Learning From the Features We Ignore: A Critical Perspective on Feature Engineering and the Role of Feature Learning in Learning Analytics

The integration of machine learning within educational environments has positioned learning analytics as a valuable approach for enhancing teaching and learning outcomes. This research examines the critical role of feature engineering in learning analytics, where the quality and interpretability of extracted features significantly influence model performance and adoption. Traditional feature engineering methodologies-comprising systematic extraction, evaluation, and selection of relevant features from educational data remain foundational but intensive. While automated feature extraction in the form of feature learning offers scalability, it often lacks interpretability, which is critical in education. Interpretability becomes particularly significant in light of the European Union’s 2024 AI Act, which explicitly mandates transparent and accountable artificial intelligence systems in high-risk domains, including education. Despite growing interest in learning analytics, the field lacks standardized methods for feature engineering, limiting model reproducibility and real-world applicability. Furthermore, highly accurate models often sacrifice interpretability, hindering adoption by educators. This study addresses two core challenges: the absence of reproducible feature engineering methodologies and the need for interpretable models aligned with ethical and legal standards.