Evaluating a Cloud-Based History Learning Model: A CIPP Study in Semarang Senior High Schools - Syaiful Amin; Imam Shofwan; Salman Rashid (Journal of Mathematics Instruction, Social Research and Opinion, 2026)
History education in the digital era cannot be confined to mere fact-reproduction and chronology; it must cultivate students’ historical thinking skills through source analysis, contextualization, and evidence-based reasoning. Although prior research and development (R&D) established that Public History–Based Image Cloud Recognition (ICR) media improved students’ historical thinking, that study did not appraise the model through a systematic program-evaluation framework, leaving its contextual fit, design quality, implementation process, and outcomes formally unevaluated. This study, therefore, conducts a secondary, evaluative reanalysis of that implementation using the Context–Input–Process–Product (CIPP) framework; no new primary data were collected. The reanalysis drew on the original survey, observation, interview, and pretest–post-test data gathered from two senior high schools in Semarang City, Indonesia (SMAN 6 and SMAN 12), each comprising one experimental and one control class. Quantitative differences in post-test historical thinking scores were examined with independent-sample t-tests and complemented by effect-size estimation (Cohen’s d); qualitative records were thematically analysed. The findings show that teachers used varied media but in pedagogically passive ways, while students’ baseline historical thinking was low. The cloud-based ICR media, anchored in local public-history content, produced statistically significant improvements in historical thinking, with t = 8.778 (SMAN 6) and t = 10.239 (SMAN 12), both exceeding the critical value (1.672), p < .001. The CIPP appraisal indicates strong contextual fit and systematic input design, while identifying onboarding, collaboration scaffolding, and data governance as conditions that must be met for adoption to be sustainable. As a secondary CIPP-based evaluation, the study is limited by its dependence on the original dataset and therefore reports conditional rather than conclusive evidence. The findings contribute an evaluative reframing that links contextual needs, programmatic inputs, implementation processes, and learning outcomes for cloud-based history learning.