An Experiential Engineering Learning Model for Knowledge and State of Flow Creation
Bibliographic record
Abstract
Education in professional degree programs is charged with serious responsibilities in the classroom and practice spaces. To meet these responsibilities, educators must serve as both teachers and learners in both spaces. This article demonstrates an experiential project-based learning model to enhance the teaching of an undergraduate engineering course on mechatronics. An important aspect of this model is an experiential learning model that complements the well-known international CDIO™ Initiative which is an innovative educational framework for producing the next generation of engineers. This model reflects on challenges of experiential learning for group-based design projects and faculty competition teams where learners including faculty and students collaborate to create their community of design and practice to physically and virtually share knowledge, perspectives, and opinions. The model reveals the impact of collaboration, practice spaces and exhibitions, and open educational resources in the enhancement of engineering education. The experience of adopting the model for several years recommends a number of practical approaches instructors may embrace to enable knowledge creation and enhance the effect of flow experience. Keywords: experiential learning, knowledge creation, reflective practice, state of flow, community of design and practice, open educational resource
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 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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".