Instructional Strategies to Support Creativity and Innovation in Education
Bibliographic record
Abstract
The purpose of the study focused on the instructional strategies that support creation of creative and innovative education. The sample for this study consisted of 11 experts in the field of instructional strategies that support innovation of education. Among them, five were specialists in design and development of teaching and learning, three were in technology and innovational education, and the other three were in the design and development of innovative teaching. Research instruments used in this study were three sets of interview questions designed for those specialists in their own expertise. Collected data was analyzed and categorized into key issues and themes based on literature. The results were presented through the form of descriptive analysis. The findings revealed that instructional strategies which support the creation of creative and innovative education should focus on system approach. The instructional strategies usually based on design based learning, problem solving, creative problem solving, creative thinking, research based learning, problem based learning, project based learning, science, or innovative teaching process could lead to innovative education creatively. Teaching that involves practicalities should also be focused. These instructional strategies have common elements and processes: problems in the beginning, solutions findings, testing, and evaluation. Also, using a variety of stimulating ideas to find possible solutions to the problems facilitates brainstorming and helps learners think about new ideas. Results also showed that instructional strategies using questions, classroom discussion, self-directed study, inductive and deductive thinking, media or social media make students engage students in learning activities and create innovation in learning.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".