Predicting Assessment Item Difficulty Levels Using a Gaussian Mixture Model

• Published in: 2018 International Conference on Data Science and Engineering (ICDSE) pp. 1 - 6
• Main Authors: Narayanan, Sankaran, Saj, Fensa Merry, Soumya, M.D., Bijlani, Kamal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2018
• Subjects: Adaptation models; adaptive learning systems; Calibration; Data models; Electronic learning; evaluation methodologies; Gaussian mixture model; intelligent tutoring systems; Manuals; personalized learning
• DOI: 10.1109/ICDSE.2018.8527800

The difficulty level of an assessment item plays an important role in ensuring well qualified evaluation process as well as helping in the generation of appropriate assessments for personalized learning. AMrita Personalized Learning and Evaluation (AMPLE) platform adopts an automatic calibration methodology using Gaussian Mixture Models for difficulty level assignment. This methodology uses performance features derived from the test-takers responses recorded in the assessment engine. Verification of this model, carried out on a diverse data set of assessment items spread over six subjects and 6000 students achieved about 91% accuracy by comparing the model-generated output with teacher-supplied difficulty levels.