A PRACTICAL APPROACH TO DEVELOP INFORMATION SEEKING SKILLS OF ENGINEERING STUDENTS
Bibliographic record
Abstract
During their undergraduate studies, the engineering students must develop the skills necessary to carry out proper research of information. The project-based courses give a perfect context and allow for the development of such proficiencies. The information seeking challenges offered by an engineering project is omnipresent during the entire product development and design phases. According to the phase of the process, the students may want to identify competitive products, look for solutions, find principles used in other applications that can be applied to the issue at hand, consult an expert in order to better appreciate the risks related to a concept, find parts and suppliers, consult scientific literature to better understand a phenomena and to assess a system, verify the existence of patents, ensure that a solution is conforming to the standards and regulations in effect. Nonetheless, the students are often reluctant to make the necessary efforts to effect a good research of information. Without the benefit of the senior engineer's experience, it is imperative that they proceed in a systematic manner in order to identify and look up the pertinent information. This article offers an approach that has been put in place to support engineering students during their research of information. The Guide that has been elaborated proposes a systematic approach along with research tools for each type of information required by the engineers. On the other hand, experience has shown that putting a research guide in the hands of the students is not sufficient enough for them to become skilled researchers. To reach this objective, we must insist on in-depth researches supported by an information seeking report. Without these elements of evaluation, it is not possible to substantiate that the students have developed the skills that were hoped for.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".