TY - GEN
T1 - Lightweight, early identification of at-risk CS1 students
AU - Liao, Soohyun Nam
AU - Zingaro, Daniel
AU - Laurenzano, Michael A.
AU - Griswold, William G.
AU - Porter, Leo
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/8/25
Y1 - 2016/8/25
N2 - Being able to identify low-performing students early in the term may help instructors intervene or differently allocate course resources. Prior work in CS1 has demonstrated that clicker correctness in Peer Instruction courses correlates with exam outcomes and, separately, that machine learning models can be built based on early-term programming assessments. This work aims to combine the best elements of each of these approaches. We offer a methodology for creating models, based on in-class clicker questions, to predict cross-term student performance. In as early as week 3 in a 12-week CS1 course, this model is capable of correctly predicting students as being in danger of failing, or not, for 70% of the students, with only 17% of students misclassified as not at-risk when at-risk. Additional measures to ensure more broad applicability of the methodology, along with possible limitations, are explored.
AB - Being able to identify low-performing students early in the term may help instructors intervene or differently allocate course resources. Prior work in CS1 has demonstrated that clicker correctness in Peer Instruction courses correlates with exam outcomes and, separately, that machine learning models can be built based on early-term programming assessments. This work aims to combine the best elements of each of these approaches. We offer a methodology for creating models, based on in-class clicker questions, to predict cross-term student performance. In as early as week 3 in a 12-week CS1 course, this model is capable of correctly predicting students as being in danger of failing, or not, for 70% of the students, with only 17% of students misclassified as not at-risk when at-risk. Additional measures to ensure more broad applicability of the methodology, along with possible limitations, are explored.
KW - CS1
KW - Clickers
KW - Peer Instruction
KW - Prediction
UR - http://www.scopus.com/inward/record.url?scp=85000416324&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85000416324&partnerID=8YFLogxK
U2 - 10.1145/2960310.2960315
DO - 10.1145/2960310.2960315
M3 - Conference contribution
AN - SCOPUS:85000416324
T3 - ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research
SP - 123
EP - 131
BT - ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research
PB - Association for Computing Machinery, Inc
T2 - 12th Annual International Computing Education Research Conference, ICER 2016
Y2 - 8 September 2016 through 12 September 2016
ER -