Web-based learning systems with adaptive capabilities to personalize content are becoming nowadays a trend in order to offer interactive learning materials to cope with a wide diversity of students attending online education. Learners’ interaction and study practice (quizzing, reading, exams) can be analyzed in order to get some insights into the student’s learning style, study schedule, knowledge, and performance. Quizzing might be used to help to create individualized/personalized spaced repetition algorithm in order to improve long-term retention of knowledge and provide efficient learning in online learning platforms. Current spaced repetition algorithms have pre-defined repetition rules and parameters that might not be a good fit for students’ different learning styles in online platforms. This study uses different machine learning models and a rich context model to analyze quizzing and reading records from e-learning platform called Hypocampus in order to get some insights into the relevant features to predict learning outcome (quiz answers). By knowing the answer correctness, a learning system might be able to recommend personalized repetitive schedule for questions with maximizing long-term memory retention. Study results show that question difficulty level and incorrectly answered previous questions are useful features to predict the correctness of student’s answer. The gradient-boosted tree and XGBoost models are best in predicting the correctness of the student’s answer before answering a quiz. Additionally, some non-linear relationship was found between the reading learning material behavior in the platform and quiz performance that brings added value to the accuracy for all used models.

E-learning is being considered as a widely recognized option to traditional learning environments, allowing for highly tailor-made adaptive learning paths with the goal to maximize learning outcomes. However, for being able to create personalized e-learning systems, it is important to identify relevant student prerequisites that are related learning success. One aspect crucial for all kind of learning that is relatively unstudied in relation to e-learning is working memory (WM), conceptualized as the ability to maintain and manipulate incoming information before it decays. The aim of the present study was to examine how individual differences in online activities is related to visuospatial-and verbal WM performance. Our sample consisted of 98 participants studying on an e-learning platform. We extracted 18 relevant features of online activities tapping on Quiz accuracy, Study activity, Within-session activity, and Repetitive behavior. Using best subset multiple regression analyses, the results showed that individual differences in online activities significantly predicted verbal WM performance (p < 0.001, R 2 Adjusted = 0.166), but not visuospatial WM performance (p = 0.058, R 2 Adjusted = 0.065). The obtained results contribute to the existing research of WM in e-learning environments, and further suggest that individual differences in verbal WM performance can be predicted by how students interact on e-learning platforms.

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