electronic international standard serial number (EISSN)
One of the most investigated questions in education is to know which factors or variables affect learning. The prediction of learning outcomes can be used to act on students in order to improve their learning process. Several studies have addressed the prediction of learning outcomes in intelligent tutoring systems environments with intensive use of exercises, but few of them addressed this prediction in other web-based environments with intensive use not only of exercises but also, for example, of videos. In addition, most works on prediction of learning outcomes are based on low level indicators such as number of accesses or time spent in resources. In this paper, we approach the prediction of learning gains in an educational experience using a local instance of Khan Academy platform with an intensive use of exercises and taking into account not only low level indicators but also higher level indicators such as students' behaviours. Our proposed regression model is able to predict 68% of the learning gains variability with the use of six variables related to the learning process. We discuss these results providing explanation of the influence of each variable in the model and comparing these results with other prediction models from other works.
Decision support system Educational data mining Learning analytics Predictive modelling