What Can Students’ Bibliographies Tell Us?- Evidence Based Information Skills Teaching for Engineering Students
Bibliographic record
Abstract
Objective - This project sought to identify students’ strengths and weaknesses in locating, retrieving, and citing information in order to deliver information skills workshops more effectively. Methods - Bibliographies submitted from first-year engineering and second- and fourth-year chemical engineering students’ project reports were analysed for the number of items cited, the variety of items cited, and the correct use of citation style. The topics of the project reports were also reviewed to see the relationships between the topics and the items cited. Results - The results show that upper level students cited more items in total than did lower level students in their bibliographies. Second- and fourth-year engineering students cited more books and journal articles than first-year students cited. Web sites were used extensively by all three groups of students, and for some first-year students these were the most frequently used sources. Students from all three groups had difficulties with citation style. Conclusion - There was a clear difference in citation frequency between upper and lower level engineering students. Different strategies of information skills instruction are needed for different levels of students. Librarians and department faculty members need to include good quality Internet resources in their teaching and to change the emphasis from finding information to finding, interpreting, and citing accurately.
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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 | 0.010 | 0.162 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".