Representing and Tracing Students’ Cognitive Processes in Project-Based Learning through the Function-Behavior-Structure Framework and Knowledge Graphs
Bibliographic record
Abstract
This research proposes a framework to represent and track students’ cognitive processes during project-based software development using the Function-Behavior-Structure (FBS) model, Knowledge Graphs (KGs), and AI-assisted analysis. The goal is to make student thinking more visible, recursive, and actionable for both learners and instructors. The system supports real-time feedback, and adaptive learning interventions by mapping student reflections, decisions, and misconceptions to evolving KGs. A prototype case study from a Computer Science Training Through Projects course illustrates how AI can automatically extract and update cognitive elements from student input, compare them to expert models, and provide targeted guidance. This approach builds on dynamic semantic network theory and leverages large language models to reduce instructor workload while improving transparency and formative assessment. Future work includes piloting the system with students to evaluate usability, precision, and impact on metacognitive development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".