Automatic Assessment Item Bank Calibration for Learning Gap Identification

• Published in: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) pp. 1429 - 1435
• Main Authors: Narayanan, Sankaran, Saj, Fensa Merry, Bijlani, Kamal, Rajan, Sreeranga P.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2018
• Subjects: Adaptation models; Adaptive learning; Adaptive systems; assessments; Calibration; continuous and comprehensive Evaluation; Data models; evaluation methodologies; formative; personalized learning; Statistics; summative; Taxonomy
• DOI: 10.1109/ICACCI.2018.8554481

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.