perceived needs satisfaction in predicting group composition using multinomial logistic regression. The results indicated that not all students benefited equally from the online training program. Four distinct groups of learner profiles emerged from the analysis: high achiever-high engagement, high achiever-medium engagement, low achiever-high engagement, and low achiever-low engagement. There were significant differences in perceived autonomy and competence among these groups, with students’ perceived autonomy and competence predicting the group composition. The study suggests the importance of considering motivational factors such as students’ perceived needs satisfaction when designing tailored online learning experiences.

Keywords

• online learning
• perceived needs satisfaction
• adaptive learning
• latent profile analysis