skip to main content
10.1145/3478431.3499408acmconferencesArticle/Chapter ViewAbstractPublication PagessigcseConference Proceedingsconference-collections
research-article

Retrieval-based Teaching Incentivizes Spacing and Improves Grades in Computer Science Education

Published:22 February 2022Publication History

ABSTRACT

Desirable difficulties such as retrieval practice (testing) and spacing (distributed studying) are shown to improve long-term learning. Despite their knowledge about the benefits of retrieval practice, students struggle with application. We propose a mechanism of embedding desirable difficulties in the classroom called "retrieval-based teaching." We define it as asking students many ungraded, granular questions in class. We hypothesized that this method could motivate students to (1) study more and (2) increase the spacing of their studying. We tested these two hypotheses through a quasi-experiment in an introductory programming course. We compared 684 students' granular activities with an interactive eBook between the class discussion sections where the intervention was implemented and the control discussion sections. Over four semesters, there were a total of 17 graduate student instructors (GSIs) that taught the discussion sections. Each semester, there were five discussion sections, each taught by a distinct GSI. Only one of the five per semester implemented the treatment in their discussion section(s) by dedicating most of the class time for retrieval-based teaching. Our analysis of these data collected over four consecutive semesters shows that retrieval-based teaching motivated students to space their studying over an average of 3.78 more days, but it did not significantly increase the amount they studied. Students in the treatment group earned an average of 2.36 percentage points higher in course grades. Our mediation analysis indicates that spacing was the main factor in increasing the treated students' grades.

Skip Supplemental Material Section

Supplemental Material

SIGCSE22V1-fp602v.mp4

mp4

236.8 MB

References

  1. Abeer AlJarrah, Michael K Thomas, and Mohamed Shehab. 2018. Investigating temporal access in a flipped classroom: procrastination persists. International Journal of Educational Technology in Higher Education , Vol. 15, 1 (2018), 1.Google ScholarGoogle ScholarCross RefCross Ref
  2. David P Ausubel. 1966. Ego development among segregated Negro children. In Mental Health and Segregation . Springer, 33--40.Google ScholarGoogle Scholar
  3. Katharina Barzagar Nazari and Mirjam Ebersbach. 2018. Distributed Practice: Rarely Realized in Self-Regulated Mathematical Learning. Frontiers in Psychology (2018).Google ScholarGoogle Scholar
  4. Robert A Bjork and Ted W Allen. 1970. The spacing effect: Consolidation or differential encoding? Journal of Verbal Learning and Verbal Behavior , Vol. 9, 5 (1970), 567--572.Google ScholarGoogle ScholarCross RefCross Ref
  5. A. C. Butler, J. D. Karpicke, and H. L. III Roediger. 2008. Correcting a metacognitive error: Feedback increases retention of low-confidence correct responses. Journal of Experimental Psychology: Learning, Memory, and Cognition , Vol. 34, 4 (2008), 918--928.Google ScholarGoogle ScholarCross RefCross Ref
  6. Nicholas J Cepeda, Harold Pashler, Edward Vul, John T Wixted, and Doug Rohrer. 2006. Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological bulletin , Vol. 132, 3 (2006), 354.Google ScholarGoogle Scholar
  7. Frank N Dempster. 1988. The spacing effect: A case study in the failure to apply the results of psychological research. American Psychologist , Vol. 43, 8 (1988), 627.Google ScholarGoogle ScholarCross RefCross Ref
  8. Barbara Ericson and Bradley Miler. 2020. Using and Customizing Ebooks for Computing Courses with Runestone Interactive. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education . 1395--1395.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Barbara J Ericson, Iman YeckehZaare, and Mark J Guzdial. 2019. Runestone Interactive Ebooks: A Research Platform for Online Computer Science Learning. In Proceedings of SPLICE Workshop in the International Computing Education Research Conference (ICER '19), August 12--14, 2019, Toronto, ON, Canada. ACM.Google ScholarGoogle Scholar
  10. Joseph R Ferrari. 1994. Dysfunctional procrastination and its relationship with self-esteem, interpersonal dependency, and self-defeating behaviors. Personality and Individual Differences , Vol. 17, 5 (1994), 673--679.Google ScholarGoogle ScholarCross RefCross Ref
  11. Marissa K Hartwig and Eric D Malain. 2021. Do students space their course study? Those who do earn higher grades. Learning and Instruction (2021), 101538.Google ScholarGoogle Scholar
  12. Scott R. Hinze and David M. Rapp. 2014. Retrieval (Sometimes) Enhances Learning: Performance Pressure Reduces the Benefits of Retrieval Practice. Applied Cognitive Psychology , Vol. 28, 4 (2014), 597--606.Google ScholarGoogle Scholar
  13. Robin F Hopkins, Keith B Lyle, Jeff L Hieb, and Patricia AS Ralston. 2016. Spaced retrieval practice increases college students' short-and long-term retention of mathematics knowledge. Educational Psychology Review , Vol. 28, 4 (2016), 853--873.Google ScholarGoogle ScholarCross RefCross Ref
  14. Jeffrey D Karpicke and Janell R Blunt. 2011. Retrieval practice produces more learning than elaborative studying with concept mapping. Science , Vol. 331, 6018 (2011), 772--775.Google ScholarGoogle ScholarCross RefCross Ref
  15. Jeffrey D Karpicke and Henry L Roediger. 2008. The critical importance of retrieval for learning. science , Vol. 319, 5865 (2008), 966--968.Google ScholarGoogle Scholar
  16. Ayaan M Kazerouni, Stephen H Edwards, and Clifford A Shaffer. 2017. Quantifying incremental development practices and their relationship to procrastination. In Proceedings of the 2017 ACM Conference on International Computing Education Research. ACM, 191--199.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Kyung Ryung Kim and Eun Hee Seo. 2015. The relationship between procrastination and academic performance: A meta-analysis. Personality and Individual Differences , Vol. 82 (2015), 26--33.Google ScholarGoogle ScholarCross RefCross Ref
  18. Nate Kornell and Robert A Bjork. 2007. The promise and perils of self-regulated study. Psychonomic Bulletin & Review , Vol. 14, 2 (2007), 219--224.Google ScholarGoogle ScholarCross RefCross Ref
  19. Nate Kornell, Alan D. Castel, Teal S. Eich, and Robert A. Bjork. 2010. Spacing as the friend of both memory and induction in young and older adults. Psychology and aging , Vol. 25, 2 (2010), 498.Google ScholarGoogle Scholar
  20. Russell Lenth, Henrik Singmann, Jonathon Love, Paul Buerkner, and Maxime Herve. 2018. Emmeans: Estimated marginal means, aka least-squares means. R package version , Vol. 1, 1 (2018), 3.Google ScholarGoogle Scholar
  21. Keith B Lyle, Campbell R Bego, Robin F Hopkins, Jeffrey L Hieb, and Patricia AS Ralston. 2020. How the amount and spacing of retrieval practice affect the short-and long-term retention of mathematics knowledge. Educational Psychology Review , Vol. 32, 1 (2020), 277--295.Google ScholarGoogle ScholarCross RefCross Ref
  22. Brad Miller and David Ranum. 2014. Runestone interactive: tools for creating interactive course materials. In Proceedings of the first ACM conference on Learning@ scale conference . 213--214.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Margus Pedaste, Mario M"aeots, Leo A Siiman, Ton De Jong, Siswa AN Van Riesen, Ellen T Kamp, Constantinos C Manoli, Zacharias C Zacharia, and Eleftheria Tsourlidaki. 2015. Phases of inquiry-based learning: Definitions and the inquiry cycle. Educational research review , Vol. 14 (2015), 47--61.Google ScholarGoogle Scholar
  24. Fernando Rodriguez, Mariela Rivas, Lani Matsumura, Mark Warschauer, and Brian Sato. 2018. How do students study in STEM courses? Findings from a light-touch intervention and its relevance for underrepresented students. PloS One , Vol. 13, 7 (2018).Google ScholarGoogle ScholarCross RefCross Ref
  25. Fernando Rodriguez, Renzhe Yu, Jihyun Park, Mariela Janet Rivas, Mark Warschauer, and Brian K Sato. 2019. Utilizing learning analytics to map students' self-reported study strategies to click behaviors in STEM courses. In Proceedings of the 9th international conference on learning analytics & knowledge. 456--460.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Henry L Roediger III and Andrew C Butler. 2011. The critical role of retrieval practice in long-term retention. Trends in cognitive sciences , Vol. 15, 1 (2011), 20--27.Google ScholarGoogle Scholar
  27. Henry L Roediger III and Jeffrey D Karpicke. 2006. Test-enhanced learning: Taking memory tests improves long-term retention. Psychological science , Vol. 17, 3 (2006), 249--255.Google ScholarGoogle Scholar
  28. Yves Rosseel. 2012. Lavaan: An R package for structural equation modeling and more. Version 0.5--12 (BETA). Journal of statistical software , Vol. 48, 2 (2012), 1--36.Google ScholarGoogle ScholarCross RefCross Ref
  29. Varshita Sher, Marek Hatala, and Dragan Gavs ević. 2020. Analyzing the consistency in within-activity learning patterns in blended learning. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge. 1--10.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Nicholas C Soderstrom and Robert A Bjork. 2015. Learning versus performance: An integrative review. Perspectives on Psychological Science , Vol. 10, 2 (2015), 176--199.Google ScholarGoogle ScholarCross RefCross Ref
  31. Lisa K Son. 2010. Metacognitive control and the spacing effect. Journal of Experimental Psychology: Learning, Memory, and Cognition , Vol. 36, 1 (2010), 255.Google ScholarGoogle ScholarCross RefCross Ref
  32. Piers Steel. 2007. The nature of procrastination: A meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological bulletin , Vol. 133, 1 (2007), 65.Google ScholarGoogle Scholar
  33. Lev Semenovich Vygotsky. 1980. Mind in society: The development of higher psychological processes .Harvard university press.Google ScholarGoogle Scholar
  34. Christopher A Wolters. 2003. Understanding procrastination from a self-regulated learning perspective. Journal of educational psychology , Vol. 95, 1 (2003), 179.Google ScholarGoogle ScholarCross RefCross Ref
  35. Veronica X Yan, Elizabeth L Bjork, and Robert A Bjork. 2016. On the difficulty of mending metacognitive illusions: A priori theories, fluency effects, and misattributions of the interleaving benefit. Journal of Experimental Psychology: General , Vol. 145, 7 (2016), 918.Google ScholarGoogle ScholarCross RefCross Ref
  36. Iman YeckehZaare, Elijah Fox, Gail Grot, Sean Chen, Claire Walkosak, Kevin Kwon, Annelise Hofmann, Jessica Steir, Olivia McGeough, and Nealie Silverstein. 2021. Incentivized Spacing and Gender in Computer Science Education. In Proceedings of the International Computing Education Research Conference (ICER '21), August 16--19, 2021, Virtual. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Iman YeckehZaare, Gail Grot, Isadora Dimovski, Karlie Pollock, and Elijah Fox. 2022 a. Another Victim of COVID-19: Computer Science Education. In Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2022), March 3--5, 2022, Providence, RI, USA. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Iman YeckehZaare, Victoria Mulligan, and Grace Victoria Ramstad. 2022 b. Semester-level Spacing but Not Procrastination Affected Student Exam Performance. In Proceedings of the Tenth International Conference on Learning Analytics & Knowledge . ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Iman YeckehZaare, Paul Resnick, and Barbara Ericson. 2019. A Spaced, Interleaved Retrieval Practice Tool that is Motivating and Effective. In Proceedings of the International Computing Education Research Conference (ICER '19), August 12--14, 2019, Toronto, ON, Canada. ACM.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Ilker Yengin and Adem Karahoca. 2012. What is Socratic Method? The Analysis of Socratic Method through Self Determination Theory and Unified Learning Model. Global Journal on Technology , Vol. 2 (2012).Google ScholarGoogle Scholar

Index Terms

  1. Retrieval-based Teaching Incentivizes Spacing and Improves Grades in Computer Science Education

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 1
      February 2022
      1049 pages
      ISBN:9781450390705
      DOI:10.1145/3478431

      Copyright © 2022 ACM

      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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 February 2022

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      Overall Acceptance Rate1,595of4,542submissions,35%

      Upcoming Conference

      SIGCSE Virtual 2024

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader