Closing the Teaching-Learning Loop in Learning Support Platforms
Student retention and welfare are now central to higher‑education strategy, not just for reputational or financial reasons but for sustaining meaningful student success. But too many platforms stop at data collection. The real breakthrough lies in closing the loop: converting study data into insights, enabling timely intervention, and tracking outcomes.
Ignoring the impact of analytics is a mistake: universities with real-time actionable information on student engagement can effectively target those areas where risks to continuation are evident – whether at the programme or cohort level, or defined by protected characteristics or risks to equality of opportunity.
Rachel Maxwell
Principal Advisor at Kortext
The problem: Why traditional LMS fall short
Most institutions utilise learning management systems or digital content platforms, gathering detailed engagement data. But in many cases, that information remains underused. Reports are generated, dashboards are reviewed, and trends are discussed, yet little feedback or individualised action follows.
In the UK, this gap is reflected in the data. Recent commentary places the national drop-out rate at around 6.3 per cent, while the Complete University Guide reports that the average continuation rate fell from 92.2 per cent in 2025 to 89.7 per cent in 2026 (Complete University Guide, 2026). These figures highlight a persistent issue: too many students disengage before completing their studies, often without targeted support or early-warning intervention.
From data to action
A new generation of learning-intelligence approaches is emerging to connect the full cycle of engagement data, behavioural insight, risk signalling, staff response, and outcome tracking. Rather than merely observing, these systems are designed to respond.
Adaptive analytics can identify when a student's study time, revision patterns or engagement habits begin to diverge from those typically associated with success. Equally important is the feedback process, which allows institutions to understand how interventions, from mentoring to wellbeing check-ins or pathway adjustments, influence subsequent engagement and performance.
The StudyStash pilot study with the University of Birmingham has shown that such systems can enhance cognitive load management, student engagement and retention, especially when insights are paired with opportunities for human-led intervention (StudyStash, 2025).
Spotting early welfare signals
Retention challenges are rarely academic alone. Declining engagement often stems from wellbeing, financial or social factors. When learning analytics integrate academic performance with behavioural and welfare indicators, institutions can better detect the early signs of difficulty.
For instance, a sustained reduction in study sessions accompanied by longer completion times might suggest discouragement or overwhelm. A pattern of late submissions or repeated attempts could indicate stress or insufficient foundational understanding. Similarly, reduced participation in collaborative or peer-based modules may reflect isolation or disengagement from the academic community.
Interpreted responsibly, these signals allow universities to intervene early and appropriately, connecting students to academic or welfare support before disengagement becomes withdrawal.
Ethics, privacy and trust
Using learning data to support students demands rigorous ethical safeguards. Responsible practice begins with anonymisation and role-based access controls, ensuring that only authorised staff can view sensitive information. It also requires clear consent processes, so students understand how their data is used and when it might trigger an intervention.
Crucially, human oversight remains essential: algorithmic indicators should prompt discussion, not automatic remediation. Ongoing monitoring and fairness reviews, as recommended in recent educational-AI guidance such as EDUCAUSE's Working Group Paper, further strengthen transparency and accountability (EDUCAUSE, 2025). This ethical framework helps ensure that data-driven insights translate into trusted, human centred action.
The future of retention
The next phase of student success will depend on closing the feedback loop, not simply measuring continuation, but understanding why students stay, thrive, or leave. Adaptive and ethically grounded systems that integrate learning and welfare insight have the potential to transform how universities sustain student success.
With national continuation rates now below 90 per cent, the urgency is clear. Retention is no longer about keeping students enrolled at all costs; it is about enabling every learner to progress with confidence, support and purpose. Closing the teaching-learning loop is no longer just innovation – it is imperative.
