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Impact of Flipped Classroom Model on High-workload and Low-income Students in Upper-division Computer Science

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Computing and Information Technology Division Technical Session 3

Tagged Division

Computing and Information Technology

Page Count

19

Permanent URL

https://peer.asee.org/37282

Download Count

170

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Paper Authors

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Alberto Cureg Cruz California State University, Bakersfield

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Dr. Cruz is an Assistant Professor of Computer Science, Principal Investigator of the Computer Perception Laboratory (COMPLAB), and board member of the Center for Environmental Studies (CES) at the California State University, Bakersfield (CSUB). He received the B.S in Electrical Engineering from the University of California, Riverside (UCR) in 2008 and the Ph.D. in Electrical Engineering from UCR in 2014 as a Fellow of the NSF Integrative Graduate Education Research Traineeship (IGERT). He is the co-author of five refereed journal articles, four book chapters, twelve refereed conference proceedings with full paper, and holds two co-patent applications. Dr. Cruz was awarded funding to support his research from the Consolidated Central Valley Table Grape Pest and Disease Control District, the CSU Program for Education and Research in Biotechnology and the California Energy Research Center. His referee experience includes perennial membership on program committees for the IEEE Conference on Tools with Artificial Intelligence (ICTAI), the IEEE Conference on Artificial Intelligence for Industries (AI4I). He was also the Finance and Registration Chair for the IEEE Conference on Automatic Face and Gesture Recognition (FG) 2018 and 2020. His work on automatic facial expression analysis by computer vision algorithms was featured in Motor Trend Magazine in 2014, 2015 and 2016 for the Best Driver Car of the Year event. Dr. Cruz obtained a few awards for dissemination of research to the greater public (NSF Community Award (2013) and NSF Judges Choice Award (2012) in NSF IGERT Video/Poster Competition). He is a member of the American Society for Engineering Education (ASEE), the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers (IEEE), and the American Society of Agricultural and Biological Engineers (ASABE). Current efforts focus on curriculum design of classes to address the gap in teachers for California’s supplementary authorizations in Computer Science, supported by a five year grant from the Department of Education.

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Antonio-Angel L. Medel California State University Bakersfield

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Antonio-Angel Medel is a first-generation Chicano University graduate. He graduated with a Bachelors of Science in Computer Science and has other publications. One of which is "Computational Thinking and Flipped Classroom Model for Upper Division Computer Science Majors," accepted to FECS'20 in Las Vegas Nevada with a 21% acceptance rating.

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Anthony Chistoper Bianchi California State University, Bakersfield

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Dr. Anthony Bianchi is currently an Assistant Professor in the Department of Computer and Electrical Engineering and Computer Science at California State University, Bakersfield. He has experienced a wide variety of research areas including but not limited to Computer Vision, Image Processing, MRI Computational Analysis, Machine Learning and Science Education. Dr. Bianchi received his Ph.D. from the University of California, Riverside in 2014 where he worked on automated analysis of mild traumatic brain injury via MRI. Then was a Postdoc at Albert Einstein College of Medicine where he worked with MRI analysis of the Tumor Microenvironment of Metastasis. From there her began his Assistant Professorship and started working on research into Science Education.

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Vincent Wong On California State University, Bakersfield Orcid 16x16 orcid.org/0000-0002-0425-3158

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Dr. Vincent Wong On is an assistant professor at California State University, Bakersfield, where he has been a faculty member since 2018. He completed his Ph.D. and M.S. in electrical engineering and his bachelor’s degree in physics with a minor in math at the University of California, Riverside and was a member of the Video Bioinformatics IGERT Fellowship. His research interests include artificial intelligence, machine learning, computer vision, image processing, 3D modelling, and bioinformatics. Dr. On has collaborated actively with researchers in computer science and biology, producing several publications and bioinformatics tools.

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Melissa Danforth California State University, Bakersfield

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Melissa Danforth is a Professor of the Department of Computer and Electrical Engineering and Computer Science at California State University, Bakersfield (CSUB). Dr. Danforth was the PI for a NSF Federal Cyber Service grant (NSF-DUE1241636) to create models for information assurance education and outreach. Dr. Danforth was the Project Director for a U.S. Department of Education grant (P031S100081) to create engineering pathways for students in the CSUB service area. Additionally, she was the co-PI for an NSF IUSE grant for STEM retention (NSF-DUE 1430398) and the co-PD for multiple U.S. Department of Education grants related to engineering education and outreach. Her research interests are focused on network and system security, particularly with respects to protecting mission-critical resources and services. She is also conducting research in applying biological concepts to cybersecurity, such as artificial immune systems.

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Abstract

The flipped classroom (FC) improves student experiences, attitudes, interaction, and performance. With FC, collaborative problem-solving activities replace the lecture (peer-instruction). Outside of class, students watch self-paced videos rather than complete homework. The instructor checks for understanding with a quiz before each lecture, Just in Time Teaching (JiTT). This work concludes a two year study on FC at a medium sized Hispanic Serving Institution involving 70 consenting participants, three sections of Computer Architecture and three sections of Artificial Intelligence (AI). Literature establishes the benefits of FC, yet there is still much to be investigated. Much has changed in the two decades since the FC model was proposed. Modern classrooms are highly online with video content (recorded lectures). We are motivated to reassess student preferences, resource use, performance, and attitudes in comparison to the modern class. Most FC studies focus on CS-0, lower division, and non-majors leaving upper-division core understudied, and we uniquely present our work relative to a control population (CON) with a fixed curriculum and set of instructors. Surprisingly, students favor our CON model, where lecture is spent modeling example problems. The result is statistically significant (p < .1), whereas FC attitudes do not change with participation. When responding to questions about the quality of lecture, 70.59% of comments support our CON model. Literature in secondary education suggests minority students do not prefer dialogic instruction, and it is possible this phenomenon continues with postsecondary education. However, CON has no measurable impact on academic performance while FC does. For Artificial Intelligence midterm exam performance is 79.97% ± 7.75% and 73.22% ± 7.73% for FC and CON respectively, which is significant (p < .1). FC may improve performance in algorithm/theory-based classes despite no significant difference in attitude. FC instructors found prep times to increase by hundreds of hours, concurring with other works, which is considerable in the context of our other findings. It is possible for a traditional classroom to reproduce the attitudes of a FC if well taught and using specific teaching strategies, such as recording lectures. We find that students will enroll in MOOCs or online courses concurrently with the class to obtain quality videos if they are not provided. Some resources are not free, and this behavior is concerning for programs that serve low-income or high-workload/low-availability students. Instructors should make their own videos or provide a curated list, regardless of classroom model. Future work will study effective ways to better engage of female students.

Cruz, A. C., & Medel, A. L., & Bianchi, A. C., & On, V. W., & Danforth, M. (2021, July), Impact of Flipped Classroom Model on High-workload and Low-income Students in Upper-division Computer Science Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37282

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