Silva, M., & Shaffer, E. G., & Nytko, N., & Amos, J. R. (2020, June), A Case Study of Early Performance Prediction and Intervention in a Computer Science Course  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--33977

\bibitem{asee_peer_33977}
  Silva, M., \& Shaffer, E. G., \& Nytko, N., \& Amos, J. R. (2020, June), \emph{A Case Study of Early Performance Prediction and Intervention in a Computer Science Course}
  Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line .
  10.18260/1-2--33977

Mariana Silva, Eric G. Shaffer, Nicolas Nytko, and Jennifer R. Amos.  "A Case Study of Early Performance Prediction and Intervention in a Computer Science Course".  2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June.  ASEE Conferences, 2020.  https://peer.asee.org/33977 Internet.  10 May, 2024

\bibitem{asee_peer_33977}
  Mariana Silva, Eric G. Shaffer, Nicolas Nytko, and Jennifer R. Amos.
  "A Case Study of Early Performance Prediction and Intervention in a Computer Science Course".
  \emph{2020 ASEE Virtual Annual Conference Content Access, Virtual On line , 2020, June}.
  ASEE Conferences, 2020.
  https://peer.asee.org/33977 Internet.  10 May, 2024

@INPROCEEDINGS{asee_peer_33977
author = "Mariana Silva, Eric G. Shaffer, Nicolas Nytko, and Jennifer R. Amos"
title = "A Case Study of Early Performance Prediction and Intervention in a Computer Science Course"
booktitle = "2020 ASEE Virtual Annual Conference Content Access"
year = "2020"
month = "June"
address = "Virtual On line "
publisher = "ASEE Conferences"
note = {https://peer.asee.org/33977}
number = {10.18260/1-2--33977}
}

TY  - CPAPER
AB  - This work presents the results of an intervention study performed in an upper-division undergraduate computer science (CS) course with 348 students, designed to offer additional resources to students that were identified as “at-risk” of low performance after completing graded assessments during the first two weeks of the semester. The course uses Python as the required programming language, however not every student that takes the class has prior experience with Python. Moreover, the overall programming experience is not uniform among the students, which can be partially explained due to the large diversity of majors in the class (44% CS, 19% CS + X, 19% Engineering and 18% other majors). This disparity in programming skills can greatly affect the overall student’s experience in the classroom and potentially their overall course performance.
 
Other studies have shown how prerequisite grades are used to predict a student's performance in a course. For example, Liao et al [1] determined that prerequisite grades are the most predictive data for upper-division courses. However, for introductory programming courses, in-class short quiz questions are the most predictive data for student performance. In this study, we used data collected during the first two weeks of the semester to predict if a student was at risk of low performance. The available data consisted of two quizzes and two homework assignments covering linear algebra (course prerequisite) and basic Python programming. We used equivalent data from previous semesters to train a model using machine learning algorithms to predict students that were at risk of lower performance, here defined as a final grade less than 80% (roughly representing 30% of the class). Students that were identified as “at-risk” received an invitation to join a 6-week course, which was created to give students an additional opportunity to work on programming activities using Python. Out of the 60 students that received the invitation, 24 accepted and joined the short course that started on week 3 of the semester. The class met once a week for 80 minutes, and was held in an active learning classroom, where each group table had a large computer monitor and a white board. Students  were split into groups of 5 and given a programming problem that required the entire group to collaborate. The tasks involved real world examples, designed in a structured way to allow students to complete the solution on their own, without a lot of guidance from the instructors. Focus groups were conducted along with a survey to capture student perceptions of the course.
 
[1] Liao et al, “Exploring the Value of Different Data Sources for Predicting Student Performance in Multiple CS Courses”, SIGCSE '19.

AU  - Mariana Silva
AU  - Eric G. Shaffer
AU  - Nicolas Nytko
AU  - Jennifer R. Amos
CY  - Virtual On line 
DA  - 2020/06/22
PB  - ASEE Conferences
TI  - A Case Study of Early Performance Prediction and Intervention in a Computer Science Course
UR  - https://peer.asee.org/33977
DO  - 10.18260/1-2--33977
ER  -