TY - GEN
T1 - An empirical study of iterative improvement in programming assignments
AU - Pettit, Raymond
AU - Homer, John
AU - Gee, Roger
AU - Starbuck, Adam
AU - Mengel, Susan
N1 - Publisher Copyright:
Copyright © 2015 ACM.
PY - 2015/2/24
Y1 - 2015/2/24
N2 - As automated tools for grading programming assignments become more widely used, it is imperative that we better understand how students are utilizing them. Other researchers have provided helpful data on the role automated assessment tools (AATs) have played in the classroom. In order to investigate improved practices in using AATs for student learning, we sought to better understand how students iteratively modify their programs toward a solution by analyzing more than 45,000 student submissions over 7 semesters in an introductory (CS1) programming course. The resulting metrics allowed us to study what steps students took toward solutions for programming assignments. This paper considers the incremental changes students make and the correlating score between sequential submissions, measured by metrics including source lines of code, cyclomatic (McCabe) complexity, state space, and the 6 Halstead measures of complexity of the program. We demonstrate the value of throttling and show that generating software metrics for analysis can serve to help instructors better guide student learning.
AB - As automated tools for grading programming assignments become more widely used, it is imperative that we better understand how students are utilizing them. Other researchers have provided helpful data on the role automated assessment tools (AATs) have played in the classroom. In order to investigate improved practices in using AATs for student learning, we sought to better understand how students iteratively modify their programs toward a solution by analyzing more than 45,000 student submissions over 7 semesters in an introductory (CS1) programming course. The resulting metrics allowed us to study what steps students took toward solutions for programming assignments. This paper considers the incremental changes students make and the correlating score between sequential submissions, measured by metrics including source lines of code, cyclomatic (McCabe) complexity, state space, and the 6 Halstead measures of complexity of the program. We demonstrate the value of throttling and show that generating software metrics for analysis can serve to help instructors better guide student learning.
KW - Automated assessment tools
KW - Automated feedback
KW - Computer aided instruction
KW - Computer science education
UR - http://www.scopus.com/inward/record.url?scp=84942436966&partnerID=8YFLogxK
U2 - 10.1145/2676723.2677279
DO - 10.1145/2676723.2677279
M3 - Conference contribution
AN - SCOPUS:84942436966
T3 - SIGCSE 2015 - Proceedings of the 46th ACM Technical Symposium on Computer Science Education
SP - 410
EP - 415
BT - SIGCSE 2015 - Proceedings of the 46th ACM Technical Symposium on Computer Science Education
A2 - Decker, Adrienne
A2 - Eiselt, Kurt
A2 - Tims, Jodi
A2 - Alphonce, Carl
PB - Association for Computing Machinery
T2 - 46th SIGCSE Technical Symposium on Computer Science Education, SIGCSE 2015
Y2 - 4 March 2015 through 7 March 2015
ER -