Predicting academic performance with learning analytics in virtual learning environments: A comparative study of three interaction classifications

Learning analytics is the analysis of static and dynamic data extracted from virtual learning environments, in order to understand and optimize the learning process. Generally, this dynamic data is generated by the interactions which take place in the virtual learning environment. At the present time, many implementations for grouping of data have been proposed, but there is no consensus yet on which interactions and groups must be measured and analyzed. There is also no agreement on what is the influence of these interactions, if any, on learning outcomes, academic performance or student success. This study presents three different extant interaction typologies in e-learning and analyzes the relation of their components with students? academic performance. The three different classifications are based on the agents involved in the learning process, the frequency of use and the participation mode, respectively. The main findings from the research are: a) that agent-based classifications offer a better explanation of student academic performance; b) that at least one component in each typology predicts academic performance; and c) that student-teacher and student-student, evaluating students, and active interactions, respectively, have a significant impact on academic performance, while the other interaction types are not significantly related to academic performance.

Student academic performance stochastic simulator based on the Monte Carlo method

In this paper, a computer-based tool is developed to analyze student performance along a given curriculum. The proposed software makes use of historical data to compute passing/failing probabilities and simulates future student academic performance based on stochastic programming methods (MonteCarlo) according to the specific university regulations. This allows to compute the academic performance rates for the specific subjects of the curriculum for each semester, as well as the overall rates (the set of subjects in the semester), which are the efficiency rate and the success rate. Additionally, we compute the rates for the Bachelors degree, which are the graduation rate measured as the percentage of students who finish as scheduled or taking an extra year and the efficiency rate (measured as the percentage of credits of the curriculum with respect to the credits really taken). In Spain, these metrics have been defined by the National Quality Evaluation and Accreditation Agency (ANECA). Moreover, the sensitivity of the performance metrics to some of the parameters of the simulator is analyzed using statistical tools (Design of Experiments). The simulator has been adapted to the curriculum characteristics of the Bachelor in Engineering Technologies at the Technical University of Madrid(UPM).