|Ph.D. (Computer Science) — Kansas State University, Manhattan, KS
Dissertation: Bringing Computational Thinking to K-12 and Higher Education
|M.S. (Computer Science) — Kansas State University, Manhattan, KS
Thesis: A Convolutive Model for Polyphonic Instrument Identification and Pitch Detection using Combined Classification
|B.S. (Computer Systems Technology) — Kansas State University, Salina, KS||2007-2011|
|Teaching Assistant Professor, Kansas State University, Manhattan, KS
Primary responsibilities include teaching and advising students, as well as service to the department/university, involvement with student organizations, outreach to the community, and research.
Courses taught (details in Teaching Experience) include:
|(Instructor) Fall 2017
Spring 2018 – Present
|Graduate Research Assistant, Kansas State University, Manhattan, KS
||2012 – 2017|
|Insight GK12 Fellow, Kansas State University, Manhattan, KS
||2013 – 2015|
|Graduate Teaching Assistant, Kansas State University, Manhattan, KS
|System Engineer Intern, Cerner Corporation, Kansas City, MO
||May 2011- July 2011|
|Writing Center Tutor, Kansas State University, Salina, KS
|IT Assistant, Kasa Industrial Controls Inc, Salina, KS
||2010 – 2011|
|Technology Intern, Philips Lighting, Salina, KS
||2008 – 2010|
CIS 115 – Introduction to Computing Technology
A survey of the discipline of Computing Science and its interaction with other disciplines, incorporating historical development, theories, and tools of Computing Science (algorithm design and programming). Topics include: mechanical computers, digital computers, big data, AI, robotics, security, scientific computing, simulation, and web technologies. Students also learn Python as part of this course to reinforce the previously mentioned topics.
CIS 300 – Data and Program Structures
Planned and taught an hour introduction to programming lesson to 4th-6th grade students from the Manhattan Kansas Starbase program. In a pair programming exercise, students translated the sheet music of the song Ode to Joy using the Scratch programming language.
STEM Summer Institute
Planned and taught programming courses to 5th-9th grade students. These courses range in theme, including introduction to Scratch programming, artificial intelligence, game design, and micro controllers. Lesson are aimed to give young students an introduction to programming and other technology, as well as get them interested in pursuing a STEM- field.
Girl Scouts of the USA
Created and planned a three-hour workshop for junior girl scouts to learn about artificial intelligence. Scouts learned the basics in translating simple human intelligence into computer controlled sprite, how artificial neural networks operate, and how supercomputing clusters operate. This workshop relied heavily on the Scratch programming language and CS unplugged activities.
|Spring 2016, 2017|
GROW and EXCITE
Participated in outreach programs designed to engage young women in 6th-12th grade in STEM. As part of these programs, I taught lessons in storytelling, music, and artificial intelligence in the Scratch programming language.
K-State Codes – Introduction to Python
Develop and instruct an accelerated course on Python programming. This course is a free, non-credit course open to all students and faculty of K-State. This course aims to teach fundamental programming concepts and syntax in the Python language.
|Fall 2016 – 2017|
CIS 415 – Ethics and Computing Technology
Ethics and Computing Technology covers ethical and moral issues that involve computing technologies and their impact on society. This class is given in two eight-week sections, leveraging a flipped classroom model and covering topics including basic ethical principles and theories, software engineering and ACM codes of ethics, privacy, cyber security, safety-critical software, and computing technologies in society. Served as a graduate teaching assistant for the first section, and am currently serving as an instructor for the second.
Develop and deliver technology and engineering lessons at the K-12 level. Lessons include, but are not limited to sensors, Lego NXT robots, Scratch programming, Wii remotes, and Kinect sensors. A goal of this program is to expose students, particularly from rural areas, to engineering and various technologies.
CIS 200 — Programming Fundamentals
Programming Fundamentals is an introduction to Java programming with a small focus on C#. Students focus on learning how to program in Java as well as general programming practices and methodology. Responsibilities included teaching multiple lab sections, proctoring exams, grading, and holding regular office ours.
CIS 104 — Introduction to Word Processing Applications
This course is built to teach students the basics of Microsoft Word. Learning objectives focused on different formatting and tools in Word. Responsibilities included presenting lectures, proctoring exams, grading, and holding office hours.
CIS 103 — Introduction to Database Applications
This course is built to teach students how to use Microsoft Access. Students learned the basics of databases and why they are needed. Responsibilities included presenting lectures, proctoring exams, grading, and holding office hours.
CIS 102 — Introduction to Spreadsheet Applications
This course is built to teach students the basics of Microsoft Excel. Students learned how to format spreadsheets and use formulas. Responsibilities included presenting lectures, proctoring exams, grading, and holding office hours.
Treasurer, Kansas State University, Manhattan, KS
Kansas State University, Manhattan, KS
|Reviewer for ICLS||2018|
|Reviewer for ITiCSE||2017|
|Reviewer for ASEE||2016, 2017|
|Reviewer for SIGCSE||2017-2018|
|Reviewer for CSCL||2016|
|Department Head Search Committee Member — Kansas State University, Manhattan, KS||Fall 2016|
- 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. To Appear.
- 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. To Appear.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Weese, J. L. Bringing Computational Thinking to K-12 and Higher Education. (Dissertation). Kansas State University. Manhattan, KS, USA.
- 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.
|KSU Research and the State – STEM Education Group – 1st place||2016|
|Kansas State Research Forum – Engineering, Math, and Physical Sciences Poster Session – 2nd place||2014|