ABSTRACT
From 2017-2022, our research project supported faculty at higher-ed institutions in the United States to adopt POGIL in CS1 courses. The faculty participated in summer workshops and mentoring groups during the academic year. At the end of each term, the faculty submitted a summary of their students' grades to the research team. This paper presents a Bayesian analysis of the student grades using a hierarchical ordinal logistic regression model. The data included the number of A, B, C, D, F, and W grades, disaggregated by gender and race, for all students enrolled in the course. In addition to each POGIL term, faculty submitted grades for one or two previous terms when they taught the same course without POGIL. Most faculty observed an improvement in student pass rates in the second and third term after they began teaching with POGIL. We present detailed visualizations of grade distributions from 25 faculty, along with the results of the statistical analysis. Our model suggests that CS1 faculty adopting POGIL can expect to see a modest increase of A grades and a modest decrease of DFW grades. However, the grades of Black, Hispanic, and Indigenous students decreased slightly, especially in the first term faculty taught with POGIL. The results of this study demonstrate the importance of gender and racial analysis in evaluating pedagogical approaches.
- Alan Agresti. 2010. Analysis of Ordinal Categorical Data second ed.). John Wiley & Sons, Inc., Hoboken, NJ, USA. xii396 pages. https://doi.org/10.1002/9780470594001Google ScholarCross Ref
- Jens Bennedsen and Michael E. Caspersen. 2019. Failure Rates in Introductory Programming: 12 Years Later. ACM Inroads, Vol. 10, 2 (2019), 30--36. https://doi.org/10.1145/3324888Google ScholarDigital Library
- Catherine Bénéteau, Zde?ka Guadarrama, Jill E. Guerra, Laurie Lenz, Jennifer E. Lewis, and Andrei Straumanis. 2017. POGIL in the Calculus Classroom. PRIMUS, Vol. 27, 6 (2017), 579--597. https://doi.org/10.1080/10511970.2016.1233159Google ScholarCross Ref
- John J. Farrell, Richard S. Moog, and James N. Spencer. 1999. A Guided-Inquiry General Chemistry Course. J. Chem. Educ, Vol. 76, 4 (1999), 570--574. https://doi.org/10.1021/ed076p570Google ScholarCross Ref
- Bhuvaneswari Gopal and Stephen Cooper. 2022. POGIL-like Learning in Undergraduate Software Testing and DevOps - A Pilot Study. In Proceedings of the ACM Conference on Innovation and Technology in Computer Science Education. https://doi.org/10.1145/3502718.3524776Google ScholarDigital Library
- Helen H. Hu and Tricia D. Shepherd. 2014. Teaching CS 1 with POGIL Activities and Roles. In Proceedings of the 45th ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/2538862.2538954Google ScholarDigital Library
- Helen H. Hu, Aman Yadav, Donna M. Gavin, Clif Kussmaul, and Chris Mayfield. 2023. Teamwork in CS1: Student Learning and Experience with POGIL. In Proceedings of the 54th ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/3545945.3569813Google ScholarDigital Library
- Clif Kussmaul, Helen H. Hu, Patricia B. Campbell, Chris Mayfield, and Aman Yadav. 2022. Professional Development and Support for POGIL in Computer Science. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/3478431.3499381Google ScholarDigital Library
- Clif Kussmaul, Helen H. Hu, Chris Mayfield, and Patricia B. Campbell. 2023. A Five Stage Faculty Development Program to Transform Introductory Courses in Computer Science: The IntroCS POGIL Project. In Handbook of STEM Faculty Development, Sandra M. Linder, Cindy Lee, and Karen High (Eds.). Information Age Publishing.Google Scholar
- Stanley M. Lo and Jonathan I. Mendez. 2019. POGIL: An Introduction to Process Oriented Guided Inquiry Learning for Those Who Wish to Empower Learners. Stylus Publishing, LLC, Sterling, VA, Chapter L: Learning -- The Evidence, 85--110.Google Scholar
- P. C. Lotlikar and R. Wagh. 2016. Using POGIL to Teach and Learn Design Patterns - A Constructionist Based Incremental, Collaborative Approach. In 2016 IEEE Eighth International Conference on Technology for Education (T4E). https://doi.org/10.1109/T4E.2016.018Google ScholarCross Ref
- Chris Mayfield, Sukanya Kannan Moudgalya, Aman Yadav, Clif Kussmaul, and Helen H. Hu. 2022. POGIL in CS1: Evidence for Student Learning and Belonging. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/3478431.3499296Google ScholarDigital Library
- Briana B. Morrison, Beth A. Quinn, Steven Bradley, Kevin Buffardi, Brian Harrington, Helen H. Hu, Maria Kallia, Fiona McNeill, Oluwakemi Ola, Miranda Parker, Jennifer Rosato, and Jane Waite. 2022. Evidence for Teaching Practices That Broaden Participation for Women in Computing. In Proceedings of the 2021 Working Group Reports on Innovation and Technology in Computer Science Education. https://doi.org/10.1145/3502870.3506568Google ScholarDigital Library
- Suzanne M. Ruder and Sally S. Hunnicutt. 2008. POGIL in Chemistry Courses at a Large Urban University: A Case Study. Vol. 994. American Chemical Society, Washington, DC, 133--147. https://doi.org/10.1021/bk-2008-0994.ch012Google ScholarCross Ref
- Shawn R. Simonson (Ed.). 2019. POGIL: An Introduction to Process Oriented Guided Inquiry Learning for Those Who Wish to Empower Learners. Stylus Publishing, LLC, Sterling, VA.Google Scholar
- Andrei Straumanis and Emily A. Simons. 2008. A multi-institutional assessment of the use of POGIL in Organic Chemistry. Vol. 994. American Chemical Society, Washington, DC, 226--239. http://dx.doi.org/10.1021/bk-2008-0994.ch019Google ScholarCross Ref
- Stan Development Team. 2022a. RStan: the R interface to Stan. http://mc-stan.org/ R package version 2.26.13.Google Scholar
- Stan Development Team. 2022b. Stan Modeling Language Users Guide and Reference Manual. http://mc-stan.org/ Version 2.26.Google Scholar
- Lindsey Walker and Abdi-Rizak M. Warfa. 2017. Process oriented guided inquiry learning (POGIL®) marginally effects student achievement measures but substantially increases the odds of passing a course. PLOS ONE, Vol. 12, 10 (2017), 1--17. https://doi.org/10.1371/journal.pone.0186203Google ScholarCross Ref
- Aman Yadav, Clif Kussmaul, Chris Mayfield, and Helen H. Hu. 2019. POGIL in Computer Science: Faculty Motivation and Challenges. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education. https://doi.org/10.1145/3287324.3287360Google ScholarDigital Library
Index Terms
- Analysis of Student Grades Before and After Adopting POGIL
Recommendations
Teamwork in CS1: Student Learning and Experience with POGIL
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1Employers regularly list teamwork as one of the most desirable skills they are seeking in college graduates. This paper describes a study about the effect of teamwork on student learning and classroom culture in a CS1 college class. In Fall 2021, an ...
Analysis of Student Grades After Switching to POGIL
SIGCSE 2023: Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2Process Oriented Guided Inquiry Learning (POGIL) is becoming increasingly popular in computer science as an evidence-based teaching strategy. From 2017--2022, the IntroCS-POGIL project supported faculty at higher-ed institutions in the United States to ...
Examining Interest and Grades in Computer Science 1: A Study of Pedagogy and Achievement Goals
Computer Science 1 (CS1), the first course taken by college-level computer science (CS) majors, has traditionally suffered from high failure rates. Efforts to understand this phenomenon have considered a wide range of predictors of CS success, such as ...
Comments