Project Description

The School of Engineering Education and Department of Computer Information Technology at Purdue seek a postdoctoral fellow to study how undergraduate students develop self-regulated learning skills to: a) apply AI algorithms for computers to learn from data, and b) prevent potential biases in the data used to train AI algorithms, among other ethical considerations. The proposed research is grounded in constructivist learning theories, including the computational cognitive apprenticeship (CCA) framework. Data collection and analysis for this project will involve mixed-methods approaches, including through use of surveys and interviews to investigate student understandings and perceptions of technical/disciplinary knowledge (e.g., AI algorithms and techniques) and associated social and ethical considerations. One central objective of this study is to develop new instructional materials and strategies that improve students’ conceptual understanding of machine learning techniques while also increasing their awareness of how such technologies both shape and are shaped by the social contexts where they are developed and used. In addition to the core research aims outlined here, the fellow will have opportunities to relate research to instructional practice by helping to pilot and assess educational interventions, and engage with the broader policy and outreach dimensions of this research through the National Institute for Engineering Ethics (NIEE).

Start Date

Spring, Summer or Fall 2024

Postdoc Qualifications

Minimum Qualifications: 1) Ph.D. in Computer Science, Computer Engineering, Engineering or Computer Education, Learning Sciences, or closely related field, 2) Programming expertise and ability to learn data science and machine learning skills, 3) familiarity or interest in the societal and ethical implications of AI, and 4) strong interest in discipline-based education research.

Desired Qualifications: Experience with data analytics; Experience with AI (technical and/or societal aspects); Experience with learning analytics; Experience with qualitative and quantitative methods; Experience writing and publishing in peer-reviewed journals; Ability to work on collaborative teams across multiple disciplines; Ability to communicate effectively.

Co-Advisors

Brent K. Jesiek (bjesiek@purdue.edu), Professor, Engineering Education and Electrical and Computer Engineering, https://web.ics.purdue.edu/~bjesiek/

Alejandra Magana (admagana@purdue.edu), Professor, Computer and Information Technology, https://polytechnic.purdue.edu/rocketed

Short Bibliography

Fennell, H. W., Lyon, J. A., Madamanchi, A., & Magana, A. J. (2020). Toward computational apprenticeship: Bringing a constructivist agenda to computational pedagogy. Journal of Engineering Education, 109(2), 170-176. https://doi.org/10.1002/jee.20316

Howland, S. J., Claussen, S., Jesiek, B. K., & Zoltowski, C. B. (2022). Influences on U.S. undergraduate engineering students’ perceptions of ethics and social responsibility: Findings from a longitudinal study, Australasian Journal of Engineering Education, 27:2, 88-99. https://doi.org/10.1080/22054952.2022.2154009

Magana, A. J. (in press). Teaching and learning in STEM with computation, modeling, and simulation practices: A guide for practitioners and researchers. Purdue University Press.

Sanchez-Pena, M., Vieira C., & Magana, A. J. (2022). Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding. Computer Applications in Engineering Education. http://doi.org/10.1002/cae.22580

Zhu, Q., & Jesiek, B. K. (2017). A pragmatic approach to ethical decision-making in engineering practice: Characteristics, evaluation criteria, and implications for instruction and assessment. Science and Engineering Ethics, 23(3): 663-679. https://doi.org/10.1007/s11948-016-9826-6