Year: 2025
Type: conference-paper
Abstract: In this research, we analysed exploratory behaviour trace data for students engaging in learning tasks in a technology-enhanced data analytics course as the first step towards modelling curiosity in learning.Curiosity is a complex phenomenon that is not amenable to direct modelling, but it can be understood through related behaviours like exploration, which is critical to effective learning.We analysed trace data from 40 students using visualisation and network analysis techniques, focusing on their interactions with learning tasks within the JupyterLab environment.Our analysis found that providing sufficient exploration time before explicit instruction or answer revelation, and designing learning tasks that embrace errors as opportunities, encouraged behaviours associated with curiosity-driven learning.These findings highlight the importance of designing learning environments that foster curiosity and promote active exploration.
Authors: Joe Tang, Andrew Gibson, Peter Bruza
Institution: Queensland University of Technology
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4407842604