Student Response to the Integration of Online Education in High School Physics Classrooms: A Case Study
Bibliographic record
Abstract
In response to the COVID-19 pandemic, high school classrooms in the province of Quebec had to switch to a half-in-person and half-online attendance, reducing in-person class sizes to follow the physical distancing requirements. With very few studies examining this education model, this study aims to investigate the teacher and student response, as well as observe its impact on student engagement within the high school physics classroom. Ten students in the same Grade 11 physics classroom participated in this case study. Two surveys, completed at the beginning and end of the 2020 fall semester, respectively, were used to evaluate the progression of the student’s physics interest, study habits, preferred learning methods and engagement in online and in-person settings over the course of the semester. Additionally, the instructor and five students volunteered to be interviewed. These interviews provided a deeper understanding of the survey data, as well as insight on the student’s emotional response to their new classroom setting. Results indicated that while the majority of the participants preferred to attend classes in-person, the half/half model was highly ranked, as a significant portion of the students mentioned its advantages. Technical difficulties, isolation and the increased workload were the main reasons for the lower motivation to attend school online. However, most students enjoyed the increased efficiency, schedule flexibility, comfort, and the ability to simultaneously cooperate with peers during teacher-led lectures when attending classes online. The students’ motivation to take the course, and their overall satisfaction and criticism of the course was found to be independent of the classroom setting and of the students’ main learning type – be it visual, auditory or kinetic.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".