Abstract

Despite the importance of academic counselling for student success, providing timely and personalized guidance can be challenging for higher education institutions. In this study, we investigate the impact of counselling instances supported by a learning analytics (LA) tool, called TrAC, which provides specific data about the curriculum and grades of each student. To evaluate the tool, we measured changes in students' performance ranking position over 3 years and compared the performance of students who received counselling with and without the tool. Our results show that using the tool is related to an improvement in cohort ranking. We further investigated the characteristics of counselled students using cluster analyses. The findings highlight the potential beneficial influence on academic outcomes arising from the provision of guidance to students regarding their course load decisions via TrAC-mediated counselling. This study contributes to the field of LA by providing evidence of the impact of counselling supported by an LA tool in a real-world setting over a long period of time. Our results suggest that incorporating LA into academic counselling practices can improve student success.Practitioner notes What is already known about this topic By analysing student performance, teaching strategies and resource impact, learning analytics (LA) empowers institutions to make informed changes in curriculum design, resource allocation and educational policies. Through insights into academic progress, engagement and behaviour, LA counselling tools enable the identification of at-risk students and those needing additional support. In the related literature, there are areas for further exploration such as understanding the scalability and long-term effects of interventions on student success and retention. What this paper adds Through rigorous data analysis, the paper establishes a connection between LA utilization and enhanced student performance, offering concrete evidence of the effectiveness of LA interventions. By examining various factors such as academic stage and course load, the research offers valuable insights into the contextual nuances that optimize the outcomes of LA tool-based support. It adds to the growing body of evidence that supports the efficacy of data-driven interventions in education, fostering a more informed and evidence-based approach to student support and success. Implications for practice and policy Enhanced student support strategies: By tailoring counselling interventions to align with the identified effective conditions, educators can proactively address individual student needs, improving academic outcomes and retention rates. Informed decision making: The demonstrated positive impact highlights the potential of similar data-driven initiatives to foster student success. Policymakers can consider incentivizing the adoption of such interventions at institutional levels. Future directions for research: By identifying contextual factors that influence the efficacy of LA interventions, it encourages further exploration into how other LA interventions can be optimized for specific conditions. This can guide the development of more precise and effective student support strategies in the future.