AcademiGuard is a proactive student monitoring platform that detects early signs of disengagement before academic performance suffers — powered by a GRU Autoencoder, Reinforcement Learning, and SHAP-based Explainable AI for full educator transparency.
An integrated architecture unifying predictive modeling, explainable AI, and adaptive cognitive support.
Random Forest + XGBoost + LightGBM (2:2:1 soft-voting) achieves 97.8% accuracy identifying at-risk students from 17-attribute academic and demographic data.
A GRU Autoencoder monitors 10-week LMS behavioral sequences and flags students exceeding the 97th percentile reconstruction error as high-risk for disengagement.
A Q-learning agent prescribes optimal re-engagement — soft nudges (40.9%), reminders (6.8%), or escalation — while LLaMA delivers personalized, empathetic messaging.
Bridging Educational Data Mining, Explainable AI, and Reinforcement Learning to build a trustworthy end-to-end academic early-warning ecosystem.
Educational Data Mining (EDM) and Learning Analytics (LA) have emerged as critical research domains [1][2][19]. With widespread LMS adoption, behavioral footprints — login frequencies, assessment results, assignment patterns, and session durations — are now accessible at scale and proven to be highly reliable early indicators of academic trajectory [3].
Supervised ML techniques (decision trees, SVMs, random forests, deep neural networks) achieve strong predictive accuracy [3][15][16]. Temporal models such as GRU and LSTM networks further capture sequential behavioral patterns for more accurate at-risk identification [4][5], while autoencoder architectures enable unsupervised anomaly detection in LMS activity streams [6]. However, their "black-box" nature limits classroom adoption — educators are hesitant to act on risk warnings they cannot interpret. SHAP (SHapley Additive exPlanations) has emerged as the leading approach to bridge this gap [8][10][11]. Learner disengagement patterns in online environments have been studied extensively, particularly in MOOC contexts [7][9][20].
Recent work also demonstrates that reinforcement learning agents can prescribe adaptive, personalized interventions by optimizing long-term re-engagement outcomes [12][13][14], yet no unified platform combines real-time anomaly detection, explainability, and adaptive interventions within a scalable cloud architecture [17][18].
Despite significant progress across individual research domains, a critical gap persists: the majority of existing systems remain siloed. They either focus exclusively on risk prediction without explainability, or lack the infrastructure to trigger adaptive personalized interventions automatically.
Three specific gaps were identified during our literature review:
AcademiGuard directly addresses this gap by synthesizing sequential disengagement detection, SHAP-driven explainability, and adaptive intervention recommendations into a single cohesive platform.
Traditional academic monitoring relies on lagging indicators — primarily midterm and final grades. Interventions based on these metrics occur too late to meaningfully alter a student's academic trajectory.
A student who gradually reduces forum participation, exhibits irregular login patterns, or consistently submits assignments late is often already on a dropout trajectory — long before grades reflect the problem. Universities frequently fail to catch these early warning signs.
This requires bridging early behavioral anomaly detection, prescription of optimal intervention strategies, and human-interpretable reasoning behind every automated recommendation.
Deploy a full-stack cloud educational monitoring system integrating React, Firebase, and Python microservices via FastAPI.
Build a Hybrid Ensemble (RF + XGBoost + LightGBM, 2:2:1 soft-voting) for baseline cumulative academic risk assessment.
Apply a GRU Autoencoder to detect sequential behavioral anomalies in LMS data before grades reflect academic decline.
Use a Q-learning RL agent to prescribe optimal, escalating re-engagement strategies based on student risk profiles and response history.
Embed SHAP-based XAI to provide per-feature attribution scores, ensuring every risk prediction is interpretable and actionable for educators.
AcademiGuard operates as a dual-engine ML framework with three integrated stages:
Soft-voting classifier aggregates Random Forest (300 estimators, depth 12), XGBoost (lr=0.05, scale_pos_weight=4), and LightGBM (2:2:1 weights). Trained on 17-attribute UCI dataset with 80:20 stratified split and 5-fold cross-validation. SHAP TreeExplainer surfaces per-feature contributions for every prediction.
Trained on 75,000 weekly LMS records across 5,000 students. Compresses 10-week behavioral sequences via Adam optimizer (MSE loss). Normal patterns are learned; reconstruction error spikes identify anomalous, disengaging students. Students exceeding the 97th percentile threshold trigger the intervention module.
Trained on 25,000 historical interaction samples. State space: risk_level, risk_trend, last_action, no_response_streak, fatigue_level. Selects from DO_NOTHING (52.3%), SOFT_NUDGE (40.9%), and REMINDER (6.8%). LLaMA translates decisions into personalized empathetic natural-language messages.
Key assessments and deliverables throughout the research lifecycle. Use the dropdown below to navigate to a specific milestone.
Initial topic assessment submitted with a brief overview of the proposed research, problem statement, objectives, and member task breakdown.
✓ Completed · 30 May 2025Team Agreement Form submitted outlining scope, objectives, overall solution architecture, and member responsibilities for AcademiGuard.
✓ Completed · 24 Jun 2025Submission of the Project Charter Form formally defining the project scope, objectives, team roles, and initial planning framework for AcademiGuard.
✓ Completed · 23 Jul 2025Presented to a panel of judges covering AcademiGuard's proposed dual-engine architecture, methodology, and expected research contributions.
✓ Completed · 11 Sep 2025In-depth analysis report covering the research problem, literature review, proposed solution architecture, methodology, and expected contributions to EDM and LA.
✓ Completed · 19 Sep 2025Document overviewing key Phase 1 implementation tasks completed by each team member, including the Hybrid Ensemble engine and initial dataset preprocessing.
✓ Completed · 05 Jan 2026Evaluation of 50% project completion demonstrating the Hybrid Ensemble engine achieving 97.8% accuracy and the early GRU prototype to the assessment panel.
✓ Completed · 06 Jan 2026Evaluation of 90% project completion with full platform demonstration: GRU anomaly detection, RL interventions, SHAP visualizations, and LLaMA-powered messaging.
✓ Completed · 09 Mar 2026Document overviewing key Phase 2 implementation tasks completed by each team member, including the GRU Autoencoder, RL intervention agent, and SHAP explainability layer.
✓ Completed · 09 Mar 2026Submission of the draft thesis covering the complete AcademiGuard research, implementation details, experimental evaluation, and preliminary conclusions.
✓ Completed · 26 Apr 2026Submission of the AcademiGuard research project website showcasing all project components, team information, documents, milestones, and research outcomes.
✓ Completed · 26 Apr 2026Final project evaluation and viva examination demonstrating the complete AcademiGuard platform and defending research contributions before the assessment panel.
⏳ Pending · 05 May 2026Submission of the evaluated research website alongside the project logbook documenting the development and research progress throughout the year.
⏳ Pending · 05 May 2026Formal submission of the IEEE-format research paper documenting the complete AcademiGuard platform, methodology, and experimental results for publication review.
⏳ Pending · 08 May 2026Submission of the final individual and group thesis documents covering the complete AcademiGuard research, implementation, evaluation, and conclusions.
⏳ Pending · 13 May 2026Submission of evidence confirming the research paper has been accepted or published in an IEEE conference or journal proceeding.
⏳ Pending · 15 Jun 2026All project documents grouped by category. Click a row to expand, then view or download individual files.
Presentation slides from past assessments and future milestones.
Information Technology undergraduates at SLIIT, dedicated to making AI-driven academic support transparent, proactive, and trustworthy.
Have questions about AcademiGuard? Reach out to our team or supervisors for collaboration or further information.
Sri Lanka Institute of Information Technology (SLIIT), Malabe, Sri Lanka
sanji.c@sliit.lk
ishara.w@sliit.lk
Department of Information Technology