Supporting Computational Apprenticeship Through Educational and Software Infrastructure: A Case Study in a...

Area of Science:

• Computational biology
• Mathematical biology
• Interdisciplinary life sciences

Background:

• Growing need for quantitative approaches in life sciences.
• Communication and education gaps hinder multidisciplinary research.
• Opportunity for education research-supported cross-disciplinary training.

Purpose of the Study:

• To describe efforts in prototyping and refining a mentorship infrastructure for undergraduate research experiences.
• To evaluate the utility of the computational apprenticeship framework.
• To explore implications for undergraduate instruction and scientific communication.

Main Methods:

• Case study approach.
• Utilized the computational apprenticeship theoretical framework.
• Focused on a computational biology lab's mentorship program.

Main Results:

• The computational apprenticeship framework effectively supported both computational/math students in biology and biologists in computational methods.
• Identified challenges, benefits, and lessons learned in developing the mentorship infrastructure.
• Demonstrated the framework's utility in fostering cross-disciplinary learning.

Conclusions:

• The computational apprenticeship model is a viable mechanism for cross-disciplinary training in the life sciences.
• Effective mentorship infrastructure can overcome educational bottlenecks.
• Implications for undergraduate education and scientific communication are significant.