Effects of the Instructional Model Based on Creative Problem-Solving Principles with Social Media to Promote the Creation of Educational Innovation for Pre-service Teachers
Bibliographic record
Abstract
The main research purpose focused on the effects of conducting the instructional model based on the principles of creative problem solving with social media to promote the creation of educational innovation for pre-service teachers. The participants consisted of twelve pre-service teachers. Research instruments were 1) the instructional model based on the principles of creative problem solving with social media, 2) the test of knowledge and creation of educational innovation, 3) the creation of educational innovation’s evaluation form, and 4) the questionnaires’ conducting this instructional model. Collected data were analyzed with statistics and categorized into key issues based on literature. The results were presented through the form of Shapiro-Wilk, Wilcoxon signed-ranks test, arithmetic mean, standard deviation, and descriptive analysis. The research findings were presented as follows: 1) the effects of conducting the instructional model that was conducted sixteen weeks on the course 0537211 Innovation in Educational Technology and Communications in the first semester of 2020. The research hypothesizes were followed the established as follows; 1.1) the pre-service teachers had post-test scores’ the knowledge and creation of educational innovation higher than pre-test with statistical significance at the .01 level. 1.2) they had post-learning scores for creating educational innovations’ processes at the overall excellent level (M = 92.83, S.D. = 11.78), and their educational innovations were be post-learning at the overall good level (M = 48.33, S.D. = 7.45) 2) the opinions’ pre-service teachers toward conducting this instructional model that they have positive opinions to this conduct at the overall excellent level (M = 4.92, S.D. = 0.25).
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".