Development, Acceptability, and Effectiveness of Interactive Instructional Materials in Statistics Education

Abstract

This study examined the development, acceptability, and effectiveness of interactive instructional materials (IIMs) in enhancing statistics education in selected higher education institutions (HEIs) in Metro Manila. A mixed-methods research design was employed, integrating quantitative and qualitative approaches. Quantitative data were collected from 256 students through structured surveys and pre-test–post-test assessments, while 30 mathematics experts evaluated the materials using standardized checklists. Qualitative data from interviews and open-ended responses provided deeper insights into user experiences.
Findings revealed that the instructional materials were rated acceptable across all domains, including objectives, content, organization, navigation, evaluation, and application, with an overall mean of 3.97. The materials were particularly commended for their clarity, alignment with learning competencies, and learner-centered design.
Students’ academic performance significantly improved, with mean scores increasing from 29.59 (Moving Towards Mastery) in the pre-test to 38.49 (Closely Approximating Mastery) in the post-test. A paired-samples t-test indicated a statistically significant difference, t(255) = 72.81, p < .001. Furthermore, the computed effect size (Cohen’s d ≈ 4.55) suggests an extremely large practical impact, indicating that the intervention produced substantial learning gains.
Qualitative findings highlighted the effectiveness of interactive and contextualized activities in enhancing engagement and comprehension. However, respondents recommended clearer step-by-step instructions and the inclusion of more real-life applications.
The study concludes that interactive instructional materials are an effective pedagogical innovation in statistics education. It recommends continuous refinement of instructional design and the adoption of experimental or quasi-experimental designs with control groups in future research to strengthen causal inferences.