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In this work, we present the design and plan of Quantum machine learning (QML) course in a computer science (CS) University program at senior undergraduate level / first year graduate level. Based on our survey, there is a lack of detailed design and assessment plan for the delivery of QML course. In this paper we have presented the QML course design with week by week details of QML concepts and hands on activities that are covered in the course. We also present how this QML course can be assessed from CS program learning outcomes perspective.
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Error Management Bias in Student Design Teams
Abstract This research examines how cognitive bias manifests in the design activities of graduate student design teams, with a particular focus on how to uncover evidence of these biases through survey-based data collection. After identifying bias in design teams, this work discusses those biases with consideration for the intent of error management, through the lens of adaptive rationality. Data was collected in one graduate level design course across nine design teams over the course of a semester-long project. Results are shown for five different types of bias: bandwagon, availability, status quo, ownership, and hindsight biases. The conclusions drawn are based on trends and statistical correlations from survey data, as well as course deliverables. This work serves as a starting point for highlighting the most common forms of bias in design teams, with the goal of developing ways in which to mitigate those biases in future work.
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- Award ID(s):
- 2207448
- PAR ID:
- 10418251
- Date Published:
- Journal Name:
- Journal of Mechanical Design
- Volume:
- 145
- ISSN:
- 1050-0472
- Page Range / eLocation ID:
- 1 to 40
- 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
- Journal Article:
- https://doi.org/10.1115/1.4055899
