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Research Spending & Results

Award Detail

Doing Business As Name:University of Illinois at Urbana-Champaign
  • Luc Paquette
  • (917) 724-1105
Award Date:01/16/2020
Estimated Total Award Amount: $ 695,871
Funds Obligated to Date: $ 113,212
  • FY 2020=$113,212
Start Date:02/01/2020
End Date:01/31/2025
Transaction Type:Grant
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.076
Primary Program Source:040106 NSF Education & Human Resource
Award Title or Description:CAREER: Combining Human Judgment and Data-Driven Approaches for the Development of Interpretable Models of Student Behaviors: Applications to Computer Science Education
Federal Award ID Number:1942962
DUNS ID:041544081
Parent DUNS ID:041544081
Program:ECR-EHR Core Research
Program Officer:
  • Wu He
  • (703) 292-7593

Awardee Location

Street:1901 South First Street
Awardee Cong. District:13

Primary Place of Performance

Organization Name:University of Illinois at Urbana-Champaign
Street:506 S. Wright Street
Cong. District:13

Abstract at Time of Award

Debugging, the process of identifying and resolving defects in computer programs, is an important skill to acquire while learning to program. However, many novice programmers, with good understanding of programming, struggle with the debugging process. Despite this, debugging is rarely explicitly taught. This project will use data-driven approaches to study the debugging processes of novice programmers enrolled in a college level introductory computer science course, identify the meaningful elements of their debugging behaviors and how those elements combine to form common debugging strategies. This knowledge will be used to create computer algorithms that are able to identify which debugging strategies students use when programming. These algorithms will be designed to be interpretable by students and instructors. These algorithms will be used to support students in learning efficient debugging strategies, assist instructors in monitoring their students' debugging practices, and help future K-12 teachers in learning about the meaningful elements of the debugging process. The results of this project will positively impact the state of computer science education in both college level introductory computer science courses and in the K-12 level by supporting future teachers in learning how to foster important debugging skills. The project will contribute to the state of the art in student behavior modeling by formalizing an approach that combines knowledge engineering and machine learning to create interpretable models of student behavior. It will provide empirical evidence illustrating how the interpretability of a student behavior model can provide powerful pedagogical advantages beyond its accuracy at predicting a student's behavior. The proposed approach will be applied to study debugging strategies in college level introductory computer science courses through the log data collected from an online problem-solving platform named PrairieLearn. The benefits of interpretable models will be compared to those of traditional machine learning approaches, using rigorous research to identify the best methods for supporting students and instructors. Specifically, the project will apply this approach, leveraging the increased interpretability of the models it creates, to: (1) better understand students' debugging behaviors; (2) support students in self-reflecting about their debugging strategies and developing efficient debugging practices; (3) provide instructors with actionable information about their students' debugging processes; and (4) support future teachers in acquiring expertise in formulating hypotheses about students' debugging strategies. By doing so, the project will contribute to general knowledge about debugging processes for novice programmers, and establish methods to support college students and future K-12 teachers in acquiring explicit knowledge about the debugging process. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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