Exploring interactions between learners and ChatGPT from a learner agency perspective: A multiple case study on historical inquiry
A noticeable surge in students’ widespread adoption of ChatGPT in the past year brought attention to the need for a deeper understanding of their interactions with this new technology. While attempts at theorising learner-ChatGPT interactions have been made, few studies offer empirical accounts of the interactions between learners and ChatGPT. This study aims to address this gap by utilising Emirbayer and Mische’s Choral Triad of Agency as an analytical framework to investigate secondary school students’ self-initiated interactions with ChatGPT in the context of historical inquiry. Through an in-depth examination of three cases, we unpacked three distinct types of learner-ChatGPT interactions—ChatGPT-as-historical source, ChatGPT-as-feedback, and principled non-use. Although students presented unique interaction patterns with ChatGPT, each case was found to have limited routined interactions with ChatGPT. Our analysis revealed that the students held static agentic orientations in their use of ChatGPT due to their limited experiences with ChatGPT and inadequate ideation for alternative ways of utilising it. Implications of this study propose the need for deliberate interventions to encourage students to have more diverse and meaningful interactions with ChatGPT.
Unveiling the dynamics of learning behaviors in learning K-12 math: An exploration of an assistments dataset
This study delves into the dynamics between diverse learning behaviors among K-12 students and their learning gains using a dataset of 508 students learning three math skills in ASSISTments. Employing K-means clustering based on students’ initial and final skill mastery alongside their engagement level, three distinct clusters emerged for each skill, revealing varying degrees of learning from ASSISTments. By analyzing decision tree classification models for each skill using affective labels such as boredom and frustration, we hypothesize that students within the same cluster of a skill may exhibit heterogeneous learning patterns that affect their subsequent learning of new skills. Further exploration demonstrates that students who transit between clusters when learning new skills differ significantly in their initial and final mastery of previously learned skills and their affective labels associated with those skills. Regression analysis underscores that students’ initial and final mastery of antecedent skills have some influence on their subsequent mastery of new skills. Unraveling the intricate relationship between student learning behaviors and the effectiveness of ASSISTments offers valuable insights into tailoring AI-enhanced educational tools, not only for learning the current skill but also for preparing for the future learning of new skills.