Customizing Feedback for Introductory Programming Courses Using Semantic Clusters

Victor J. Marin, Hadi Hosseini, Carlos R. Rivero

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings
EditorsAlexandra I. Cristea, Christos Troussas
PublisherSpringer Science and Business Media Deutschland GmbH
Pages279-285
Number of pages7
ISBN (Print)9783030804206
DOIs
StatePublished - 2021
Event17th International Conference on Intelligent Tutoring Systems, ITS 2021 - Virtual, Online
Duration: Jun 7 2021Jun 11 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12677 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Intelligent Tutoring Systems, ITS 2021
CityVirtual, Online
Period6/7/216/11/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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