Information-seeking Behavior Among First-year Engineering Students and the Impacts of Pedagogical Intervention
Bibliographic record
Abstract
Abstract ABET accreditation criteria specifically require engineering students to demonstrate the ability to research broad contexts informing engineering problems and emerging technical developments to facilitate solutions. Such research fundamentally underpins engineering students’ ability to succeed in design projects, and this research is even more crucial to their professional activity. As a result, engineering programs incorporate diverse interventions to professionalize engineering students’ information-seeking approaches to filtering out unreliable sources and identifying credible ones. Programs generally assume that these approaches equip students for the information needs of engineering careers. However, relatively little is actually known about what first-year engineering students do when they seek information and whether interventions have any effect on this behavior. Recent research has demonstrated that professional engineers rely on professional networks and non-scholarly media for their information, and then invest heavily in resource evaluation to verify information and mitigate risks of error. Some emerging research suggests that engineering students may use similar strategies to seek information, but there is a lack of consensus about why first-year engineering students select certain sources and how they evaluate the credibility of these sources. To address this gap in understanding, we conducted a multi-stage reflective case study of 289 first-year engineering students’ information-seeking behavior in a mandatory engineering-communications course. We established the students’ baseline information-seeking behavior in a pre-research task, an engineering librarian conducted a workshop to teach engineering research practices, students submitted a design project with evidence of their research activities, and then we evaluated the extent to which course interventions had any impact on the students’ information-seeking behavior. Our results have significant implications for the design of engineering-design and engineering-communications courses in Canadian and U.S. engineering programs to meet the intent of accreditation criteria and the demands of engineering industry.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".