Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.