Work

Supporting Computational Apprenticeship Through Educational and Software Infrastructure: A Case Study in a Mathematical Oncology Research Lab

Year: 2021

Type: article

Abstract: There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although several institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remain a bottleneck. The opportunity is ripe for using education research-supported mechanisms of cross-disciplinary training at the intersection of mathematics, computation, and biology. This case study uses the computational apprenticeship theoretical framework to describe the efforts of a computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction and cross-disciplinary scientific communication.

Source: PRIMUS

DOI: 10.1080/10511970.2021.1881849

Authors: Aasakiran Madamanchi, Madison Thomas, Alejandra Magana, Randy Heiland, Paul Macklin

Language: English

Topic: Interdisciplinary Research and Collaboration

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Funders: National Science Foundation, Breast Cancer Research Foundation, Jayne Koskinas Ted Giovanis Foundation for Health and Policy, National Cancer Institute

Awards: G3119586788, G4895674666, Network for Computational Nanotechnology - Engineered nanoBIO Node