Student Engagement in Capstone Projects Through Integration of Technologies, Project Assessment and Academic Integrity
Bibliographic record
Abstract
Many technology students face challenges when it comes to executing assigned objectives in their team-based capstone projects. Problems in implementation can be due to a lack in project planning experience and the related issues involved with student confidence in light of inexperience, difficulties appreciating the many requirements involved with applied learning, including the practical skills involved, issues with developing strong team communications, or problems securing resources to bring projects to fruition. The paper will briefly present the Canadian accreditation process for technology programs and the authors' experiences in conducting and assessing a capstone course over its five year developmental span. The paper will also elaborate on the processes that enable simplification of the many elements to project development, including the establishment of effective communications and technical reporting, the process of task assignment to team members, the use of evaluative tools within the online context of the college's learning management system, and how students solve problems and manage time commitments throughout their learning process. The paper will provide a sample of the processes and assessment tools used in Lambton College's School of Technology, which awards students an advanced technology diploma degree.
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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 | 0.028 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".