Newark, New Jersey
April 22, 2022
April 22, 2022
April 23, 2022
The van Hiele learning model of spatial abilities has been shown to effectively assess the preparedness of students learning geometry. Moreover, Force Concept Inventory (FCI) Test MRI data compiled on the neural networks of engineering students showed activation beyond the neural networks associated with regular math operations. The recently reported qualitative study of students’ verbal responses to problems on the FCI in the framework of the van Hiele learning model and Redish cognitive resources model has been expanded by our group to include a quantitative study of students’ math responses. The use of Excel Solver optimization pedagogy in introductory physics courses for engineering and algebra-proficient students during the COVID lockdown and reopening challenge was performed by our group. The selection of the optimization applications was designed to be consistent with the activation of the neural networks reported in MRI studies on engineering students, physics professors and haptic learners. The effectiveness of the optimization approach would confirm the assertion put forth in an ASEE previous presentation that engineering physics is a universal donor degree. It would also provide a means by which to implement the recommendation presented in another previous ASEE paper in which the engineering students’ conclusion was “the learning of physics being irrelevant in their third semester after completing introductory physics”. The contrast between the van Hiele learning model and Bloom’s taxonomy model on educational learning objectives in the learning of physics is discussed. The use of the spatial-numeric tool provided by Excel in terms of the plagiarism prevention and equity issues is discussed.
Dehipawala, S., & Kokkinos, D. S., & Taibu, R., & Tremberger, G., & Cheung, T. (2022, April), Excel optimization pedagogy using Van Hiele learning model of spatial abilities with Force Concept Inventory Test MRI and haptic learner data for COVID-19 online challenge Paper presented at 2022 Spring ASEE Middle Atlantic Section Conference, Newark, New Jersey. https://peer.asee.org/40053
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