Instructional Strategies to Produce Educational Media Systematically
Bibliographic record
Abstract
The main research purpose focused on investigating the instructional strategies to produce educational media systematically. The qualitative research methods were conducted by in-depth interviews with the experts and undergraduate students on effectively designing these instructional strategies. The participants consisted of two groups; 1) nine experts in the field of instructional strategies. 2) twelve undergraduate students. Research instruments were two semi-structured interviews with open questions; 1) two sets of interview questions designed for those specialists, and 2) the interview questions designed for an excellent student. Collected data was analyzed and categorized into key issues. The results were presented in descriptive analysis. The findings revealed as following: 1) two main popular instructional media types as follows; 1.1) digital media and 1.2) handmade media 2) the instructional strategies as follows: 2.1) ADDIE included analysis, design, development, implementation, and evaluation. 2.2) 3P included pre-production, production, post-production 2.3) project-based learning 2.4) design-based learning, and 2.5) creative-based learning. 3) teaching techniques and methods encourage students such as case studies, best practices, creative practice, questions, discussion, brainstorming, team-based/group, etc. 4) organized activities to encourage students with active learning, creative knowledge, and instructional media systematically. 5) online tools and new technology could be active learning tools and motivate learners to create new media. Moreover, learning engagement, social media, and immediate feedback could engage students efficiently. 6) media evaluations should be evaluated on the design, production processes, and results. 7) the crucial factors consist of 1) instructional strategies/teaching techniques 2) teachers 3) students 5) materials, equipment, and supporting tools 6) learning environments 7) the work process. 8) the limitations are as follows; 1) teachers 2) students 3) time-limited 4) budget-limited.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".