Crafting a Responsive Teaching Framework for Data Analysis: A Phenomenological Study on Students’ Experiences in Learning Descriptive and Inferential Statistics
DOI:
https://doi.org/10.54536/jtel.v3i3.5483Keywords:
Instructional Framework, Phenomenological Study, Scaffolded Teaching, Statistics Education, Student ExperiencesAbstract
This present study employed the qualitative phenomenological approach in acquiring data regarding the lived experiences of 26 third-year BEED students at a local community college in the Philippines in learning descriptive and inferential statistics within the research coursework context. The thematic findings revealed that students find their experiences in learning statistics very much characterized by anxiety and confusion, followed by slowly developing self-confidence in understanding complicated concepts, formulas, and the usage of statistical software. Some of the common challenges faced included choosing an appropriate statistical test, interpreting the results, and applying their knowledge of statistics to real-world research issues. However, they cited several strategies used in instruction that helped them to understand most of these aspects, such as step-by-step and structured explanations, use of practical and real-life data, hands-on activities, support and patience by the instructor, visual aids, and collaborative group work. This study is grounded in Ausubel’s theory of meaningful learning, thereby stressing the connection of new knowledge to the pre-existing cognitive structures of the learner. Following from these insights, the study proceeds to propose the Scaffolded Teaching Approach to Transform Statistics Learning (S.T.A.T.S. Framework)—an innovative student-centered framework that seeks to bring in diagnostic assessment, thematic instruction, active engagement, technology-enhanced learning, and regular reflective feedback. The S.T.A.T.S. Framework is aimed at addressing the barriers to statistics education by reducing anxiety, enhancing accessibility in statistical learning, and promoting both statistical literacy and self-efficacy. The study holds implications for educators interested in molding higher education data analysis instruction into being effective, responsive, and inclusive.
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Copyright (c) 2025 Sydney Jay B. Villarin, Grace C. Lapinig, Divina Gracia C. Divino, Kemski P. Lumahang, Lily France J. Echavez
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