Lightweight, early identification of at-risk CS1 students

Soohyun Nam Liao, Daniel Zingaro, Michael A. Laurenzano, William G. Griswold, Leo Porter

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

21 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research
PublisherAssociation for Computing Machinery, Inc
Pages123-131
Number of pages9
ISBN (Electronic)9781450344494
DOIs
StatePublished - Aug 25 2016
Externally publishedYes
Event12th Annual International Computing Education Research Conference, ICER 2016 - Melbourne, Australia
Duration: Sep 8 2016Sep 12 2016

Publication series

NameICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research

Conference

Conference12th Annual International Computing Education Research Conference, ICER 2016
CountryAustralia
CityMelbourne
Period9/8/169/12/16

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Software
  • Education

Keywords

  • CS1
  • Clickers
  • Peer Instruction
  • Prediction

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  • Cite this

    Liao, S. N., Zingaro, D., Laurenzano, M. A., Griswold, W. G., & Porter, L. (2016). Lightweight, early identification of at-risk CS1 students. In ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research (pp. 123-131). (ICER 2016 - Proceedings of the 2016 ACM Conference on International Computing Education Research). Association for Computing Machinery, Inc. https://doi.org/10.1145/2960310.2960315