FLoRA Open Data IPS: Student Self-Regulated Learning and GenAI Interaction Traces

Trace data from students using the FLoRA Engine during a two-week assignment in an Introduction to Data Science course at an Australian university. The dataset includes detailed chat logs with generative AI and comparisons between high- and low-performing students. It was authored by Xinyu Li and published on figshare in 2026.

Use Cases

• Analyzing transitions in self-regulated learning processes over a two-week period based on the described trace data.
• Studying information-seeking behaviors and query refinement patterns based on the detailed chat logs with generative AI.
• Comparing goal-setting, monitoring, and adaptive strategies between high- and low-performing student groups.
• Evaluating the influence of generative AI on academic performance and information problem-solving skills based on the voluntary user/non-user contrast.

Strengths

• Data captures self-regulated learning across multiple sessions over a two-week assignment, offering a longer observation window than typical brief-task studies.
• Includes detailed chat logs with generative AI, providing direct evidence of information-seeking behaviors linked to learning processes.
• Enables comparison between users and non-users of generative AI, allowing for evaluation of its influence.
• Dataset is 166.4 MB in size and shared under a permissive CC-BY-4.0 license.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific analytical needs.
• Data is from a single university course in Australia, which may limit generalizability.

Provenance

Source

Data collected in an Introduction to Data Science course at a university in Australia using the FLoRA Engine.

Collection Method

Comprehensive trace data collection from students during a voluntary two-week assignment.

Freshness

Last updated 2026-05-19 05:56:20.

Geography

Australia