A Comparison of Beginning and Advanced Engineering Students’ Description of Information Skills
Bibliographic record
Abstract
Abstract Objective – The purpose of this research was to examine how beginning and advanced level engineering students report use of information when completing an engineering design process. This information is important for librarians seeking to develop information literacy curricula in the context of engineering design. Methods – Researchers conducted semi-structured interviews about information strategies used in engineering design with 21 engineering students (10 first and second year; 11 senior and graduate). Researchers transcribed interviews and developed an inductive coding scheme. Then, from the coding scheme, researchers extracted broader themes. Results – Beginning level engineering students interviewed: (a) relied primarily on the parameters explicitly given in the problem statement; (b) primarily used general search strategies; (c) were documentation oriented; and (d) relied on external feedback to determine when they had found enough information. Advanced level engineering students interviewed: (a) relied on both their own knowledge and the information provided in the problem statement; (b) utilized both general and specific search strategies; (c) were application oriented; and (d) relied on self-reflection and problem requirements to determine when they had found enough information. Conclusion – Beginning level students describe information gathering as externally motivated tasks to complete, rather than activities that are important to inform their design. Advanced level students describe more personal investment in their use of information through consideration of information based on their prior knowledge and questioning information. Future research should consider how to best support beginning level engineering students’ personal engagement with information.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".