POGIL in CS1: Evidence for Student Learning and Belonging
Reference: Chris Mayfield, Sukanya Kannan Moudgalya, Aman Yadav, Clif Kussmaul, 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 V. 1.
Entry Key: \cite{10.1145/3478431.3499296}
Entry Type: @inproceedings
Metadata
| Field | Value |
|---|---|
| author | Mayfield, Chris and Moudgalya, Sukanya Kannan and Yadav, Aman and Kussmaul, Clif and Hu, Helen H. |
| title | POGIL in CS1: Evidence for Student Learning and Belonging |
| year | 2022 |
| isbn | 9781450390705 |
| publisher | Association for Computing Machinery |
| address | New York, NY, USA |
| url | https://doi.org/10.1145/3478431.3499296 |
| doi | 10.1145/3478431.3499296 |
| abstract | For the past ten years, computer science instructors have adopted Process Oriented Guided Inquiry Learning (POGIL). Other STEM disciplines have shown conclusively that POGIL impacts student learning and knowledge retention. However, most research about POGIL in computer science has focused on perceptions and experiences, not learning outcomes. In this study, we examined the influence of POGIL on student learning in CS1. We collected data from all sections of CS1 at the same institution. Four of the faculty implemented POGIL, and three taught with other active methods. The learning data included pre and post assessments, midterm and final exams, and a retention test at the beginning of the next course. Students also completed three surveys about their prior programming experience, sense of belonging, and perceptions of teamwork. We used multiple regression to analyze the relationship between the survey data and learning outcomes. Our results show that students in the POGIL sections outperformed students in the other sections. POGIL students scored higher on the post-test, and a higher proportion of them met the grade requirement to progress to the next course. After the five-week winter break, POGIL students had higher and more consistent scores on the retention test. These results provide evidence that POGIL can be very effective as an instructional technique in computer science. |
| booktitle | Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 |
| pages | 439–445 |
| numpages | 7 |
| keywords | comparative study, active learning, collaborative learning |
| location | Providence, RI, USA |
| series | SIGCSE 2022 |