STUDENT CONCERNS FOR ENGAGEMENT IN ONLINE ACTIVE LEARNING ENVIRONMENTS DURING COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it