Feature Selection Effect on Context‐Aware Teacher‐Support Systems

In multi‐criteria decision‐making (MCDM), structuring the problem by defining relevant alternatives and criteria is a critical prerequisite for effective analysis. In this paper, this foundational phase is addressed within the multifaceted context influencing teacher performance, which represents a crucial task for effective decision‐making in education. However, traditional approaches often struggle to capture the complex interaction between the different features distributed over the teacher's living environment, work setting and emotional state, which represent a large and complex set of potential decision criteria. These features, represented as criteria, are essential for a comprehensive understanding of a teacher's context, represented as alternatives. The proposed approach introduces a formal, ontology‐driven approach to this problem structuring task. We investigate the impact of feature selection on representing the multidimensional context of teachers (the alternatives), both individually and collectively. We propose a novel, unsupervised feature selection approach based on feature variance, which leverages a teacher context ontology to identify the most salient criteria (features) for subsequent analysis. By employing an importance‐based threshold, the approach efficiently eliminates features with minimal explanatory power, leading to a more parsimonious and interpretable representation. Additionally, the proposed approach demonstrates superior performance according to the selected context in several key areas, providing a consistent, reliable set of representing features across different variations of data. Moreover, the proposed approach generates interpretable structures, such as lattices, to facilitate informed decision‐making.