STUDENT CONCERNS FOR ENGAGEMENT IN ONLINE ACTIVE LEARNING ENVIRONMENTS DURING COVID-19
Bibliographic record
Abstract
This paper shares a summary of the self-reported concerns of 134 first-year engineering students around engagement in online active learning environments during COVID-19. The students had volunteered to participate in remote weekly problem-solving workshops for four weeks that utilized Active Learning techniques. In this paper, we specifically analyze samples from the students who participated in only one workshop and responded to the following question: What concerns do you have that might limit your ability to engage in online active learning environments? Twenty of the participants reported no concerns. The tone of each student's response and personal feelings reported were also analyzed. Then, a thematic analysis of each student response was made, with the transcription and coding agreement being performed by two coders. As expected, most of the students expressed their concerns in a negative or neutral tone, and only a few expressed an affinity for current educational settings. Word mining of feeling terms shows that more students had verbalized being disengaged, followed by distracted and uncomfortable and none communicated a positive feeling. Our thematic analysis showed that learning socially (72/114, or 63%) is the most pressing concern for the students, followed by more personal regulating factors such as attitude and motivation (44%), quality of physical and virtual study environment (40%), as well as the guidance received from the course administrators (24%). Findings suggest the need for developing a global understanding of what active learning in an online environment entails in the context of engineering education, and to develop and adjust tools and practices to help students learn in this new context.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".