INFORMATION LITERACY AND THE ART OF DOING RESEARCH WITHIN THE CONTEXT OF AN ENGINEERING TECHNICAL COMMUNICATION CLASS
Bibliographic record
Abstract
In this paper, we present some of the pedagogical outcomes of a study we undertook to determine whether research skills are valuable “soft skills” to have within an Engineering context, or whether they are merely “short-term competencies” as some would contend. We argue that engineering students (as future professionals) must develop two important – and confluent – skills: finding valid, complex technical information and translating it into a useful communication. As professionals, engineers must be able to find the information they need, assess it and apply it to their designs and to their communications. As students, they need to become acquainted with their professional discourse community, so we suggest that assigning a research paper in a field where applications and design exigencies are important is a worthwhile thing to do since this kind of research activity underpins students’ future roles as professional engineers and promotes their lifelong learning. In the technical communication class offered in our school of Engineering, students learn how to engage in the systematic process of finding, selecting, organizing, distilling and presenting information, a process that enhances their comprehension of the subject, develops their critical thinking and introduces them to their discourse community.
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.059 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".