Publication

Abstract : The benefit of assessments (formative or summative) is fully realized when they result in accurate identification of learning gaps. AMrita Personalized Learning and Evaluation (AMPLE) platform adopts a unified approach to pinpoint learning gaps by integrating student performance data from paper-and-pen assessments and computerized adaptive tests. In order to aid the estimation of learning gaps, we have also developed a calibration technique for automatic assignment of difficulty levels to assessment items. This calibration technique uses statistical features derived from student performance data and a Gaussian Mixture Model (GMM) to effectively identify difficulty levels of assessment items. We also show our verification of this model using a diverse data set of student assessments spread over six subjects and 6000 students. Our model achieved about 91% accuracy by comparing the model-generated output with teacher-supplied difficulty levels. By auto-calibrating the assessment items, we pave the way for accurate learning gap analysis, obviating significant efforts from the teacher.