Extrinsic and Intrinsic for online Classroom
Bibliographic record
Abstract
The objectives of this research are 1) to examine how Thai youth in tertiary education feel about extrinsic and intrinsic rewards when studying online.2) to explore any similarities and differences between the two techniques. 3) to determine how students felt about the reward system used in this class. The samples in this research are 37 students. They are all the students who study in an online classroom for the whole semester during the COVID19 global pandemic (2019-2021).The questionnaire and the interview instruments were designed to clarify participants’ attitude and used a five point Likert scales and the entire reliability value is 0.80. The statistics used for data analysis were included descriptive statistics; and proportion and percentage, and inferential statistics such as multiple regression and Chi-square- test. The result disclose as follows : 1) The students showed that all of the four dimensions of this variables test of which one variables is extrinsic, have significant, positive relationships with satisfaction (r = .690, p < 0.01). 2) The results indicate that extrinsic and intrinsic variables had a negative effect on satisfaction (b = .051, p > 0.01), (b = .252, p > 0.01).3) the results indicate that Feelings had a positive effect on satisfaction (β = .638, p < 0.01) and could predict satisfaction variables by 56.1 percent (adjusted R2 = 0.561), and extrinsic and intrinsic variables had a negative effect on satisfaction (p > 0.01).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".