Innovation in Inferential Statistics Learning: Predict, Observe, and Explain (POE)-Based Teaching Materials to Optimizing Adaptive Reasoning
DOI:
https://doi.org/10.31849/64kww169Keywords:
Predict–Observe–Explain (POE) Inferential statistics Adaptive reasoning Critical thinking skills Skills in statistics, Predict–Observe–Explain (POE) , Inferential statistics , Adaptive reasoning, Critical thinking skills, Skills in statisticsAbstract
Inferential statistics is a cornerstone of higher education mathematics that equips students with essential skills for evidence-based decision-making, yet many undergraduates continue to face persistent difficulties in applying adaptive reasoning, particularly in hypothesis testing, regression analysis, and data interpretation. Conventional lecture-based and textbook-driven approaches tend to focus on computational procedures, leaving a crucial gap in cultivating reasoning and reflective thinking. Addressing this challenge, the present study developed Predict-Observe-Explain (POE)-based teaching materials explicitly designed to optimize adaptive reasoning in inferential statistics learning. Using a developmental research design guided by the Hannafin and Peck model, the study progressed through needs assessment, design, development, implementation, and continuous evaluation. Validity was established through expert reviews of content accuracy, pedagogical coherence, and presentation quality, while practicality was assessed via small- and large-group trials with undergraduate mathematics education students at IAIN Kerinci. Findings demonstrated high validity (90.75%) and acceptable practicality (77.6%), affirming the feasibility of integrating POE-based materials into undergraduate statistics instruction. By embedding prediction, observation, and explanation tasks into statistical problem-solving, the materials promoted students’ logical reasoning and improved their ability to justify conclusions. This study extends the POE approach beyond its common use in science education to abstract mathematical contexts, providing an empirically validated instructional innovation that fosters higher-order reasoning. The findings contribute to enhancing curriculum design in mathematics education and support global priorities for advancing critical thinking, problem-solving, and data literacy as central competencies for twenty-first century learning.
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