There is a certain philosophical or ethical side to this notion of two approaches as well: if learners are always 21st-century skills of collaboration, communication, with the knowledge, skills, and attitudes to participate that augment the human intellect, through visual approaches for learning analytics (Engelbart, 1995). BACKGROUNDChapter 12: Learning Analytics Dashboards guidelines on how to get started with the development of learning analytics dashboards are presented for practitioners and researchers.

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.

In online learning, teachers need constant feedback about their students' progress and regulation needs. Learning Analytics Dashboards for process-oriented feedback can be a valuable tool for this purpose. However, few such dashboards have been proposed in literature, and most of them lack empirical validation or grounding in learning theories. We present a teacher-facing dashboard for process-oriented feedback in online learning, co-designed and evaluated through an iterative design process involving teachers and visualization experts. We also reflect on our design process by discussing the challenges, pitfalls, and successful strategies for building this type of dashboard.

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This paper investigates the suitability of using Data Visualization, specifically Parallel Coordinates Plots (PCPs), as an instrument to help distance learning instructors identify students with poor performance. To answer this question, we developed a web application that lets users generate and interact with PCPs and evaluated it through usability tests inspired by the NOVIS frameworka usability framework specifically designed for evaluating data visualizations. The results show that PCPs and Data Visualization are perceived by distance learning instructors as a useful tool for following students' progress on Virtual Learning Environments.