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The increasing use of the Learning Management Systems (LMSs) is making available an ever-growing, volume of data from interactions between teachers and students. This study aimed to develop a model capable of predicting students' academic performance based on indicators of their self-regulated behavior in LMSs. To accomplish this goal, the authors analyzed behavioral data from an LMS platform used in a public University for distance learning courses, collected during a period of seven years. With this data, they developed, evaluated, and compared predictive models using four algorithms: Decision Tree (CART), Logistic Regression, SVM, and Naïve Bayes. The Logistic Regression model yielded the best results in predicting students' academic performance, being able to do so with an accuracy rate of 0.893 and an area under the ROC curve of 0.9574. Finally, they conceived and implemented a dashboard-like interface intended to present the predictions in a user-friendly way to tutors and teachers, so they could use it as a tool to help monitor their students' learning process. [ABSTRACT FROM AUTHOR]

Discussion forums in learning management systems (LMS) have been shown to promote student interaction and contribute to the collaborative practice in the teaching-learning process. By evaluating the postings, teachers can identify students with learning difficulties. However, due to the large volume of posts that are generated on a daily basis in these environments, manual analysis becomes impractical. This article proposes a mechanism to support teaching through the thematic relevance analysis of the posts made by students in discussion forums. For this, text mining and metrics from network science were used to process and extract characteristics of the texts. Then, the processed texts were classified through supervised learning algorithms. The results show that the use of these techniques may generate potentially useful indicators for teachers to help them improve their pedagogical practices. [ABSTRACT FROM AUTHOR]