Search for: All records

This exploratory study delves into the complex challenge of analyzing and interpreting student responses to mathematical problems, typically conveyed through image formats within online learning platforms. The main goal of this research is to identify and differentiate various student strategies within a dataset comprising image-based mathematical work. A comprehensive approach is implemented, including various image representation, preprocessing, and clustering techniques, each evaluated to fulfill the study’s objectives. The exploration spans several methods for enhanced image representation, extending from conventional pixel-based approaches to the innovative deployment of CLIP embeddings. Given the prevalent noise and variability in our dataset, an ablation study is conducted to meticulously evaluate the impact of various preprocessing steps, assessing their potency in eradicating extraneous backgrounds and noise to more precisely isolate relevant mathematical content. Two clustering approaches—k-means and hierarchical clustering—are employed to categorize images based on student strategies that underlies their responses. Preliminary results underscore the hierarchical clustering method could distinguish between student strategies effectively. Our study lays down a robust framework for characterizing and understanding student strategies in online mathematics problem-solving, paving the way for future research into scalable and precise analytical methodologies while introducing a novel open-source image dataset for the learning analytics research community.