Montreal, Quebec, Canada
June 22, 2025
June 22, 2025
August 15, 2025
Computers in Education Division (COED)
Diversity
18
https://peer.asee.org/56478
Friday James is a PhD Candidate at Kansas State University. He has a double-majored Bachelor's degree in Statistics/Computer Science from University of Agriculture, Makurdi - Nigeria. He got a Master's degree in Statistics and a Master's degree in Computer Science from University of Ilorin - Nigeria and Kansas State University - Kansas USA in 2015 and 2021 respectively. His research interest cuts across the use of machine learning and data science in Computing Science Education to improve teaching and learning.
Dr. Josh Weese is a Teaching Assistant Professor at Kansas State University in the department of Computer Science. Dr. Weese joined K-State as faculty in the Fall of 2017. He has expertise in data science, software engineering, web technologies, computer science education research, and primary and secondary outreach programs. Dr. Weese has been a highly active member in advocating for computer science education in Kansas including PK-12 model standards in 2019 with an implementation guide the following year. Work on CS teacher endorsement standards are also being developed. Dr. Weese has developed, organized and led activities for several outreach programs for K-12 impacting well more than 4,000 students.
Dr. Nathan Bean is a Teaching Associate Professor at Kansas State University Department of Computer Science and Co-Director of the Advancing Learning and Teaching in Computer Science (ALT+CS) Lab. His research is focused on the need to grow the body of students skilled in computing – both within the field of Computer Science, and within other disciplines that increasingly rely on the tools computer science makes available to advance their own work. Thus, his research involves investigations into how to effectively reach a broader and more diverse audience of students, and developing pedagogical techniques and technologies that allow it to be done at scale.
Russell Feldhausen received a bachelor’s degree in computer science in 2008, and a master’s degree in computer science in 2018, both from Kansas State University. He is currently pursuing a doctorate in computer science with a focus on computer science education, also at K-State. Feldhausen’s research interest is computer science education, targeting rural populations and exploring ways to integrate mastery learning into CS curricula. He is also actively involved in many K-12 outreach programs providing curricula and teacher training throughout Kansas.
Dr. Michelle Friend is an Associate Professor in the Teacher Education Department at the University of Nebraska at Omaha. She teaches CS teaching methods and research methods. Her research focuses on equity in computer science and interdisciplinary connections between computer science and other subjects. She received her Ph.D. from Stanford University in Learning Science and Technology Design, and previously taught middle school computer science.
Carrie Aponte holds a Bachelors degree in Psychology, and a minor in Communication. Currently she is an undergraduate researcher and teaching assistant in the Department of Computer Science at Kansas State University. Her research interests include Data Science and Computer Science Education.
The lack of computer science education in rural areas presents unique challenges in the present pursuit of achieving equitable access to computer science education. The increase in the recognition of the need for computer science education comes with a need for inclusion of rural areas, and a corresponding increase in the demand of competent computer science teachers and educators. Teacher training programs play an important role in meeting these demands. This paper evaluates the impact of a teacher training program with focus on professional identity, commitment, confidence and competence as it relates to the teaching of computer science. The research includes teachers from rural, suburban and town locales enrolled in three separate semester courses. Through a mixed-method design, it uses quantitative data obtained through surveys taken prior to and at the completion of the training program to measure the impact. A combination of p-values and effect sizes were used to measure the impact of the teacher training programs. The survey covers three different domains - Teacher and Computing Identity, Rural Identity and Teacher Mindset, and lastly, Teaching Perceptions and Computational Thinking. Qualitative data gathered through reflective journals provides insights into teachers’ backgrounds and teaching experiences as well as anticipated professional growth. Following the training, findings show that rural teachers reported positive shifts in their identities and teaching competencies and are more likely to advocate for more students to take computer science courses. Teachers from the rural locales also showed a marked improvement in confidence and commitment to teaching computer science.
James, F. E., & Weese, J. L., & Bean, N. H., & Feldhausen, R., & Friend, M., & Stewart, R., & Aponte, C. G., & Allen, D. S. (2025, June), Expanding Computer Science Education in Rural Areas: Impact of Teacher Training on Teachers’ Identity, Commitment, Confidence and Competence Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/56478
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