Implementation of Technology-Based Learning (Utilization Of Technology In Smart Digital Class and Regular Class at MA Sunniyyah Selo)
Bibliographic record
Abstract
AbstractDigitalization of education is now unavoidable. The use of digital media in learning is one way that can be done in the process of digitizing education. The aim of this research is to explore the application of technology-based learning in smart digital classes and regular MA Sunniyah Selo classes. This research uses a qualitative method with a case study approach. The case study in this research is used to compare technology-based learning in the two different classes. Observations, documentation and interviews were carried out to collect data before analysis was carried out by sorting, grouping, coding, looking for appropriate themes for later interpretation. The research results show that differences in digital media access in the learning process are not a differentiating factor in learning outcomes between smart digital classes and regular classes. Even some of the advantages of learning in smart digital classes are weaknesses in regular classes. Likewise, the advantages of the regular class are the weaknesses of the smart digital class. This shows that the effectiveness of technology-based learning in smart digital classes and regular classes influences the strategies, methods and approaches in the learning process. So that this can be used as a reference in developing technology-based learning in two different classes at once.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".