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Students bring different levels of interest to learning experiences, which impacts how they engage with learning materials. This study aims to understand the relationship between student's interest levels and their scientific observation behaviors within a Minecraft-based learning system. Motivated by the growing interest in integrating human-AI collaboration within educational research, we combine the capabilities of Large Language Models (LLMs) with the expertise of human researchers to capture the emerging themes within students’ observations. Using epistemic network analysis, we then visualized and compared the observational patterns of students with high and low situational interest. Our findings indicate that students with higher situational interest tend to make observations across a broader range of topics, with a particular emphasis on scientific content. These results highlight the potential for developing timely interventions to support students with low situational interest. 

This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies — Zero-shot, Few-shot, and Few-shot with contextual information — as well as the use of embeddings. We do so in the context of qualitatively coding three distinct educational datasets: Algebra I semi-personalized tutoring session transcripts, student observations in a game-based learning environment, and debugging behaviours in an introductory programming course. We evaluated the performance of each approach based on its inter-rater agreement with human coders and explored how different methods vary in effectiveness depending on a construct’s degree of clarity, concreteness, objectivity, granularity, and specificity. Our findings suggest that while GPT-4 can code a broad range of constructs, no single method consistently outperforms the others, and the selection of a particular method should be tailored to the specific properties of the construct and context being analyzed. We also found that GPT-4 has the most difficulty with the same constructs than human coders find more difficult to reach inter-rater reliability on. 

Extensive research underscores the importance of stimulating students' interest in learning, as it can improve key educational outcomes such as self-regulation, collaboration, problem-solving, and overall enjoyment. Yet, the mechanisms through which interest manifests and impacts learning remain less explored, particularly in open-ended game-based learning environments like Minecraft. The unstructured nature of gameplay data in such settings poses analytical challenges. This study employed advanced data mining techniques, including changepoint detection and clustering, to extract meaningful patterns from students' movement data. Changepoint detection allows us to pinpoint significant shifts in behavior and segment unstructured gameplay data into distinct phases characterized by unique movement patterns. This research goes beyond traditional session-level analysis, offering a dynamic view of the learning process as it captures changes in student behaviors while they navigate challenges and interact with the environment. Three distinct exploration patterns emerged: surface-level exploration, in-depth exploration, and dynamic exploration. Notably, we found a negative correlation between surface-level exploration and interest development, whereas dynamic exploration positively correlated with interest development, regardless of initial interest levels. In addition to providing insights into how interest can manifest in Minecraft gameplay behavior, this paper makes significant methodological contributions by showcasing innovative approaches for extracting meaningful patterns from unstructured behavioral data within game-based learning environments. The implications of our research extend beyond Minecraft, offering valuable insights into the applications of changepoint detection in educational research to investigate student behavior in open-ended and complex learning settings.