MAKING UNDERGRADUATE RESEARCH EXPERIENCE MORE PRODUCTIVE
Bibliographic record
Abstract
Problem analysis is taught in the classroomenvironment by having students solve problems that oftenhave ready solutions. Because classroom problems areoften solvable within an hour, problem analysis, as oneCEAB (Canadian Engineering Accreditation Board)graduate attribute, may be viewed separately from othergraduate attributes. Students participating inundergraduate research, however, learn problem analysisby also developing investigative skills, use of engineeringtools (Matlab, Excel), even communication skills. In thispaper we discuss our undergraduate research experiencefrom perspectives of mentor and mentee. Mentor'smotivation to recruit undergraduate research studentscould include (i) high probability of finding talentedstudents to work on project of relatively short duration(i.e., one year) and (ii) producing solutions to a variety ofproblems that could lead to research problems. Thesemotivations align well with motivations of undergraduateresearch mentees, i.e., experience in solving morerealistic (open-ended) problems and strengthening theirresearch portfolios. Generating "real world" problemscan be achieved by introducing student-proposed designor analysis project component into a third-year course.Projects completed can then be continued into summerresearch projects. Results from the summer projects inturn enrich the third-year course content. To makeundergraduate research experience more productive,mentor encourages mentee to write and present together aconference paper, such as CEEA 2018 Conference.National-level conference experience would strengthenstudent's research portfolio. Projects with highertechnical content could even be presented to engineeringconferences, which is what we are aiming for. We discussways to increase student participation. We provide acourse project template for faculty members who areinterested in adopting our experience into their teachingand research activities.
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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 | 0.023 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.018 |
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".