SLICING AND DICING COMMUNITY ENGAGED LEARNING IN ENGINEERING EDUCATION
Bibliographic record
Abstract
Community engaged learning (community engagement or service learning) is known to be an effective pedagogy to develop social responsibility and many engineering graduate attributes (outcomes). However, as community engaged learning is a pedagogy still establishing itself in engineering education the scope and boundaries are stillbeing defined.Studies that report on implementation of community engaged learning have sometimes been characterized as anecdotal and isolated. Before we increase focus on work that measures impact and suggests strategic use ofcommunity engaged learning pedagogy – we must begin to tie down the scope, terminology and types of community engaged learning to ensure that a cohesive body of knowledge is formed.This paper is largely a literature review of community engaged learning and how categorizing has been approached. The purpose of this paper is to call attention for the need of more systemized reporting of community engaged learning. In our review, we find that there are two general strategies for distinguishing one type of community engaged learning type from another. In a collaborative spirit, we use the merits of both pproaches to categorizing community engaged learning to conduct a thought- experiment towards finding a middle ground for conventions when reporting community engaged learning experiences.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".