Curriculum-Level Innovation in History Education: Developing a Technology-Integrated and Contextually Adaptive Model for Senior High Schools in Indonesia
Bibliographic record
Abstract
This study aimed to develop and evaluate a digital-based history curriculum tailored for Indonesian senior high schools, addressing the pedagogical gap between traditional history instruction and 21st-century learning demands. Employing a Research and Development (R&D) methodology guided by the ADDIE model, the research involved five curriculum and pedagogy experts, ten history teachers, and over 200 students from six public high schools with varying levels of technological infrastructure. The study encompassed a comprehensive needs analysis, expert and teacher validation, guided classroom implementation, and multi-source impact evaluation using interviews, observations, formative assessments, and digital questionnaires. The curriculum was validated with high scores by experts (mean = 4.80) and teachers (mean = 4.60), highlighting its structural coherence, relevance, and ease of implementation. Classroom trials across schools demonstrated consistent effectiveness (mean = 4.60), with no significant variance found through ANOVA analysis (p > 0.05). Qualitative results indicated increased student engagement, deeper historical understanding, and development of contextual thinking. The study offers a novel approach to system-level curriculum design where digital media is the backbone of instructional planning. This model contributes to global discourse by presenting a scalable, equitable, and pedagogically resilient solution for integrating digital history education in diverse and resource-limited settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".