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The connection between student-staff interaction, students&#x2019; positive outcomes, and institutions has been widely studied as a key focus of research on student engagement and quality learning in higher education. In this study, a learning analytic approach is taken to establish a model for student-staff interaction. Two African institutions are engaged in the analysis for data acquisition. The two student engagement datasets used in this study are acquired by survey approach using National Survey of Student Engagement Instrument from the student perspectives. An association rule mining technique with Frequent Pattern Growth algorithm is implemented to discover interesting associative patterns among the student engagement indicators in relation to two student engagement datasets. Structural equation modelling was then employed to investigate the discovered interesting associative relationships. This study evaluated 16 different student-staff interaction models using various fit indices to identify the most accurate predictor of student-staff interaction (SSI). The results suggest that poor quality interactions (QI), reflective and integrated learning (RI), and quantitative reasoning (QR) are key factors that influence the quality of SSI. The methodology and resulting validated models offer a unique contribution to the field and can inform the development of policies and best practices to enhance student engagement and improve learning outcomes in higher education.

A Learning Analytics Approach To Modelling Student-Staff Interaction From Students’ Perception Of Engagement Practices