Colton Harper
Ph.D. StudentSchool of Computing
University of Nebraska-Lincoln
Lincoln, NE, 68508
Office: Schorr 114
Phone: (620) 931-7482
Email: colton.harper {at} huskers.unl.edu
Google Scholar Profile
Ph.D. Student @ Univsersity of Nebraska-Lincoln
Learn MoreColton Harper is a Computer Science Ph.D. student at the University of Nebraska-Lincoln (UNL), working under the guidance of Dr. Stephen Cooper. Colton is focused on designing and facilitating strategies to make computing education more inclusive, engaging, and effective for students in higher education.
Colton's research is centered around iteratively developing conceptual learning and assessment toolss in computing education. He is currently exploring the power of student-generated analogies as a teaching and learning tool for computing concepts. This work primarily draws from cognitive and educational theories including analogical reasoning, conceptual metaphor theory, and constructivism. His current aim is to investigate the potential of student-generated analogies in introductory computing courses, by providing both a theoretical basis and practical recommendations.
Colton's research journey began in 2015 as a freshman in the Molecular and Biochemical Telecommunication (MBiTe) Lab, under the guidance of Dr. Massimiliano Pierobon. As a member of the MBiTe Lab, Colton participated in the 2016 UNL iGEM synthetic biology team. Colton continued his work in the MBiTe Lab through Fall 2021, researching biomolecular communication developing synthetic biological models, carrying out simulations, and applying information theory to measure, develop metrics, and engineer communication among biological cells. In Fall 2021, Colton's passion for teaching and research let him to transition to computing education research under Dr. Stephen Cooper's supervision. Throughout his academic career, Colton has received funding and additional support from UCARE, REU, McNair, the Othmer Graduate Fellowship, and graduate research assistantships.
Apart from his research, Colton is committed to help address the ethical and societal impacts of emerging technologies. He has worked closely with the UNL Ethics Center to establish and maintain a grassroots embedded ethics program for UNL's School of Computing, making it the first land-grant state university to develop such an initiative. This program integrates ethics education touchpoints throughout the degree curriculum, fostering a more ethically-aware computing student body and future professionals. Colton's technology ethics involvement also extends to his contributions as a cofounder, prior program chair and grant writer for Initialize UNL and the Broader Considerations of Technology student groups, and associate conference organizer for the inaugural 2019 Ethics and Broader Considerations of Technology Conference.
My research explores new approaches of how we can leveraging students' personal experiences and prior knowledge to more effectively teach abstract computing concepts in an inclusive way. I seek to help students connect the rather abstract computing concepts to contexts more familiar to the learners and ultimately help make computer science curricula more accessible, engaging, and inclusive.
I primarily draw upon analogical reasoning, conceptual metaphor theory, and constructivism. Grounded in these theories, I investigate how students can utilize their existing knowledge and identities to construct their own understanding of computing topics. In this way, we can empower students to take charge of their learning process and foster a more active and meaningful educational experience.
Perhaps my key contribution is the ongoing iterative development of scaffolding for student-generated analogies, which has shown promise not only as a learning tool but also as a valuable assessment tool for instructors. Through this method, instructors can also gain rich insights into students' conceptual understanding, and may identify potential opportunities for additional support in students' learning.
My recent work with conceptual metaphor theory offers some new insights into student understanding in introductory computing education and has the potential to be applied to assess the alignment between student and instructor language. Alignment of such language can lead to more effective and efficient communication and knowledge transfer in the classroom.
Fall 2022 & Fall 2023
This introductory course provided students with a solid foundation in computer science principles, computational problem solving, and data science using Python. Students with little or no prior programming experience were guided through topics including Python basics, functions, data structures, object-oriented programming, data processing, analysis, and visualization. The course also integrates some discussions and activities to help highlight the importance of ethical reasoning and the intersection of society and technology, particularly as it relates to data science, course topics, and timely recent events. Students engaged with the material through lectures, labs, and assignments to develop basic programming skills and understanding foundational computing concepts.