Adapting to Change: A Study of Assessment Practices in Engineering Education Amidst Major Disruptions
Bibliographic record
Abstract
Post secondary education experienced two major disruptions in the past five years that forced us to change the way we do our jobs. First the pandemic shifted us to remote delivery, and then generative AI was released to the public. Many academic institutions provided some form of guidance on how to handle these challenges in hopes that the teaching and learning processes would not be significantly impacted. Both disruptions forced educators to rethink the evaluation and assessment practices used within our programs, as noted in a recent policy review that identified the need to adopt ‘multifaceted evaluation strategies’. It is unclear, however, how, or if, the engineering education community responded to this need. This qualitative systematic review answers ‘What assessment and evaluation strategies are reported in the engineering education and assessment and evaluation literature from 2020 (beginning of the pandemic) until the end of 2024 (two years of generative AI availability)?’ Examined through the lens of the Macro-Meso-Micro (3M) framework for educational change, analysis of 35 conference and journal papers indicate a shift in assessment strategies, techniques, and methods from a focus on content to learning, a move to more continuous and formative assessment, and a choice of assessment practices that meet the evaluation and assessment needs of multiple levels of the 3M framework
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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 | 0.114 | 0.228 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".