Data-driven feature analysis for student performance prediction

We have witnessed the widespread adoption of online teaching and learning platforms in recent years. Teachers employ a variety of learning activities and techniques to follow their students' learning progress, including summative assessments. However, concerns have been raised about the trustworthiness of summative assessments, such as quizzes and tests. At the same time, online teachers have found other learning activities that are not typically included in predictive machine learning models of online platforms are effective in helping them identify student learning status. This study uses two feature selection techniques, filter and embedded, along with a teacher self-reported questionnaire to evaluate which learning activities influence students' performance in summative assessments like quizzes. Our findings reveal that features from non-summative learning activities can effectively predict students' performance in an online environment. Teachers agree on the importance of the identified learning activities' features in helping them determine their students' learning status. The study provides practical recommendations for educators, course designers, and policy-makers to optimize online assessment strategies.