Publications

Conferences

  1. Feldhausen, R., Weese, J. L., & Bean, N. (2018). Increasing Student Self-Efficacy in Computational Thinking via STEM Outreach Programs. Proceedings of the 49th ACM Technical Symposium on Computer Science Education (SIGCSE 2018), Baltimore, MD, USA, February 21–24, 2018.
  2. Aguirre, C., Gullapalli, S., De La Torre, M. F., Lam, A., Weese, J. L., & Hsu, W. H. (2017). Learning to Filter Documents for Information Extraction using Rapid Annotation. Proceedings of the 1st International Conference on Machine Learning and Data Science (MLDS 2017). Noida, India, December 14-15, 2017.
  3. Weese, J. L., &  Feldhausen, R. (2017). STEM Outreach: Assessing Computational Thinking and Problem Solving. Proceedings of the 124th American Society for Engineering Education Annual Conference and Exposition (ASEE 2017), Columbus, OH, USA, June 25-28, 2017. 
  4. Weese, J. L. (2016). Mixed Methods for the Assessment and Incorporation of Computational Thinking in K-12 and Higher Education (Doctoral Consortium). Proceedings of the 12th International Computing Education Research Conference (ICER 2016), Melbourne, VIC, Australia, September 8-12, 2016. 
  5. Weese, J. L., Feldhausen, R., & Bean, N. H. (2016). The Impact of STEM Experiences on Student Self-Efficacy in Computational Thinking. Proceedings of the 123rd American Society for Engineering Education Annual Conference and Exposition (ASEE 2016), New Orleans, LA, USA, June 26-29, 2016.
  6. Weese, J. L. & Hsu, W. H. (2016). Work in Progress: Data Explorer – Assessment Data Integration, Analytics, and Visualization for STEM Education Research. Proceedings of the 123rd Annual American Society for Engineering Education Annual Conference and Exposition (ASEE 2016), New Orleans, LA, USA, June 26-29, 2016.
  7. Bean, N., Weese, J. L., Feldhausen, R., & Bell, R. (2015). Starting From Scratch: Developing a Pre-Service Teacher Program in Computational Thinking. Frontiers in Education (FIE 2015), p. 1307-1314. El Paso, TX, USA, October 21-24, 2015.

Book Chapters

  1. Weese, J. L., Hsu, W. H., Knight, K., & Murphy, J. C. (2016). Towards a Web of Derivative Works: Machine Learning for Parody Detection by Classification. In Hai-Jew, S., ed., Data Analytics in Digital Humanities. Hershey, PA, USA: IGI Global.
  2. Weese, J. L. (2014). Predictive Analytics in Digital Signal Processing: A Convolutive Model for Polyphonic Instrument Identification and Pitch Detection Using Combined Classification. In Hsu, W. H., ed., Emerging Methods in Predictive Analytics: Risk Management and Decision-Making, p. 223-253. Hershey, PA, USA: IGI Global.

Other

  1. Weese, J. L.  Bringing Computational Thinking to K-12 and Higher Education. (Dissertation). Kansas State University. Manhattan, KS, USA. 
  2. Weese, J. L.  A Convolutive Model for Polyphonic Instrument Identification and Pitch Detection Using Combined Classification. (Master’s thesis). Kansas State University. Manhattan, KS, USA.