Data-Driven Assessment of Cultural Sensitivity among BA History Students: Implications for Technology-Supported Digital Cultural Heritage Education
DOI:
https://doi.org/10.70917/ijcisim-2026-5300Keywords:
Cultural sensitivity, data-driven assessment, cultural heritage education, digital cultural heritage, educational technology, learner analyticsAbstract
Cultural sensitivity is relevant to higher education in settings where students encounter diverse cultural identities, traditions, and heritage. This study assessed the level of Kalinga cultural sensitivity among 91 Bachelor of Arts in History students at Kalinga State University during School Year 2016–2017. Cultural sensitivity was examined across personal, behavioral, and intellectual dimensions. The study used a structured questionnaire with a pilot-test reliability coefficient of 0.82 and conducted informal interviews with selected respondents to provide contextual interpretation of the quantitative findings. Frequency, percentage, and weighted mean were used in the analysis. The respondents obtained an overall weighted mean of 3.64, interpreted as Much Sensitive. The intellectual dimension obtained the highest mean (4.22), followed by the behavioral dimension (approximately 3.57), while the personal dimension obtained the lowest mean (3.13). Within the personal dimension, comparatively lower ratings were observed for appreciation of local songs and music from other cultures (1.82) and preference for wearing native attire (1.94). The findings indicate that positive recognition of cultural diversity does not necessarily correspond to equally strong engagement with selected traditional cultural expressions. From a technology-oriented perspective, these assessment patterns may inform the development of culturally responsive digital learning resources, including digital storytelling, multimedia cultural archives, and interactive cultural heritage materials. The study does not develop or evaluate a digital cultural heritage system; rather, it provides an empirical baseline that may inform future technology-supported cultural heritage education and subsequent research involving larger datasets and learning analytics.