Teaching First-year Engineering in an Online Learning Environment
Bibliographic record
Abstract
Teaching models in face-to-face classes have evolved over time with goals to maximize student learning and use techniques such as problem and project based, experiential, active and discovery learning to name a few. Mastery of these techniques requires an instructor to be knowledgeable and proficient with different media (e.g. whiteboard, projector, demonstration equipment, feedback tools, communication tools, and learning management systems) while teaching and assessing students. In addition, instructors must also be experts in their own disciplines. When using different types of delivery methods (face-to-face, blended, or fully online) it is important to ensure that alternatives exist in all methods to accommodate and enhance learning. The recent Pandemic has caused a rapid transition to online teaching without time to adjust teaching methodologies. This paper compares the use of face-to-face and online teaching methodologies in some first-year engineering classes. Conclusions are then made on opportunities to improve teaching and learning in an online environment.
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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 | 0.001 |
| 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".