“There is No Single Right Answer”: The Potential for Active Learning Classrooms to Facilitate Actively Open-minded Thinking
Bibliographic record
Abstract
Education is meant to open your mind, but is that what universities are really doing? Rather than fostering open-minded thinking, the format of lecturing, the lack of interaction among students and instructors, and the passive nature of learning are likely producing the opposite, students with closed-minds. The development and implementation of Active Learning Classrooms (ALC) has the capability to counteract this negative trend by providing a configuration suited for more collaborative learning and opportunities for students to share their thoughts, hear other perspectives from peers, and have the potential to become more open-minded. A description of a study on students in a fourth year psychology course is provided in which the instructor changed her course in order to use the ALC to its fullest capacity. Students were also given an Actively Open-minded Thinking questionnaire (Stanovich & West, 1997) pre and post course, with results indicating that open-minded thinking increased over the term. Although there are many components that could contribute to this result, the impact that educational spaces may have on student learning are discussed.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".