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Abstract Diversity, equity, and inclusion (DEI) are interconnected with bioengineering, yet have historically been absent from accreditation standards and curricula. Toward educating DEI-competent bioengineers and meeting evolving accreditation requirements, we took a program-level approach to incorporate, catalog, and assess DEI content through the bioengineering undergraduate program. To support instructors in adding DEI content and inclusive pedagogy, our team developed a DEI planning worksheet and surveyed instructors pre- and post-course. Over the academic year, 74% of instructors responded. Of responding instructors, 91% described at least one DEI curricular content improvement, and 88% incorporated at least one new inclusive pedagogical approach. Based on the curricular adjustments reported by instructors, we grouped the bioengineering-related DEI content into five DEI competency categories: bioethics, inclusive design, inclusive scholarship, inclusive professionalism, and systemic inequality. To assess the DEI content incorporation, we employed direct assessment via course assignments, end-of-module student surveys, end-of-term course evaluations, and an end-of-year program review. When asked how much their experience in the program helped them develop specific DEI competencies, students reported a relatively high average of 3.79 (scale of 1 = “not at all” to 5 = “very much”). Additionally, based on student performance in course assignments and other student feedback, we found that instructors were able to effectively incorporate DEI content into a wide variety of courses. We offer this framework and lessons learned to be adopted by programs similarly motivated to train DEI-competent engineering professionals and provide an equitable, inclusive education.
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Who Tests the Testers? Avoiding the Perils of Automated Testing
Instructors routinely use automated assessment methods to evalu- ate the semantic qualities of student implementations and, some- times, test suites. In this work, we distill a variety of automated assessment methods in the literature down to a pair of assessment models. We identify pathological assessment outcomes in each model that point to underlying methodological flaws. These the- oretical flaws broadly threaten the validity of the techniques, and we actually observe them in multiple assignments of an introduc- tory programming course. We propose adjustments that remedy these flaws and then demonstrate, on these same assignments, that our interventions improve the accuracy of assessment. We believe that with these adjustments, instructors can greatly improve the accuracy of automated assessment.
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- Award ID(s):
- 1714431
- PAR ID:
- 10093712
- Date Published:
- Journal Name:
- Proceedings of the 2018 ACM Conference on International Computing Education Research
- Page Range / eLocation ID:
- 51 to 59
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3230977.3230999
