Instructional Strategies to Support Creativity and Innovation in Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it