Early Identification of At-Risk Students Using Learning Analytics: A DataDriven Approach to Supporting Academic Success

Learning analytics has created new opportunities to identify students at risk of
academic failure, withdrawal, or disengagement before negative outcomes become
difficult to reverse. This paper synthesizes research on the use of learningmanagement-system activity, assessment records, administrative data, and other
educational traces to support early identification and timely academic intervention.
The synthesis examines data sources, predictive techniques, risk-detection timing, and
the translation of predictions into actionable support. The findings indicate that
effective early-warning approaches combine behavioral and academic indicators
rather than relying on a single measure. Common signals include declining learning
activity, weak assessment performance, late or missing submissions, irregular
participation, and prior academic risk. Predictive approaches include logistic
regression, decision trees, random forests, k-nearest neighbors, support-vector
methods, neural models, ensemble techniques, and time-series analysis; however,
predictive accuracy alone does not establish educational value. A key challenge is
translating prediction into effective intervention, as identifying high-risk students does
not necessarily ensure improved outcomes. The paper therefore proposes a data-toaction framework linking early identification with human review, transparent risk
communication, targeted outreach, and monitored support. Privacy, fairness,
transparency, false positives, and responsible use of student data are treated as central
design requirements. The study concludes that learning analytics is most educationally
valuable when predictive capability is embedded within a responsive support system
that enables institutions to act early, appropriately, and transparently.