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Paper Type
Complete
Description
As the demand for data-driven decision making continues to rise, data science education is becoming more and more crucial. The primary aim of the study was to understand how data science education may be scaled to non-science participants, particularly using immersive DSE training sessions that culminated in a datathon (data science hackathon). The data science immersion were based on a project-based pedagogy informed by the CRoss Industry Standard Process for Data Mining (CRISP-DM) to integrate data science's transdisciplinary nature. Quantitative exploratory research was adopted in this study to answer the research questions. The study surveyed 107 datathon participants to collect the study data. Using various statistical measures including Exploratory Factor Analysis, the key results revealed that non-science participants who completed the data science immersions and datathon were sufficiently knowledgeable in all the CRISP-DM components as well as those who attended other data science programmes. This means data science skills can be attained when individuals learn and apply skills at the same time. In this case, the learning process is sped up while presenting an opportunity for other team members to learn. The study recommends data science immersions and datathon to encourage transdisciplinary collaborative learning to massify data science skills.
Paper Number
1300
Recommended Citation
Msweli, Nkosikhona Theoren; Mawela, Tendani; and Twinomurinzi, Hossana, "Massifying Data Science Education through Immersive Datathons" (2023). AMCIS 2023 Proceedings. 8.
https://aisel.aisnet.org/amcis2023/sig_ed/sig_ed/8
Massifying Data Science Education through Immersive Datathons
As the demand for data-driven decision making continues to rise, data science education is becoming more and more crucial. The primary aim of the study was to understand how data science education may be scaled to non-science participants, particularly using immersive DSE training sessions that culminated in a datathon (data science hackathon). The data science immersion were based on a project-based pedagogy informed by the CRoss Industry Standard Process for Data Mining (CRISP-DM) to integrate data science's transdisciplinary nature. Quantitative exploratory research was adopted in this study to answer the research questions. The study surveyed 107 datathon participants to collect the study data. Using various statistical measures including Exploratory Factor Analysis, the key results revealed that non-science participants who completed the data science immersions and datathon were sufficiently knowledgeable in all the CRISP-DM components as well as those who attended other data science programmes. This means data science skills can be attained when individuals learn and apply skills at the same time. In this case, the learning process is sped up while presenting an opportunity for other team members to learn. The study recommends data science immersions and datathon to encourage transdisciplinary collaborative learning to massify data science skills.
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