A Learning Style Comparison between Synchronous Online and Face-to-Face Engineering Graphics Instruction
Bibliographic record
Abstract
The implementation of a successful engineering program to a synchronous online curriculum is subject to many impacting factors. One such factor, that has not seen much investigation, concerns learning styles. Student learning styles may have a dramatic influence on the success of a synchronous online deliverable engineering graphics curriculum. The immediate objective of this research was to look at the effectiveness of teaching Engineering Graphics with a synchronous online delivery method and to compare it to a more traditional face-to-face delivery method. Using Kolb’s learning style inventory, student learning styles in both educational settings were investigated and analyzed to discover the student population’s prevailing learning style. Data relating to class success was collected with surveys, personal feedback, and by observing overall student performance based on grades and responses to the survey material presented. The study targeted 6 separate sections of an engineering graphics course taught by the same instructor, in the same physical setting, and with identical curricula over a two-year period. Data analysis allowed for an introspective look into correlations between academic success and the learning styles of the students. Findings suggest that (1) Converger students receive significantly higher final course grades when they are in a synchronous online environment; (2) Assimilator and Converger synchronous online students show significant improved differences in their final open-ended project scores over their face-to-face taught peers, the prevalent learning style within the course. Suggestions to accommodate learning styles are present.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".