From Traditional to Computational: Teaching and Learning Statistics with Jupyter Notebook
DOI:
https://doi.org/10.26423/xw1a4c13Keywords:
statistics, learning experience, pedagogical innovation, teaching methodAbstract
Learning statistics is challenging when approached in an abstract manner and disconnected from real-world contexts. This research presents a descriptive statistic teaching experience in higher education using Jupyter Notebook with Python libraries and real-world datasets. A case study was conducted with 28 students in the Experimental Sciences, Mathematics, and Physics Education program at a public university in Ecuador. The intervention was structured into three cyclical phases (initiation, development, and closure) and implemented over 10 hours of in-person instruction spread across two weeks. Qualitative analysis of student testimonials revealed that the experience was perceived as innovative and satisfying, promoting understanding of fundamental statistical concepts, the handling of datasets, and the application of visualization techniques. The results suggest that Jupyter Notebook can serve as a relevant pedagogical tool for fostering active and contextualized statistical learning experiences, provided its use is accompanied by appropriate faculty guidance from instructors with basic proficiency in Python and in the design of technology-mediated learning environments.
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