Developing the Smart Classroom Environment Model to Enhance Innovative Thinking and Digital Literacy
Bibliographic record
Abstract
This research aims to 1) study the current state of learning environments that promote innovative thinking and digital literacy, 2) develop the SCID Model for smart classrooms, 3) examine the model’s effectiveness, and 4) evaluate and validate the model through expert review. The study is conducted in four phases: Phase 1 involves surveying the existing learning environment; Phase 2 focuses on developing the SCID Model and research tools; Phase 3 implements the model with students; and Phase 4 evaluates and validates the model. The research sample comprises 245 faculty members from Northeast Rajabhat Universities, seven educational technology experts, seven research instrument specialists, six expert panel members, and 23 undergraduate students enrolled in the Technology and Innovation for Learning course. Research tools include a learning environment questionnaire, in-depth interviews, the SCID Model, implementation tests, assessments of innovative thinking and digital literacy, and a satisfaction survey. Data analysis employs frequency, percentage, mean, standard deviation, dependent t-tests, and content analysis for qualitative data. Results reveal that the current smart classroom environments face significant challenges in physical, psychological, social, and technological aspects, with the highest demand for improvement. The developed SCID Model comprises input factors (physical environment), a learning process (psychological, social, technological environments), outputs (digital literacy skills), and outcomes (innovative thinking skills). Implementation showed significant improvements in students’ skills at the 0.05 level, with high satisfaction. Expert validation found 33.33% confirmed model completeness, while 66.66% suggested refinements.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".