Thematic Review of Canadian Engineering Education Research between 2017-2021
Bibliographic record
Abstract
Trends in engineering education have shifted over time, with teaching methods adapting to facilitate student learning [1], and the importance of equity, diversity and inclusivity (EDI) coming to the forefront [2]. It is important to understand the trends in engineering education to assess where we are coming from and where things currently stand in terms of teaching methods, culture, and pedagogy, to guide engineering educators and researchers moving forward. The purpose of this paper is to provide an overview of thematic trends in past CEEA papers over the previous five years (2017-2021), exploring shifts and evolutions in the major topics discussed as well as looking at the impact of the COVID-19 pandemic on engineering education research. Papers were analyzed from the 2017-2021 CEEA proceedings. By studying the frequency of main themes in papers for each year, the popularity of subjects that have been trending were determined, allowing for an analysis of the major trends seen year over year. During the period under review, institutions across Canada transitioned to online learning in response to the COVID-19 pandemic. This resulted in a prevalent thematic shift in paper topics towards an increased interest regarding pure online delivery of a course during the COVID-19 pandemic. Prior to the 2021 proceedings, which saw 41 (41.8%) papers discuss online learning in some form, research into this topic generally had little traction with 2017 having the next highest frequency of 17 (10.2%) publications, and 2018-2020 each having under five publications on this topic. Up until 2021, the focus on teaching beyond conventional formats had been primarily on mixed delivery (such as flipped classrooms and blended learning), as opposed to purely online. Other trends observed from the analysis include the importance of K-12 outreach with this theme seeing most focus at the CEEA 2020 conference with seven (7.9%) papers discussing this topic. In addition to the changing trends in topics, a discussion on the ambiguity of research and practice-based papers and their definition was undertaken. This analysis will assist engineering educators to understand the research topics of interest that past CEEA submissions have gravitated towards, and will highlight topics that are important, but are presently understudied.
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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 | 0.021 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.087 | 0.115 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".