CHI TIẾT NGHIÊN CỨU …
Tiêu đề
Predicting student success in computer science - A reproducibility study
Tác giả
Kumar A.N.
Năm xuất bản
2019
Source title
Proceedings - Frontiers in Education Conference, FIE
Số trích dẫn
1
DOI
10.1109/FIE.2018.8658429
Liên kết
Tóm tắt
(Research Category/Full paper) - A recent study conducted at the University of Oklahoma, a large research university attempted to use the grades on initial Computer Science courses to predict the success of Computer Science majors. We attempted to reproduce this study in a mid-sized liberal arts institution. We analyzed 15 years of data of students (majors as well as non-Computer Science majors) who had taken introductory Computer Science courses. We found that the better the grade on Computer Science I, the introductory course in the major, the better the cumulative GPA of the student upon graduation, and this applied to Computer Science majors as well as non-majors. All the students who had successfully graduated with a Computer Science degree had earned at least a C grade in the first three required courses: Computer Science I, Computer Science II and Data Structures. When we considered grades on six of the required courses in the Computer Science sequence, we found that students generally earned the same or lower grade on each subsequent course. Therefore, the performance of Computer Science majors on the first three courses in the required course sequence can reasonably be used as predictors of their success in the major. Finally, we found that Math SAT score was a good predictor of student success in Computer Science I as well as obtaining an undergraduate degree regardless of the major. Our study generalizes the results of the previous study and strengthens the results by finding that they are statistically significant. (Abstract) © 2018 IEEE.
Từ khóa
Computer Science enrollment management; Computer Science I; Predictors of success
Tài liệu tham khảo
Trytten D.A., McGovern A., Moving from managing enrollment to predicting student success, Proc. Frontiers in Education (FIE 2017), (2017); Generation CS: Computer Science Undergraduate Enrollments Surge since 2006, (2017); Beaubouef T., Mason J., Why the high attrition rate for computer science students: Some thoughts and observations, SIGCSE Bull., 37, 2, pp. 103-106, (2005); Kumar A.N., Closed labs in computer science i revisited in the context of online testing, Proc. of SIGCSE Technical Symposium on Computer Science Education (SIGCSE 2010), pp. 539-543, (2010); Wing J.M., Computational thinking, CACM Viewpoint, pp. 33-35, (2006); Steele C.M., A threat in the air: How stereotypes shape intellectual identity and performance, American Psychologist, 52, pp. 613-629, (1997); Rosser P., The SAT gender gap: Indetifying the causes, Center for Women Policy Studies; Drummond C., Replicability is not reproducibility: Nor is it good science, Proc. Evaluation Methods for Machine Learning Workshop, 26th ICML, (2009); Estimating the reproducibility of psychological science, Science, 349, 6251, (2015)
Nơi xuất bản
Institute of Electrical and Electronics Engineers Inc.
Hình thức xuất bản
Conference paper
Open Access
Nguồn
Scopus