Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
New Engineering Educators Division (NEE)
19
10.18260/1-2--42782
https://peer.asee.org/42782
206
Yanjun Yan is an Associate Professor in Engineering and Technology at Western Carolina University. Her research interests include engineering education, swarm robotics, statistical signal processing, and swarm intelligence.
In active learning, similar problems with random parameterization assigned to different students effectively encourage independent work while inhibiting plagiarism. Pearson’s Mastering platform and McGraw Hill’s Connect platform are the leading competitors on the market to provide online customized problem definitions with randomized parameterization. The author used Mastering in a class in Fall 2019 and Connect in another class in Spring 2020, as a trial for free (and the author was grateful for the publishers’ support). The students commented that these platforms were helpful, but their price tags were prohibitive. Therefore, the author used Blackboard to create similar problems in Fall 2020 and Spring 2021, skipped Fall 2021 given her sabbatical leave, and switched to Canvas (due to campus-wide adoption of Canvas to replace Blackboard) to do the same in Spring 2022 and Fall 2022. It takes time to set up such enhanced multi-part problems on Blackboard or Canvas, but these problem sets are reusable, and the student responses are positive. This paper describes how to create such multi-part problems with random parameterization on Blackboard and Canvas, and presents the evolvement of student perceptions from Fall 2019 to Fall 2022, to reflect on the impact of the pandemic.
Yan, Y. (2023, June), Create Multi-Part Problems with Random Parameterization on Blackboard and Canvas Similar to “Mastering” and “Connect” Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42782
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