Higher education students who either do not complete the courses they have enrolled on or interrupt their studies indefinitely remain a major concern for practitioners and researchers. Within each course, early prediction of student dropout helps teachers to intervene in time to reduce dropout rates. Early prediction of course achievement helps teachers suggest new learning materials aimed at preventing at-risk students from failing or not completing the course. Several machine learning techniques have been used to classify or predict at-risk students, including tree-based methods, which, though not the best performers, are easy to interpret. This study presents two procedures for identifying atrisk students (dropout-prone and non-achievers) early on in an online university statistics course. These enable us to understand how classifiers work. We found that student dropout and course performance prediction was only determined by their performance in the first half of the formative quizzes. Nevertheless, other elements of participation on the virtual campus were initially considered. The classifiers will serve as a reference for intervention, despite their moderate performance metrics.

This paper describes the experience of applying a new teaching strategy to a basic mathematics course at the Open University of Catalonia. This strategy requires students to engage with practice and assessment tests with automatic feedback. The main aim of this research is to look into the extent to which the key elements of this teaching strategy influence the students’ course completion and academic achievement. In order to achieve this purpose, qualitative and quantitative variables over one term have been measured weekly. Our findings suggest that this teaching strategy significantly reduced the number of students who dropped out and improved academic results. More specifically, regular practice activities at the beginning of the semester help to engage students in the completion of the course.

Academic institutions dedicate a substantial effort to ensure the academic success of their students. At the course level, teachers recommend learning paths for students to guarantee the achievement of their learning outcomes. In terms of performance, these kinds of approaches are deemed more effective than others based uniquely on performing a collection of independent activities. However, there is no systematic means to validate if following the learning path is effective, nor to assess whether and to what extent students adhere to these recommendations. This paper introduces a novel technique for modelling recommended learning paths, including not only an evaluation of path utility, but also a quantitative measure of student adherence thereto using process mining, and more precisely, log skeletons. Following an event abstraction process regarding real student-recorded activity, a scoping process is employed to retain the trajectories that adhere to the prescribed learning path. The method based on process mining is translated into practice by considering an online university mathematics course. Results confirm the applicability of the method and, in this case, reveal that adhering to the suggested path correlates positively with final grades. Few students strictly follow the prescribed learning path, although the vast majority support it. The method can be easily applied to overcome several challenges associated with enhancing academic performance from the learning analytics perspective.

Most teachers of mathematics think that regular practice is essential for success. In face-to-face instruction settings, regular practice requires doing homework, which has to provide students with feedback in order to be useful. Online homework allows teachers to assume the workload involved in providing feedback to a large number of students enrolled in higher education. Several studies have established an association between completing online homework quizzes and learning achievement in face-to-face environments. In a fully asynchronous online environment, where students have to work autonomously, offering them a set of optional interactive quizzes with automated feedback may be a good teaching strategy to support them in their learning process. Within this framework, this paper investigates the relationship between optional quizzes and final exam performance in a calculus module. By means of multiple linear regression, this study found that an association exists between the learning achievement measured by final exam marks and participation in online quizzes together with the learning achieved through completion of these quizzes.