TY - GEN
T1 - Customizing Feedback for Introductory Programming Courses Using Semantic Clusters
AU - Marin, Victor J.
AU - Hosseini, Hadi
AU - Rivero, Carlos R.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The number of introductory programming learners is increasing worldwide. Delivering feedback to these learners is important to support their progress; however, traditional methods to deliver feedback do not scale to thousands of programs. We identify several opportunities to improve a recent data-driven technique to analyze individual program statements. These statements are grouped based on their semantic intent and usually differ on their actual implementation and syntax. The existing technique groups statements that are semantically close, and considers outliers those statements that reduce the cohesiveness of the clusters. Unfortunately, this approach leads to many statements to be considered outliers. We propose to reduce the number of outliers through a new clustering algorithm that processes vertices based on density. Our experiments over six real-world introductory programming assignments show that we are able to reduce the number of outliers and, therefore, increase the total coverage of the programs that are under evaluation.
AB - The number of introductory programming learners is increasing worldwide. Delivering feedback to these learners is important to support their progress; however, traditional methods to deliver feedback do not scale to thousands of programs. We identify several opportunities to improve a recent data-driven technique to analyze individual program statements. These statements are grouped based on their semantic intent and usually differ on their actual implementation and syntax. The existing technique groups statements that are semantically close, and considers outliers those statements that reduce the cohesiveness of the clusters. Unfortunately, this approach leads to many statements to be considered outliers. We propose to reduce the number of outliers through a new clustering algorithm that processes vertices based on density. Our experiments over six real-world introductory programming assignments show that we are able to reduce the number of outliers and, therefore, increase the total coverage of the programs that are under evaluation.
UR - http://www.scopus.com/inward/record.url?scp=85112336705&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85112336705&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80421-3_30
DO - 10.1007/978-3-030-80421-3_30
M3 - Conference contribution
AN - SCOPUS:85112336705
SN - 9783030804206
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 279
EP - 285
BT - Intelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings
A2 - Cristea, Alexandra I.
A2 - Troussas, Christos
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Intelligent Tutoring Systems, ITS 2021
Y2 - 7 June 2021 through 11 June 2021
ER -