Effective Ways of Enhancing the Quality of Question Generating and Spontaneous Information Search Outside the Classroom
Bibliographic record
Abstract
This study examined how to enhance the quality of students’ question generating and to encourage their spontaneous information searches after classroom instruction in university. The teacher assigned One Minute Paper as homework, and students answered three questions; “Q1: What was the most important thing that you learned today?”, “Q2: What important question remains unanswered?”, and “Q3: What information did you search for after the classroom instruction?”. While it was necessary to answer Q1 and Q2 for submission, answering Q3 was not necessary and they could answer it if they wished to do so. The teacher, however, realized that some students were not generating questions actively and the quality of their questions were not adequately improved. After 7 weeks, he changed his intervention and gave feedback on some students’ questions to enhance their question quantity and quality. The latent growth curve modelling showed that question quality, spontaneous searching behaviour, and the link between question generation and conducting searches increased after the intervention change. The result also showed that post-intervention change slopes were larger for the feedback group than the class without feedback. The results indicate that besides assigning homework tasks, it is also important to connect learning outside along with inside the classroom to enhance question quality and encourage spontaneous information search.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".