Date of Award

8-2026

Degree Name

MS in Computer Science

Department/Program

Computer Science

College

College of Engineering

Advisor

Sumona Mukhopadhyay

Advisor Department

Computer Science

Advisor College

College of Engineering

Abstract

Measuring students’ sense of belonging, characterized by feelings of acceptance, inclusion, and encouragement from teachers, remains a significant challenge in computing education. Prior research has associated this multidimensional construct with positive academic outcomes and has identified instructors’ growth- and fixed-mindset messaging as a potential influence. However, belonging is a complex and deeply personal experience that is difficult to capture through direct observation alone. Current measurement methods rely on self-report surveys, which may not capture every aspect of an experience that can also involve emotional and cognitive responses.

This thesis investigates whether combining EEG data recorded during a belonging questionnaire with survey responses can improve the classification of students’ sense of belonging in computing education. Supervised multimodal machine learning models were constructed using several label-construction approaches derived from student survey responses. Multiple EEG representations were explored, including raw signals and two-dimensional scalograms. Because EEG-based machine learning is challenged by noisy recordings and limited training data, data augmentation was applied to the scalogram representations to improve model generalization. CNN-LSTM, CNN-GRU, and ChronoNet architectures were evaluated on these inputs, with the multimodal 2D CNN-GRU model achieving the strongest performance at an accuracy of 87.5% compared with its corresponding survey-only baseline of 80.0%. These findings offer preliminary evidence that EEG may provide complementary information and support more comprehensive approaches to understanding belonging in computing education.

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