Machine Learning Classification of Parent-Child Stem Design Conversations

Abstract

Family engagement in STEM design activities play a huge role in shaping children's STEM interests and skills. Understanding these conversations is crucial for improving informal learning process and outcomes. However, comprehensive analysis of large data from STEM design activities is often done manually, which is time-consuming. This study applied machine learning ensemble techniques to classify conversations between parents and children during STEM design activities. The dataset was generated by transcribing video recordings of family-based STEM sessions, followed by annotation to categorize conversations according to distinct design stages: identify, understand, ideate, design, build, and test. Standard preprocessing and feature extraction techniques were employed, and the data was split into 80% for training and 20% for testing. The performance of machine learning models was evaluated using accuracy, precision, recall, Fl-score, and confusion matrix metrics. Accuracy ranged from 70% to 80%. Findings indicate that feature overlap across the 'Build' and 'Design' stages impact model accuracy. Overall results demonstrate the potential of machine learning to classify conversational stages in a STEM design activity. This study highlights the viability of ML-driven approaches in identifying structured design discussions within parent-child conversations, a means of analyzing large conversational data, supporting educators in fostering more engaging and effective learning environments. Recommendations for further model improvement and refinement are also discussed.