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E-learning platforms became more and more popular not only for distance learning but also for learning in full-time education. As this popularity grows, we can use the data extracted from them to complement the professor's work and make predictions regarding students’ performance. In this paper, we present a dataset extracted from our e-Learning platform, which is based on the logs collected from testing activity. The focus of this paper is to present the dataset; the experiments presented in the paper are meant to explore the dataset along with its capabilities. The dataset consists of attributes relevant to the testing activity and provides labels which consist of average test grade and final exam grade. Our focus when building the dataset was to keep only the attributes relevant for the learning activity and to provide means to analyse and predict the student's final grade or failure. The paper presents the structure of the dataset, the methodology of collecting the data and experiments using several popular algorithms. The experimental results reveal that the actions performed by the users correlate with the results of the tests and the exam failure can be predicted with a pretty good accuracy using the default set of tuning parameters for our algorithms. As feature work, we can extend the set of experiments with other algorithms, and we can also use parameter tuning for each algorithm for a slight increase in performance.

Student Testing Activity Dataset from Data Structures Course