A Multi-dimensional Model for the Representation of Learning through Service Activities in Engineering
Bibliographic record
Abstract
Abstract A Model for Learning through Service in EngineeringThe Engineering Faculty Engagement in Learning through Service (EFELTS) project wasestablished with a key objective to identify the impact learning through service (LTS) has onfaculty and determines how to encourage faculty to adopt this instructional method. During arecent gathering of engineering instructors involved in LTS programs a group was tasked withdeveloping a model for characterizing LTS programs in engineering. Our group formulated amodel which characterizes 12 dimensions of LTS programs. This model provides a basis forcomparing and contrasting programs. In addition, it can be used as a check list for developingnew LTS programs, evolving existing LTS programs, or assessing the quality of an LTSprogram.The dimensions are formulated to capture the qualities of LTS programs that occur across a widebreadth of engineering institutions. As such the dimensions need to encompass the broad varietyof program designs that are currently occurring as well as take into account future developmentsin this pedagogy. The dimensions fall into 4 key categories: Academic, Program Design,Technical Social Balance, and Management. These dimensions are described in detail and theends of the spectrum in each dimension are defined and illustrated.The paper discusses application of the model in depth and characterizes some example programsfrom our institutions. The results are used as a basis for comparing and contrasting the programdesigns.LTS programs are becoming more common in engineering schools. They offer an opportunityfor our students to not only strengthen their engineering abilities but also achieve learningoutcomes that go beyond what can be learned in a traditional engineering course. There aremany different, successful examples of LTS. Defining examples using the proposed model mayhelp faculty new to this pedagogy design a program that would be viable at their institution. Inaddition, this model can be used to characterize and assess existing programs. The goal is tocreate a model that advances this valuable pedagogical method.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".