01660nas a2200229   4500000000100000000000100001008004100002260001200043653002100055653002300076653002300099653001800122100002900140700002700169700002500196245010000221856005800321300000900379490001300388520101500401022001401416       9998                            d  c10/202410aContent Modeling10aLearning Analytics10aSimulated Students10aSmart Content1 aAlberto Jiménez Macías1 aPedro J. Muñoz Merino1 aCarlos Delgado Kloos00aSimulations for the Precise Modeling of Exercises Including Time, Grades and Number of Attempts  uhttps://www.ijimai.org/journal/bibcite/reference/3496  a1-140 vIn press3 aStudents’ 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.  a1989-1660