Enhancing Learning About Epidemiological Data Analysis Using R for Graduate Students in Medical Fields With Jupyter Notebook: Classroom Action Research
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
BACKGROUND: Graduate 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. OBJECTIVE: This 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. METHODS: The 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. RESULTS: Improvements 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. CONCLUSIONS: The 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,092 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle