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Retina: helping students and instructors based on observed programming activities

Published: 04 March 2009 Publication History

Abstract

It is difficult for instructors of CS1 and CS2 courses to get accurate answers to such critical questions as "how long are students spending on programming assignments?", or "what sorts of errors are they making?" At the same time, students often have no idea of where they stand with respect to the rest of the class in terms of time spent on an assignment or the number or types of errors that they encounter. In this paper, we present a tool called Retina, which collects information about students' programming activities, and then provides useful and informative reports to both students and instructors based on the aggregation of that data. Retina can also make real-time recommendations to students, in order to help them quickly address some of the errors they make. In addition to describing Retina and its features, we also present some of our initial findings during two trials of the tool in a real classroom setting.

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Published In

cover image ACM SIGCSE Bulletin
ACM SIGCSE Bulletin  Volume 41, Issue 1
SIGCSE '09
March 2009
553 pages
ISSN:0097-8418
DOI:10.1145/1539024
Issue’s Table of Contents
  • cover image ACM Conferences
    SIGCSE '09: Proceedings of the 40th ACM technical symposium on Computer science education
    March 2009
    612 pages
    ISBN:9781605581835
    DOI:10.1145/1508865
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2009
Published in SIGCSE Volume 41, Issue 1

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Author Tags

  1. compilation errors
  2. cs1
  3. cs2
  4. tutoring systems

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  • (2023)Mining student coding behaviors in a programming MOOC: there are no actionable learner stereotypesEducational Technology Quarterly10.55056/etq.611Online publication date: 26-Oct-2023
  • (2020)Different assignments as different contexts: predictors across assignments and outcome measures in CS12020 Intermountain Engineering, Technology and Computing (IETC)10.1109/IETC47856.2020.9249217(1-6)Online publication date: 2-Oct-2020
  • (2019)Method and Tools for Mapping the Evolution of Programmers during the Development of Computer ProgramsProceedings of the XXXIII Brazilian Symposium on Software Engineering10.1145/3350768.3352731(491-500)Online publication date: 23-Sep-2019
  • (2018)Models for Early Identification of Struggling Novice ProgrammersProceedings of the 49th ACM Technical Symposium on Computer Science Education10.1145/3159450.3159476(699-704)Online publication date: 21-Feb-2018
  • (2018)A Demonstration of Evidence-Based Action Research Using Information Dashboard in Introductory Programming EducationTomorrow's Learning: Involving Everyone. Learning with and about Technologies and Computing10.1007/978-3-319-74310-3_62(619-629)Online publication date: 21-Jan-2018
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  • (2016)Can Interaction Patterns with Supplemental Study Tools Predict Outcomes in CS1?Proceedings of the 2016 ACM Conference on Innovation and Technology in Computer Science Education10.1145/2899415.2899428(236-241)Online publication date: 11-Jul-2016
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