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Motivation, inclusivity, and realism should drive data science education
Candace Savonen1, Carrie Wright1, Ava Hoffman1
1Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
Data science education can be more accessible to diverse communities, fostering innovation. Educators can improve teaching by focusing on motivation, inclusivity, and realism in their data science curriculum.
Area of Science:
- Data Science Education
- STEM Outreach
- Pedagogy
Background:
- Data science offers significant opportunities but faces accessibility challenges in many communities.
- Expanding data science access benefits individuals and enhances the field's innovation and impact.
- Many data science educators lack formal pedagogical training.
Purpose of the Study:
- To outline a data science teaching philosophy centered on motivation, inclusivity, and realism.
- To provide practical strategies for implementing these educational ideals in data science classrooms.
- To address the need for improved accessibility in data science education.
Main Methods:
- Summarizing a teaching philosophy based on group experiences with diverse audiences.
- Developing practical classroom implementation ideas aligned with core educational ideals.
- Iterative refinement of teaching approaches and curriculum based on effectiveness.
Main Results:
- A three-ideal framework for data science education: motivation, inclusivity, and realism.
- Practical pedagogical strategies for educators to enhance data science learning.
- Demonstrated application of these ideals across various learner groups.
Conclusions:
- Adopting a philosophy of motivation, inclusivity, and realism can significantly improve data science education.
- Practical implementation of these ideals enhances accessibility and learning outcomes.
- Continuous improvement of educational methods is crucial for effective data science training.
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