From Test Scores to Neural Spikes: Predicting Students’ Abstract Reasoning Ability Using EEG with Attention-Based Models

Abstract reasoning is a key ability for students’ cognitive development, yet traditional methods often fail to provide an accurate and objective assessment. We propose a novel machine learning approach that utilises EEG data from low-cost headsets to predict response correctness in abstract reasoning tasks. This paper presents an adaptive LSTM model incorporating multi-head attention for analysing EEG data acquired during both the reasoning (pre-response) and feedback (post-response) phases of abstract reasoning questions. Results demonstrate the model’s ability to predict answer correctness with promising performance across multiple evaluation metrics. Additionally, we investigate the influence of self-reported confidence on EEG-based correctness prediction, yielding valuable insights. Our model demonstrates scalability and robustness when applied to larger datasets. This research paves a way for prospective applications of innovative tools in personalised feedback and adaptive learning systems, and advances methods for assessing and supporting cognitive growth.