PreSS: Predicting Student Success Early in CS1. A Pilot International Replication and Generalization Study

Keith Quille, Soohyun Nam Liao, Eileen Costelloe, Keith Nolan, Aidan Mooney, Kartik Shah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This work piloted an international replication and generalization study on an existing prediction model called PreSS. PreSS has been developed and validated over nearly two decades and can predict student performance in CS1 with nearly 71% accuracy, at a very early stage in the learning module. Motivated by a prior validation study and its competitive modelling accuracy, we chose PreSS for such an international replication and generalization study. The study took place in two countries, with two institutions in Ireland and one institution in the US, totalling 472 students throughout the academic year 2020-21. In doing so, this study addressed a call from the 2015 ITiCSE working group for the educational data mining and learning analytics community: systematically analyse and verify previous studies using data from multiple contexts to tease out tacit factors that contribute to previously observed outcomes. This pilot study achieved 90% accuracy, which is higher than the prior work's. This encouraging finding sets the foundations for a larger scale international study. This paper describes in detail the pilot replication and generalization study and our progress on the larger scale study which is taking place across six continents.

Original languageEnglish
Title of host publicationITiCSE 2022 - Proceedings of the 27th ACM Conference on Innovation and Technology in Computer Science Education
PublisherAssociation for Computing Machinery
Pages54-60
Number of pages7
ISBN (Electronic)9781450392013
DOIs
Publication statusPublished - 7 Jul 2022
Event27th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2022 - Dublin, Ireland
Duration: 8 Jul 202213 Jul 2022

Publication series

NameAnnual Conference on Innovation and Technology in Computer Science Education, ITiCSE
Volume1
ISSN (Print)1942-647X

Conference

Conference27th ACM Conference on Innovation and Technology in Computer Science Education, ITiCSE 2022
Country/TerritoryIreland
CityDublin
Period8/07/2213/07/22

Keywords

  • computer science education
  • cs1
  • machine learning
  • predicting success
  • programming

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