This research addresses the critical challenge of enhancing learner satisfaction in online education by proposing an innovative theoretical and practical model for personality-based personalization. Utilizing a quantitative approach, the study analyzed data from 283 students to examine connections between satisfaction, the Big-Five personality traits, and Learning Analytics metrics. Correlation analysis revealed a consistent pattern of statistically significant relationships between personality traits and satisfaction with nine online learning activities. For instance, Agreeableness correlated with satisfaction in media (r=0.232, p<0.001) and graphics (r=0.255, p<0.001). Log-file-based learning analytics demonstrate how personality traits manifest through unique digital tracks, such as the link between Extraversion and social engagement ('Likes') (r=0.164, p<0.05) and Openness and test performance (r=0.223, p<0.01). This data-driven analysis enables the system to identify learners' personality profiles based on digital behavioral patterns, thereby eliminating the traditional need for intrusive self-report questionnaires. The proposed model suggests that once a learner's profile is identified through these digital tracks, the system can proactively offer a "tailor-made" learning path consisting of learning activities found to be most satisfying for that specific personality type. Future research should empirically validate the model’s impact on online learning satisfaction. By providing a robust framework for aligning instructional design with automatic personality identification, this study offers an interdisciplinary, systemic, and scalable solution to learner diversity. This research highlights that as the online learning revolution is already here, adopting such personalization models promotes a truly inclusive and adaptive digital education that ensures no learner is left behind in the rapidly evolving digital age.

Authors:
Orit Baruth, Tel Aviv University, Israel
Anat Cohen, Tel Aviv University, Israel

About the Presenter(s)
Orit Baruth is a PhD candidate at Tel Aviv University’s Advanced Learning and Technology Research Lab, exploring the adaptation of personalized online learning paths by integrating learning analytics techniques with personality-based analysis.

See this presentation on the full schedule – Sunday Schedule

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