Experience of distance education for project-based learning in data science.

Data science Distance education Problem-based learning Project-based learning Society 5.0

Journal

Japanese journal of statistics and data science
ISSN: 2520-8764
Titre abrégé: Jpn J Stat Data Sci
Pays: Singapore
ID NLM: 9918383078206676

Informations de publication

Date de publication:
2022
Historique:
received: 31 01 2022
revised: 07 03 2022
accepted: 17 03 2022
pubmed: 19 4 2022
medline: 19 4 2022
entrez: 18 4 2022
Statut: ppublish

Résumé

Data science plays an important role in many fields. Project-based learning is an effective teaching approach because students can learn data science practices based on real-world problems and real-world data. Because of a pandemic of COVID-19, we provided project-based learning as distance education (synchronic distance education). In this study, we explain how we developed and conducted it and provide survey results from students. The survey showed about 30% of the students found it difficult to communicate with each other and with teachers. However, it suggested that they could communicate to some extent even by remote group work. We found that, in remote communication, it is important to see the faces of all the students (and teachers) on the Zoom screen when they discuss using screen sharing. There remain some challenges such as timing to start talking and casual questions to teachers. Although some issues should be improved, distance education for project-based learning in data science can be implemented effectively. The online version contains supplementary material available at 10.1007/s42081-022-00154-2.

Identifiants

pubmed: 35434522
doi: 10.1007/s42081-022-00154-2
pii: 154
pmc: PMC8994060
doi:

Types de publication

Journal Article

Langues

eng

Pagination

757-767

Informations de copyright

© The Author(s) 2022.

Déclaration de conflit d'intérêts

Conflict of interestThe authors have no conflicts of interest to declare.

Auteurs

Kentaro Sakamaki (K)

Center for Data Science, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, 236-0027 Japan.

Masataka Taguri (M)

Department of Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.

Hiromu Nishiuchi (H)

Department of Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.

Yoshitomo Akimoto (Y)

Center for Data Science, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama, 236-0027 Japan.

Kazuyuki Koizumi (K)

Department of Data Science, Graduate School of Data Science, Yokohama City University, Yokohama, Japan.

Classifications MeSH