Replacing Final Exams with Open-Ended Course Projects in Engineering Education
Bibliographic record
Abstract
Over the last twenty years, assessment methods in Engineering education have shifted to focus on evaluating desired learning outcomes. Both Mills and Treagust (2003) and Olds, Moskal, and Miller (2005) credit the paradigm shift to accreditation procedures that report program inputs and document achievement of learning objectives. High-stakes final exams have been, and still are, widely used in Engineering education as the primary means to evaluate student learning (Flores, Veiga Simão, Barros, & Pereira, 2015). Although considered objective and efficient for large class sizes, Knight (2002) points to shortcomings associated with final exams including ineffectiveness at evaluating certain types of outcomes and a distorting effect on the taught curriculum. However, overcoming these shortcomings is possible through project-based learning and open-ended course projects. Project-based learning is a form of experiential learning that gives students the opportunity to apply theoretical concepts while developing higher-order skills (e.g., critical thinking, synthesis, and evaluation) and soft-skills (e.g., communication, management, and teamwork; Mills & Treagust, 2003). Based on three different experiences with large-scale open-ended projects, Daniels, Faulkner, and Newman (2002) conclude that the use of course projects enhances student learning while better preparing them for their future careers. Flores et al.’s (2015) findings support this notion by demonstrating that students perceive assessment methods that require active involvement as more fair and effective. This workshop aims to increase awareness around the importance of assessment and highlight that high-stakes final exams, although widely used, have a number of flaws that may bias evaluation and impact student learning. The workshop’s main goal is to introduce project-based learning as an alternative to final exams and develop skills to identify where and how instructors can use open-ended course projects effectively.
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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 | 0.072 | 0.227 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".