Students and instructors reflections on the impact of COVID-19 on computer science education after 1 year of remote teaching
Bibliographic record
Abstract
In May 2020, about 2 months after countless institutions across the world resorted to moving all their courses online in response to the COVID-19 pandemic, we conducted a survey to evaluate the impact of this transition on a group of computer science students. That first survey highlighted mostly negative effects, with students struggling to perform many class-related activities. About a year later, after a full year of remote teaching, we wanted to see if and how the students’ sentiment had changed. To assess students’ perceptions of remote teaching, we conducted a new survey composed of 41 multiple choice, Likert scale and open-ended questions. Additionally, we interviewed instructors of computer science courses, to learn about their experience and how they adapted to the new teaching modality. 137 students and 10 instructors shared their feedback regarding their positive and negative experiences in the new learning format. Our results show that the students’ experience improved significantly, to the point that many of them expressed interest in continuing learning online, at least partially, but some populations (e.g., early years students) may still be at a disadvantage in this learning format. At the same time, the instructors manifested concerns that this may not produce the best learning outcomes for the students. The results and considerations included in this report may benefit the conversation on how to conduct computer science higher education in a post-pandemic world.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".