Replication and Expansion Study on Factors Influencing Student Performance in CS2

被引:0
|
作者
Ellis, Margaret [1 ]
Hooshangi, Sara [1 ]
机构
[1] Virginia Tech, Blacksburg, VA 24061 USA
来源
PROCEEDINGS OF THE 54TH ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, VOL 1, SIGCSE 2023 | 2023年
关键词
CS2; Student Performance; Data Structures; Prior CS Knowledge; Diversity;
D O I
10.1145/3545945.3569867
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
While many studies have focused on students' performance in CS1 courses, research related to the performance and persistence of students in CS2 classes is not as widely performed. In this work, we will extend our previous work to examine students' performance in CS2. We examined a data set that spanned over seven years on more than 5300 student records. In addition to typical factors studied by others (i.e. gender, race, CS1 performance), our work also took into account the relationship between various CS1 pathways to CS2, student major, and the number of previous college CS courses (including transfer credits) and student performance in CS2. CS1 grade is a good indicator of performance in CS2. Gender was not a significant factor in determining performance in CS2 and undeclared engineering majors stood out as high performers. CS majors passed the course at higher rates than other majors. Our large data set allowed for more granular analysis according to race and ethnicity and additional access to students' underserved status. Race and ethnicity had a significant correlation with performance, and so did the underserved status. Our large data set confirmed some of the findings of our previous work, while providing some new insight.
引用
收藏
页码:896 / 902
页数:7
相关论文
共 4 条
  • [1] Factors Influencing Student Performance and Persistence in CS2
    Hooshangi, Sara
    Ellis, Margaret
    Edwards, Stephen H.
    PROCEEDINGS OF THE 53RD ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION (SIGCSE 2022), VOL 1, 2022, : 286 - 292
  • [2] Using a Student Response System in CS1 and CS2
    Chamillard, A. T.
    SIGCSE 11: PROCEEDINGS OF THE 42ND ACM TECHNICAL SYMPOSIUM ON COMPUTER SCIENCE EDUCATION, 2011, : 299 - 304
  • [3] Study Factors for Student Performance Applying Data Mining Regression Model Approach
    Khan, Shakir
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2021, 21 (02): : 188 - 192
  • [4] Factors Affecting Student Performance in E-Learning: A Case Study of Higher Educational Institutions in Indonesia
    Marlina, Evi
    Tjahjadi, Bambang
    Ningsih, Sri
    JOURNAL OF ASIAN FINANCE ECONOMICS AND BUSINESS, 2021, 8 (04): : 993 - 1001