Using Positive Visual Stimuli to Lighten The Online Learning Experience through In Class Questioning
Bibliographic record
Abstract
Using in-class questions is an efficient instructional strategy to keep abreast of the state of student learning in a class. Some studies have found that discussing in-class questions in synchronous learning is helpful. These studies demonstrated that synchronous questions not only provide students with timely feedback, but also allow teachers to change the pedagogy adaptively. However, some studies have also shown negative results of synchronous questions in that students may resist being questioned because of anxiety. Therefore, this paper proposes an idea of showing students funny images in order to reward them for providing correct answers. The effect of connecting questions with funny image rewards was examined by collecting data on student test scores, on facial expressions and on electroencephalogram (EEG) responses elicited using this strategy. The data on students' facial expressions indicated that being presented with funny images for correct answers consistently helps to arouse positive emotions in participants. Also, the data on the EEG responses showed that the participants receiving the rewarded questions demonstrated a trend toward increasing levels of attention and relaxation. However, the results also revealed that significant improvements in test scores were not apparent regardless of whether or not amusing visual stimuli were used. The findings imply that showing funny images as a stimulus enhances students' affective states in student-teacher interactions during online learning activities.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".