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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".