Simulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts

Abstract

Students’ interactions with exercises can reveal interesting features that can be used to redesign or effectively use the exercises during the learning process. The precise modeling of exercises includes how grades can evolve, depending on the number of attempts and time spent on the exercises. A missing aspect is how a precise relationship among grades, number of attempts, and time spent can be inferred from student interactions with exercises using machine learning methods, and how it differs depending on different factors. In this study, we analyzed the application of different machine-learning methods for modeling different scenarios by varying the probability of answering correctly, dataset sizes, and distributions. The results show that the model converged when the probability of random guessing was low. For exercises with an average of 2 attempts, the model converged to 200 interactions. However, increasing the number of interactions beyond 200 does not affect the accuracy of the model.