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Using Common Problem Sets to Increase Student Engagement and Retention in CS2

Published: 28 June 2017 Publication History

Abstract

Data Structures ranks as one of the most challenging courses in our program core curriculum. It has the steepest learning curve for our students, and the lowest retention rate. A persistent problem we face in teaching data structures is finding time and a mechanism to cover the mathematical concepts that are necessary for understanding the important aspects of the course. The authors addressed the challenge with various approaches over several years including: reorganization of the discrete mathematics I (MA220) and discrete mathematics II (MA290); reordering coverage of topics in courses, changing pre-requisites, changing credit hours, requiring students to submit weekly blogs, developing common problem sets to be used in both the MA220 and CS242, and providing peer-assisted learning sessions. In this poster presentation, we share our integrated pedagogical approach, and the benefits and shortcomings of various approaches we tried over the years. We share the results of a student survey we developed to assess our latest approach of using common problem sets in both courses. The survey results show that by using common problem sets, students had the opportunity to make connections not only between these two courses, but also between how what is being learned in the classroom fits into a broader scope of learning.

References

[1]
Jeffrey Stone, Using Reflective Blogs for Pedagogical Feedback in CS1, in Proceedings of the SIGCSE technical Symposium on Computer Science Education (SIGCSE '12), ACM, USA, 259 -- 264.
[2]
Fekete, A., Kay, J., Kingston, J. and Wimalaratne, K. 2000. Supporting reflection in introductory computer science. In Proceedings of the thirty-first SIGCSE technical symposium on Computer science education (SIGCSE '00), Susan Haller (Ed.). ACM, New York, NY, USA, 144--148.
[3]
Parker, A. 2014. What makes Big-O analysis difficult: understanding how students understand runtime analysis. In Journal of Computing Sciences in Colleges, Volume 29, Number 4, 164--174.

Cited By

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  • (2022)Predicting Student Success in CS2Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499276(140-146)Online publication date: 22-Feb-2022

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Published In

cover image ACM Conferences
ITiCSE '17: Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education
June 2017
412 pages
ISBN:9781450347044
DOI:10.1145/3059009
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 June 2017

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Author Tags

  1. cs2
  2. curriculum issues
  3. instruction
  4. pedagogy
  5. student engagement
  6. student retention

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ITiCSE '17
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ITiCSE '17 Paper Acceptance Rate 56 of 175 submissions, 32%;
Overall Acceptance Rate 552 of 1,613 submissions, 34%

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ITiCSE '25
Innovation and Technology in Computer Science Education
June 27 - July 2, 2025
Nijmegen , Netherlands

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  • (2022)Predicting Student Success in CS2Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499276(140-146)Online publication date: 22-Feb-2022

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