Survey of case study users during pandemic shift to remote instruction
Bibliographic record
Abstract
Before COVID-19, the use of case studies to learn science was well established in high school and postsecondary classrooms. Once the pandemic ensued, many faculty continued to use the method as a way to infuse elements of active learning remotely. The results of a survey taken by 600 faculty reveal how they accomplished this feat. Respondents to the survey found that the case method readily transferred to online learning. Most used a mixture of synchronous and asynchronous classrooms. Serious challenges were encountered, primarily due to the difficulty instructors had in keeping track of learner participation. Many obstacles were overcome by creative strategies such as using Google Forms. Some semblance of a normal classroom was achieved by using online conferencing tools and using small groups in synchronous breakout rooms. Cases were commonly broken into chunks and spread over several days. This worked especially well with cases that were already structured this way, including interrupted cases and problem-based learning exercises. Assessment of student performance largely followed the traditional path of exams, projects, and essays, although a third of the faculty attempted to evaluate participation. Classes conducted via an asynchronous approach were largely lecture based, with cases given to learners to complete as homework either individually or as groups. The greatest challenge in this setting was that answers to case questions were often readily available to learners on the internet. This was avoided by faculty modifying questions or creating their own.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".