Feedback effectively supports STEM learning. Past work usually compared learning gains when estimating the effectiveness of different feedback types. Learning rates, in contrast, quantify learning from individual instructional feedback events, which may confirm or challenge existing scientific knowledge about feedback. We study how feedback types and prior knowledge, as a common moderator of feedback effectiveness, influence learning rate. Log data from N=61 incoming first-year university students working with StoichTutor, a tutoring system for chemistry, are analyzed. A total of 1,169 feedback messages are manually categorized using a coding scheme informed by literature. We use instructional factors analysis (IFA) to assess the relation between feedback types and learning rate across students with low and high prior knowledge. Correctness feedback significantly improved the learning rate for all students. In contrast, indirect and next-step feedback had negative impact on learning rates. We discuss how next-step feedback, which provides learners with an explanation of the problem or next step without a prior mistake been made, is likely too unspecific (low prior knowledge) or redundant (high prior knowledge) for learners to be effective. To the best of our knowledge, our study is the first to model feedback-specific learning rates using IFA.
Early identification of student success is crucial for enabling timely interventions, reducing dropout rates, and promoting on time graduation. In educational settings, AI powered systems have become essential for predicting student performance due to their advanced analytical capabilities. However, effectively leveraging diverse student data to uncover latent and complex patterns remains a key challenge. While prior studies have explored this area, the potential of dynamic data features and multi category entities has been largely overlooked. To address this gap, we propose a framework that integrates heterogeneous graph deep learning models to enhance early and continuous student performance prediction, using traditional machine learning algorithms for comparison. Our approach employs a graph metapath structure and incorporates dynamic assessment features, which progressively influence the student success prediction task. Experiments on the Open University Learning Analytics (OULA) dataset demonstrate promising results, achieving a 68.6
