ArticleJMIR medical education2023
Enhancing Learning About Epidemiological Data Analysis Using R for Graduate Students in Medical Fields With Jupyter Notebook: Classroom Action Research.
Article in JMIR medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 3 citations in OpenAlex.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGraduate students in medical fields must learn about epidemiology and data analysis to conduct their research. R is a software environment used to develop and run packages for statistical analysis; it can be challenging for students to learn because of compatibility with their computers and problems with package installations. Jupyter Notebook was used to run R, which enhanced the graduate students' ability to learn epidemiological data analysis by providing an interactive and collaborative environment that allows for more efficient and effective learning.
objectiveThis study collected class reflections from students and their lecturer in the class "Longitudinal Data Analysis Using R," identified problems that occurred, and illustrated how Jupyter Notebook can solve those problems.
methodsThe researcher analyzed issues encountered in the previous class and devised solutions using Jupyter Notebook. These solutions were then implemented and applied to a new group of students. Reflections from the students were regularly collected and documented in an electronic form. The comments were then thematically analyzed and compared to those of the prior cohort.
resultsImprovements that were identified included the ease of using Jupyter R for data analysis without needing to install packages, increased student questioning due to curiosity, and students having the ability to immediately use all code functions. After using Jupyter Notebook, the lecturer could stimulate interest more effectively and challenge students. Furthermore, they highlighted that students responded to questions. The student feedback shows that learning R with Jupyter Notebook was effective in stimulating their interest. Based on the feedback received, it can be inferred that using Jupyter Notebook to learn R is an effective approach for equipping students with an all-encompassing comprehension of longitudinal data analysis.
conclusionsThe use of Jupyter Notebook can improve graduate students' learning experience for epidemiological data analysis by providing an interactive and collaborative environment that is not affected by compatibility issues with different operating systems and computers.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.