This paper aims to introduce a multidimensional academic analytics approach to course evaluation aimed at enhancing the monitoring and improvement of teaching and learning quality. It addresses limitations of traditional survey-centric evaluation methods by implementing a platform that integrates student feedback, course performance data and contextual information for institutional-level analysis.
A course evaluation platform was designed and deployed on institutional data to facilitate descriptive, trend and pattern analyses across dimensions such as student satisfaction, course grades and grade distributions by academic term, course and instructor. The platform uses user-friendly dashboards to present comparative and historical views while minimising additional workload for faculty.
The platform supports users in relating satisfaction scores to grade patterns and contextual factors. User feedback highlights the platform’s usefulness in supporting comparative and longitudinal analyses that foster reflective teaching practices and informed decision-making. However, the study also identifies limitations, such as reliance on descriptive analytics and underutilisation of students’ qualitative open-ended feedback.
The platform provides a practical tool for faculty members to contextualise course evaluation results, identify atypical patterns for closer review and support data-informed discussion. Its emphasis on interpretable visualisations reduces the need for advanced data literacy, but effective adoption depends on user training, institutional support and clear policies facilitating the interpretation and use of analytics in decision-making.
This study contributes to academic analytics and quality assurance by presenting an institution-wide, multidimensional course evaluation platform that addresses the limitations of traditional evaluation methods. By integrating student satisfaction feedback and course performance data, the platform offers a holistic approach to course evaluation, paving the way for more effective teaching and learning enhancement. The study also highlights future research directions, including the integration of advanced text analytics for qualitative feedback.
