Can Data-Driven Strategies Enhance Student Engagement and Wellbeing?

January 10, 2025

In recent years, universities have increasingly turned to data-driven strategies to support students’ academic and personal development. By integrating data, theory, and human elements, institutions aim to create a robust framework for student support that encompasses both academic engagement and wellbeing. This comprehensive approach places an emphasis on the interconnectedness of these elements to maximize student success. Leveraging data not only provides a deeper understanding of student behavior but also facilitates the deployment of proactive interventions that can address challenges before they escalate into significant issues.

The Role of Data in Higher Education

Institutions of higher learning are increasingly relying on data-driven approaches to foster student engagement and wellbeing. This practice involves integrating a university’s ethos with data collection and analysis, which in turn promotes collaborative interventions designed to optimize student outcomes. While established data proxies exist to measure academic engagement, the identification and utilization of proxies for student wellbeing are still evolving. This presents a challenge in effectively using these data sets for maximum impact. The evolving nature of these proxies necessitates continuous research and adaptation to ensure that they are accurately reflecting the nuances of student wellbeing.

The mental health crisis among young people presents a significant challenge to higher education institutions. Issues such as underreporting and non-disclosure of mental health concerns make it difficult to accurately assess the scope of the problem and implement effective solutions. Innovative data-driven strategies are essential to address these challenges effectively. Frameworks like the Jisc Core Specification for Student Engagement Analytics identify essential wellbeing data points that signal risks to student retention and continuation. By incorporating both wellbeing and traditional engagement metrics, these frameworks provide a more holistic view of student needs, allowing institutions to intervene more effectively.

Addressing Mental Health Through Analytics

The University Mental Health Charter and TASO’s Student Mental Health Evidence Hub are pivotal resources supporting institutions in tackling mental health challenges. These frameworks provide guidelines and best practices for capturing and using data to understand and support student mental health. An evaluation of the Office for Students’ mental health and analytics project at Northumbria University highlights the potential of analytics to predict and operationalize student wellbeing within support structures. This project demonstrated that analytics could identify students in need of mental health support beyond those flagged through traditional engagement or learning analytics.

However, the relationship between non-engagement and mental health risks is not straightforward. While analytics can flag students for potential mental health concerns, not all instances of non-engagement are indicative of such risks, adding complexity to the identification process. The feasibility of transferring and scaling the Northumbria model to other universities poses a critical question, particularly in scenarios involving initial non-disclosure or subsequent development of mental health issues. Ensuring the accuracy and relevance of the data used is essential for the model’s success across diverse educational environments.

The Importance of Data Accuracy and Expert Involvement

The success seen at Northumbria University was underpinned by extensive data accuracy and availability, achieved through a decade-long transformation project. This project involved the review of more than 800 data variables and the development of dynamic data systems. Involving mental health and wellbeing experts is critical to interpreting and acting on the data, helping to avoid pitfalls like false positives and missed cases. Effective data interpretation requires a deep understanding of the multifaceted nature of student behavior and mental health.

Kortext’s StREAM student engagement analytics product offers a complementary perspective, demonstrating the ability to identify risk with about 90% accuracy from just two core systems: the Virtual Learning Environment (VLE) and the student record. However, determining the precise causes of disengagement remains reliant on thoughtful conversations with students, who provide crucial context supported by their data and personal circumstances. These conversations help to uncover underlying reasons for disengagement, allowing for more targeted and effective interventions.

Combining Engagement and Wellbeing Analytics

The complexity of a holistic approach to student support demands the integration of both engagement and wellbeing analytics. This method enables universities to fulfill their legal responsibilities proactively by addressing mental health issues before they have a substantial impact on student engagement and academic performance. Relying solely on engagement data to identify mental health concerns may lead to too reactive an approach, highlighting the importance of timely and contextual assessments.

Moving forward, more research is necessary to address several key questions: how many students flagged by engagement analytics require mental health support? How often are students without initially disclosed mental health conditions subsequently identified as disengaged by engagement analytics systems? Do wellbeing analytics proxies identify the same students as those flagged by engagement analytics systems, or do appropriate categorizations emerge following student conversations? Establishing confidence in the data sets’ ability to timely and accurately identify the right students is crucial for the success of these analytics frameworks.

Future Directions and Policy Implications

In recent years, universities have increasingly adopted data-driven strategies to support students’ academic and personal growth. By merging data, theory, and human elements, these institutions aim to develop a strong framework for student support that covers both academic engagement and overall wellbeing. This inclusive approach highlights the interconnectedness of these aspects to maximize student success. Utilizing data provides a deeper insight into student behavior, allowing institutions to identify patterns and trends that may not be immediately evident. Furthermore, data analysis helps in crafting personalized support strategies that cater to individual needs. One significant advantage of leveraging data is its ability to enable proactive interventions. By identifying potential challenges early on, universities can address issues before they become serious, thereby improving retention rates and fostering a more supportive educational environment. Data-driven approaches ensure that students receive timely and effective support, promoting their academic achievements and personal development.

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