Evaluation of Student’s Performance in Programming Using Item Response Theory

Abstract

In most areas of study, many colleges and universities now recognize that a unified approach has numerous advantages. However, the aptitude of the learner has been overlooked as a critical component in student achievement. As a result, a variety of tactics, such as personalization, have been developed to support learners and adapt to a variety of learners. IRT (Item Response Theory) was employed in the development of the learning model, which was then deployed in an e-learning environment. Assessments of different level of difficulty were provided throughout the learning process. IRT assesses a student’s understanding of the topics using a probabilistic technique that considers the difficulties of the test items. The test score was evaluated using the Rasch model, and the item data was used to assign a ranking to the courses. Lessons are scaled back until the student reaches his or her competency level. The results reveal that the personalized learning model can assess a student’s success depending on their test score. As a result, the amount of time spent studying is reduced. The student’s order to enlighten was elevated using the personalized learning framework. Consequently, the intellectual development was improved.