Exploring Concept Maps as Study Tools in a First Year Engineering Biology Course: A Case Study
Bibliographic record
Abstract
Concept maps are metacognitive study tools created and used by learners as reference maps describing relationships between concepts and specific domains. The purpose of this study was to investigate any correlation between the quality of concept maps and the mark distributions in a first-year engineering biology course. Major concepts of the course included prokaryotic and eukaryotic cell structure and composition, metabolic pathways, cell transport, genetic engineering and growth kinetics. Students were asked to develop concept maps and were allowed to consult their maps in a portion of the final exam. Maps were assigned a qualitative grouping of 1 (incomplete, preliminary map) or 2 (complete map) and were associated with final exam grades to compare the effectiveness of the concept maps. Students who provided complete concept maps had significantly higher ‘open book’ portion grades (p < 0.0001) and overall final exam grades (p < 0.0001) than students who handed in preliminary maps. The quality of the concept map was positively correlated to student performance in questions requiring conceptual skills as well as in the overall final exam grade.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".