Journal of Education Innovation and Curriculum Development

Department of English Language, Faculty of Languages and Literature College of Micronesia. P.O. Box 159, Kolonia, Pohnpe

2026-08-18T15:12:32+00:00

Virtual Reality (VR) has emerged as a promising technology for enriching history education by providing immersive representations of historical environments and supporting students’ engagement with the past. A quasi-experimental pretest-posttest control group design was employed involving 80 high school students, with 40 students assigned to an experimental group and 40 to a control group. Data were collected using instruments measuring historical empathy and contextual understanding and analyzed using descriptive statistics, paired-samples t-tests, independent-samples t-tests, ANCOVA, and Cohen’s d. The results showed that the experimental group demonstrated greater improvements in historical empathy, increasing from a mean score of 64.25 to 82.70, and contextual understanding, increasing from 62.85 to 84.10. The improvements were statistically significant for historical empathy, t(39) = 14.58, p < 0.001, and contextual understanding, t (39) = 15.89, p < 0.001. Between-group comparisons also showed significant differences in gain scores, with large to very large effect sizes. ANCOVA further confirmed significant effects of the learning condition on historical empathy and contextual understanding. Qualitative findings indicated that VR enhanced emotional connection, visualization of historical environments, perspective-taking, understanding of historical circumstances, engagement, and curiosity. The study concludes that VR can serve as an effective complementary medium for history education when integrated with historical evidence, guided inquiry, discussion, and reflection. VR does not enable students to literally experience the past but provides a mediated simulation that can support more concrete, contextual, and evidence-based interpretation of historical events.

Development and Evaluation of a Learning Analytics-Based Personalized Learning Curriculum Model for Enhancing Student Engagement

2026-08-19T13:51:24+00:00

Student engagement is an essential component of effective learning, yet conventional curriculum practices often provide relatively uniform learning experiences despite differences in students’ abilities, learning progress, interests, and learning needs. The study employed a Research and Development (R&D) design consisting of needs analysis, model design, expert validation, pilot implementation, evaluation, and model refinement. A quasi-experimental pretest-posttest control group design was used to examine the effectiveness of the developed model. The developed model was evaluated through expert validation, practicality assessment, and student engagement measurement using a five-point Likert-scale instrument covering behavioral, emotional, and cognitive engagement. The findings indicated that students demonstrated diverse learning profiles, confirming the need for differentiated and data-informed curriculum adaptation. Expert evaluation indicated that the model was sufficiently valid and feasible for implementation, while teacher and implementation feedback demonstrated its practical potential in digital or blended learning environments.

Development of a Learning Analytics-Based Adaptive Curriculum Model to Support Personalized Learning

2026-08-20T13:30:54+00:00

Personalized learning requires curriculum approaches that can respond to differences in students' abilities, learning pace, engagement, and learning needs. However, conventional curricula often provide relatively uniform learning content, activities, pacing, and assessment, making continuous individual adaptation difficult. This study aimed to develop a Learning Analytics-Based Adaptive Curriculum Model to support personalized learning. The study employed a Research and Development/Design-Based Research approach involving students, teachers, and educational experts. Learning analytics data, including assessment performance, task completion, learning activity, engagement, learning pace, error patterns, and learning progress, were analyzed to develop student learning profiles and inform curriculum adaptation. The model was evaluated through expert validation, practicality testing, classroom implementation, and effectiveness assessment. The findings showed that distinct student learning profiles could be identified and translated into differentiated learning pathways involving remediation, standard learning, and enrichment. Expert evaluation indicated that the model was valid, while teacher and student responses demonstrated its practicality for instructional implementation. The implementation also showed positive changes in learning achievement, engagement, mastery, progress, and task completion. The study concludes that integrating learning analytics into curriculum decision-making can provide a systematic mechanism for connecting student learning evidence with adaptive curriculum responses and personalized learning. The model offers practical implications for teachers, curriculum developers, schools, and educational technology developers seeking to implement more responsive and data-informed learning environments.