Learning analytics driven quantification of formative assessment fairness with contextualized interventions

This study investigates the integration of learning analytics with fairness quantification in formative assessment, with an emphasis on contextualized interventions that respond to inequities in student learning processes. The research adopts a multi-institutional dataset comprising over 4,500 students across 18 classrooms, integrating log files, assessment records, and survey responses to ensure demographic, behavioral, and socio-cognitive diversity. A hybrid fairness quantification model is developed, combining statistical fairness metrics such as equal opportunity and disparate impact ratios with learning analytics indicators, including feedback latency, participation depth, and adaptive engagement. Interventions were designed through a three-layered protocol involving algorithmic detection of inequities, contextual mapping of student profiles, and targeted instructional adjustments. The results show that the fairness gap is most significant in feedback distribution, and the delay has a particularly severe impact on students from less affluent socioeconomic groups. The intervention measures increased the fairness index score by an average of 0.37 points on the standardized 0-1 scale, and the student satisfaction score was 21% higher than that of the control group. The benefits confirmed by the three-semester longitudinal follow-up were consolidated, and the standard deviation of the fairness index decreased from 0.18 to 0.07, indicating greater fairness consistency among the cohorts. Research has found that an analytical framework that emphasizes fairness not only enhances the transparency of formative assessment but also improves scalable, evidence-based intervention measures, thereby bringing about sustainable educational equity reforms.