Computers and Education

Computing has a large and growing impact on education. It is improving classroom interactivity, increasing accessibility, facilitating personalized learning inside and outside the classroom, and providing a platform for exploring fundamental questions about how people learn.

At the same time, demand for computer science education is skyrocketing world-wide. Reaching larger and more diverse audiences requires both understanding how people learn computer science and creating best practices for teaching specific computing topics.

Our faculty study broadly in both of these facets of computers and education. We build new systems, run them at scale, and design interfaces and study the human impacts of technology in the classroom. We gather and analyze data about student behavior to better understand the learning process using both data science techniques and qualitative research.

Strengths and Impact

The Computers and Education research area provides training for PhD students in a CS department, which makes it one of the first in the US and internationally. The area admitted its first PhD students to explicitly focus their research in the area in the 2019-20 academic school year and hired its first tenure-track assistant professor in 2020. The area has diverse research interests, examining how students learn computing, broadening participation in computing, K-12 CS Education, scaling and spreading teaching innovations, using computing resources to improve students’ learning, and improving accessibility of education. While only three tenure-line faculty list this as their primary research area, the area is highly collaborative, incorporating many teaching faculty into research projects and providing support for teaching faculty to publish and mentor graduate students when there is interest. The research area has been helping the department and the College of Education navigate the process of creating new CS + Education undergraduate degrees that will help establish one of the first K-12 computer science licensure programs in the state of Illinois.

Although relatively new, the research area has already begun to distinguish itself in the Computing Education Research community. Faculty have won Best Paper Awards in the Research Track at the ACM SIGCSE annual conference (Nguyen & Lewis, 2020. https://doi.org/10.1145/3328778.3366805) the ACM International Computing Education Research conference (Lewis, 2012. https://doi.org/10.1145/2361276.2361301), the American Society for Engineering Education IL/ IN section (2018), and the ACM SIGCSE best paper in the first 50 years of SIGCSE (Kaczmarczyk, Petrick, East, & Herman, 2019. https://doi.org/10.1145/3324900). Faculty in the area have been recognized as leaders in the field, ser ving as invited authors for both the Cambridge Handbook of Computing Education Research and the Cambridge Handbook of Engineering Education Research. The research area has also been recognized for its excellence in teaching and promoting diversity in computing with awards such as the 2010 and 2020 Mac Van Valkenburg Early Career Teaching Awards from the IEEE Education Society and the 2016 Denice Denton Emerging Leader Award from AnitaB. org. Three PhD students have been awarded a Graduate Research Fellowship from the NSF. Faculty in the research area have been funded by NSF, NSA, Microsoft, and Google.

Seminars

 

 

 

Faculty & Affiliate Faculty

Active Learning in Large Classrooms, Teamwork and Collaboration, Computer-Based Assessment, Instructional Technologies

Success Factors of Underrepresented Students in Online Courses, Universal Access, Crowd-Based Course Curation

Process Oriented / Guided Inquiry Learning, Training Graduate Teaching Assistants, Scalable Education, Semantics Based Autograders

Online Learning Platforms, Outcomes Assessment, Prison Education, Pedagogy

Technology to Improve Classroom Interactivity and Outcomes, Data-Driven Approaches to Teaching and Learning

Incentivizing Productive Student Behaviors, Open Source Curricula, Assessment, Learning Analytics

Outcomes Assessment

Data Discovery, Social Media, Open-Ended Creative Assessments

Pedagogy, Inclusive Classrooms, Adult and Multiple Pathways Computing Education, K-12 CS Education

Scalable Education, Automated Interactive Assessment, Blended Learning

Learning at Scale

How Students Learn Computing, Studying How to Design Effective Instructional Visualization, Teaching at Scale, Assessing Student Learning

AI in Education, Educational Games, Affective Computing, Intelligent Tutoring Systems

Broadening Participation in Computing, K-12 CS Education, Conceptual Change in CS, Cultural and Structural Barriers in CS, Anti-Racist CS Education

Understanding and supporting the needs of students with disabilities based on empirical data; Universal design for learning and best practices in STEM courses; Data driven approaches in pedagogy.

Automated Interactive Assessment, Learning Analytics, Scalable Education, Pedagogy

Teaching at Scale, Outcomes Assessment, Learning Analytics

Teaching at Scale, Assessment, Collaborative Learning, Online Learning Platforms

Pedagogy, Inclusive Classrooms, Adult and Multiple Pathways Computing Education

Intelligent Education Systems, Scalable Education, Applications of Data Science in Education

Learning Analytics, Pedagogy, Computer-Based Testing, Assessment, Asynchronous Exams, Item Generation, Concept Inventories, Plagiarism Detection

Adjunct Faculty

Collaborators

Name Research Interests
Carolyn Anderson, Educational Psychology, Psychology, and Statistics Underrepresented STEM Students, Multi-level Statistics
Suma Bhat, Electrical and Computer Engineering Online Spaces to Support Underrepresented STEM Students
Bill Cope, Education Policy, Organization and Leadership e-Learning Platforms
Jennifer Cromley, Educational Psychology STEM Students' Achievement and Retention
Sebastian Kelle, Computer Science Instructional Development Team Serious Games, Virtual Reality Learning, Interactive Storytelling, Instructional Design
Robb Lindgren, Curriculum & Instruction Learning in Emerging Platforms (e.g., Simulations, Virtual Environments)
Michael C. Loui, Electrical & Computer Engineering Student Motivation and Persistence, Affective Outcomes, Professional Ethics
Luc Paquette, Curriculum & Instruction Modeling Student Behavior, Educational Data Mining, Learning Analytics
Michelle Perry, Educational Psychology Online Spaces to Support Underrepresented STEM Students
Mike Tissenbaum, Curriculum and Instruction Computational Action, Critical Computational Literacies, Digital Empowerment, Equity and Social Justice Through Computing
Matthew West, Mechanical Science and Engineering Online Learning Platforms, Learning Analytics, Computer-Based Testing

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