ONLINE EDUCATION – ENGINEERING STUDENTS’ PERSPECTIVE
Bibliographic record
Abstract
With online learning moving into the long term, the mental and academic impacts on students arelikely to be challenging. Preliminary results obtained from three different student surveys are presented and analyzed for different cohorts of undergraduate engineering students enrolled in an engineering program at the Université de Moncton. The first survey was administered during the last week of the Winter semester, before the final exams period. This survey was administered by the Engineering Faculty and created to get an overview of students experience during their online learning sessions. Specifically, the goal of this survey was to get information on which technical tools work best for distance learning during their online sessions and to improve future online learning sessions. Another survey was completed at the end of the Fall 2020 online learning semester. About half of all engineering students completed the surveys and a preliminary analysis was conducted. Finally, a third survey was administered during the Winter 2021 online learning semester. The aim of this study is to evaluate and analyze the results of these surveys using educational data mining. This work will provide an overview of the online learning experience during the end of the Winter 2020 semester and the academic year 2020-2021 and establish relations between classroom and online learning environments. New data analysis may help to accelerate and improve future hybrid classroom-online learning pedagogy since permanent changes are expected in the near future for many engineering programs. This study shows that students vary in their abilities to adapt to this new reality. Most prefer recorded audio clipsof PowerPoint presentations beforehand combined with online synchronous learning using video conferencing software. This suggests that effective online learning requires extra time from educators to better prepare class sessions. Furthermore, there is an important correlation between the level of student motivation and their appreciation level of online learning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".