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Professor awarded an NSF Grant: Looking for Better Ways to Teach Introductory Computing

Adrienne Decker, associate professor, School of Interactive Games and Media and MAGIC affiliate has recently been awarded an NSF IUSE grant collaboratively with Briana Morrison, assistant professor, Computer Science, University of Nebraska Omaha (UNO)  and Lauren Margulieux, assistant professor, College of Education & Human Development’s Learning Technologies Division, Georgia State University (GSU).  Also participating as senior personnel is  Liz Johnson, chair, Computer Science, Xavier University.

The three year grant ($299K total) will incorporate instructional techniques identified through educational psychology research as effective ways to improve student learning and retention in introductory programming.  Specifically, this work will focus on the use of subgoal labels.  Subgoal labels, which are explanations that describe the function of steps in the problem solution to the learner and highlight the problem-solving process have been effective in other STEM fields in helping students see an expert’s solution to a problem while they are learning the approach the solving the problems.

Experts, including instructors, teaching introductory level courses are often unable to explain the process they use in problem solving at a level that learners can grasp because they have automated much of the problem-solving processes given the many years of practice. The goal of this project is to use subgoal labels throughout introductory computing courses and investigate the impact on student learning and retention. Multiple worked examples with subgoal labels for each concept in an introductory computer science course will be developed by the research team. These examples will then be implemented in multiple classrooms (RIT, UNO, GSU, and Xavier University) and across multiple semesters in order to measure the effectiveness of the intervention and to continuously improve the development and delivery of the learning materials. The use of a mixed-methods design, incorporating qualitative and quantitative data collection methods, with use of control groups will guide the investigation and measurement of learning impact. In the final year of the project, a large-scale deployment of the intervention and supporting learning materials will be disseminated to a diverse set of institutions to further investigate impacts. The findings and instructional materials generated during this project have the potential to positively impact not only computing and computer science education, but more broadly other STEM disciplines.