research-article

Adaptive Parsons Problems as Active Learning Activities During Lecture

Authors:

Barbara Ericson,

Carl Haynes-MagyarAuthors Info & Claims

ITiCSE '22: Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1

Pages 290 - 296

https://doi.org/10.1145/3502718.3524808

Published: 07 July 2022 Publication History

Abstract

Adaptive Parsons problems could be used to reduce the difficulty of introductory programming courses and increase the use of active learning in lecture. Parsons problems provide mixed-up code blocks that must be placed in order. In adaptive Parsons problems, if a learner is struggling to solve a problem it can dynamically be made easier. This makes it possible for students to correctly solve a problem in a limited time, even if they are struggling. Previous research on the effectiveness and efficiency of solving Parsons problems for learning has been conducted in controlled conditions or in lab/discussion. We tested the efficiency of solving adaptive Parsons problems versus writing the equivalent code as lecture assignments through three between-subjects experiments. The median time to solve each Parsons problem was less than the median time to write the equivalent code for all but two of the problems, both with complex conditionals. However, that difference was significant for only six of the 10 problems. Our hypothesis for why two problems had a higher median time to solve as a Parsons problem than as a write code problem was that the problem instructions did not match the Parsons problem solution and/or they also had a large number of possible correct solutions. Results from student surveys also provided evidence that most students (78%) find solving adaptive Parsons problems in lecture helpful for their learning, but that some (36.2%) would rather write the code themselves. These findings have implications for how to best use Parsons problems.

References

[1]

Sasha Barab and Kurt Squire. 2004. Design-based research: Putting a stake in the ground. The journal of the learning sciences 13, 1 (2004), 1--14.

[2]

Lecia Barker, Christopher Lynnly Hovey, and Jane Gruning. 2015. What influences CS faculty to adopt teaching practices?. In Proceedings of the 46th ACM Technical Symposium on Computer Science Education. 604--609.

Digital Library

[3]

Klara Benda, Amy Bruckman, and Mark Guzdial. 2012. When Life and Learning Do Not Fit: Challenges of Workload and Communication in Introductory Computer Science Online. Trans. Comput. Educ. 12, 4, Article 15 (Nov. 2012), 38 pages. https://doi.org/10.1145/2382564.2382567

Digital Library

[4]

Jens Bennedsen and Michael Caspersen. 2007. Failure rates in introductory programming. ACM SIGCSE Bulletin 39, 2 (2007), 32--36.

Digital Library

[5]

Jens Bennedsen and Michael E Caspersen. 2019. Failure rates in introductory programming: 12 years later. ACM Inroads 10, 2 (2019), 30--36.

Digital Library

[6]

Laura E. Berk and Adam Winsler. 1995. Scaffolding Children's Learning: Vygotsky and Early Childhood Education. National Association for the Education of Young Children.

[7]

Dennis Bouvier, Ellie Lovellette, John Matta, Jing Bai, Jacqueline Chetty, Stan Kurkovsky, and JiaWan. 2019. Factors Affecting the Adoption of Peer Instruction in Computing Courses. In Proceedings of the Working Group Reports on Global Computing Education. 1--25.

Digital Library

[8]

Michelene TH Chi. 2009. Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in cognitive science 1, 1 (2009), 73--105.

[9]

Michelene TH Chi and RuthWylie. 2014. The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational psychologist 49, 4 (2014), 219--243.

[10]

Robert Coe. 2002. It's the effect size, stupid: What effect size is and why it is important. (2002).

[11]

Gemma Corbalan, Liesbeth Kester, and Jeroen JG Van Merriënboer. 2008. Selecting learning tasks: Effects of adaptation and shared control on learning efficiency and task involvement. Contemporary Educational Psychology 33, 4 (2008), 733--756.

[12]

Paul Denny, AndrewLuxton-Reilly, and Beth Simon. 2008. Evaluating a newexam question: Parsons problems. In Proceedings of the fourth international workshop on computing education research. 113--124.

Digital Library

[13]

Yuemeng Du, Andrew Luxton-Reilly, and Paul Denny. 2020. A Review of Research on Parsons Problems. In Proceedings of the Twenty-Second Australasian Computing Education Conference. 195--202.

Digital Library

[14]

Jacquelynne Eccles. 2009. Who am I and what am I going to do with my life? Personal and collective identities as motivators of action. Educational Psychologist 44, 2 (2009), 78--89.

[15]

Barbara Ericson, Austin McCall, and Kathryn Cunningham. 2019. Investigating the affect and effect of adaptive parsons problems. In Proceedings of the 19th Koli Calling International Conference on Computing Education Research. 1--10.

Digital Library

[16]

Barbara J Ericson, James D Foley, and Jochen Rick. 2018. Evaluating the efficiency and effectiveness of adaptive parsons problems. In Proceedings of the 2018 ACM Conference on International Computing Education Research. 60--68.

Digital Library

[17]

Barbara J Ericson, Mark J Guzdial, and Briana B Morrison. 2015. Analysis of interactive features designed to enhance learning in an ebook. In Proceedings of the Eleventh Annual International Conference on International Computing Education Research. 169--178.

Digital Library

[18]

Barbara J Ericson, Lauren E Margulieux, and Jochen Rick. 2017. Solving parsons problems versus fixing and writing code. In Proceedings of the 17th Koli Calling International Conference on Computing Education Research. 20--29.

Digital Library

[19]

Lawrence M. Fisher. 2016. Booming Enrollments. Commun. ACM 59, 7 (June 2016), 17--18. https://doi.org/10.1145/2933418

Digital Library

[20]

Scott Freeman, Sarah L Eddy, Miles McDonough, Michelle K Smith, Nnadozie Okoroafor, Hannah Jordt, and Mary Pat Wenderoth. 2014. Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences 111, 23 (2014), 8410--8415.

[21]

Richard R Hake. 1998. Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American journal of Physics 66, 1 (1998), 64--74.

[22]

Jennifer Hammond and Pauline Gibbons. 2005. What is scaffolding. Teachers' voices 8 (2005), 8--16.

[23]

Carl C. Haynes and Barbara J. Ericson. 2021. Problem-Solving Efficiency and Cognitive Load for Adaptive Parsons Problems vs. Writing the Equivalent Code. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (Yokohama, Japan) (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 60, 15 pages. https://doi.org/10.1145/3411764.3445292

Digital Library

[24]

Juha Helminen, Petri Ihantola, Ville Karavirta, and Lauri Malmi. 2012. How do students solve parsons programming problems? an analysis of interaction traces. In Proceedings of the ninth annual international conference on International computing education research. 119--126.

Digital Library

[25]

Petri Ihantola and Ville Karavirta. 2011. Two-dimensional parson's puzzles: The concept, tools, and first observations. Journal of Information Technology Education 10, 2 (2011), 119--132.

[26]

Ville Karavirta, Juha Helminen, and Petri Ihantola. 2012. A mobile learning application for parsons problems with automatic feedback. In Proceedings of the 12th Koli Calling International Conference on Computing Education Research. 11--18.

Digital Library

[27]

Paivi Kinnunen and Beth Simon. 2010. Experiencing programming assignments in CS1: the emotional toll. In Proceedings of the Sixth international workshop on Computing education research. 77--86.

Digital Library

[28]

Nancy Kober. 2015. Reaching students: What research says about effective instruction in undergraduate science and engineering. National Academies Press.

[29]

Briana B Morrison, Lauren E Margulieux, Barbara Ericson, and Mark Guzdial. 2016. Subgoals help students solve Parsons problems. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education. 42--47.

Digital Library

[30]

Nadim Nachar et al. 2008. The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribution. Tutorials in quantitative Methods for Psychology 4, 1 (2008), 13--20.

[31]

Fred Paas, Tamara Van Gog, and John Sweller. 2010. Cognitive load theory: New conceptualizations, specifications, and integrated research perspectives. Educational psychology review 22, 2 (2010), 115--121.

[32]

Dale Parsons and Patricia Haden. 2006. Parson's programming puzzles: a fun and effective learning tool for first programming courses. In Proceedings of the 8th Australasian Conference on Computing Education-Volume 52. 157--163.

Digital Library

[33]

Andrew Petersen, Michelle Craig, and Daniel Zingaro. 2011. Reviewing CS1 exam question content. In Proceedings of the 42nd ACM technical symposium on Computer science education. 631--636.

Digital Library

[34]

Anthony V Robins. 2019. Novice programmers and introductory programming. The Cambridge Handbook of Computing Education Research, Cambridge Handbooks in Psychology (2019), 327--376.

[35]

John Ruscio. 2008. A probability-based measure of effect size: Robustness to base rates and other factors. Psychological methods 13, 1 (2008), 19.

[36]

David Williamson Shaffer and Mitchel Resnick. 1999. " Thick" Authenticity: New Media and Authentic Learning. Journal of interactive learning research 10, 2 (1999), 195--216.

Digital Library

[37]

John Sweller, Paul L Ayres, Slava Kalyuga, and Paul Chandler. 2003. The expertise reversal effect. (2003).

[38]

John Sweller, Jeroen JG van Merriënboer, and Fred Paas. 2019. Cognitive architecture and instructional design: 20 years later. Educational Psychology Review (2019), 1--32.

[39]

Jeroen JG Van Merriënboer and Marcel BM De Croock. 1992. Strategies for computer-based programming instruction: Program completion vs. program generation. Journal of Educational Computing Research 8, 3 (1992), 365--394.

[40]

Jeroen JG Van Merriënboer, Paul A Kirschner, and Liesbeth Kester. 2003. Taking the load off a learner's mind: Instructional design for complex learning. Educational psychologist 38, 1 (2003), 5--13.

[41]

Jeroen JG Van Merrienboer and John Sweller. 2005. Cognitive load theory and complex learning: Recent developments and future directions. Educational psychology review 17, 2 (2005), 147--177.

[42]

Christopher Watson and Frederick WB Li. 2014. Failure rates in introductory programming revisited. In Proceedings of the 2014 conference on Innovation & technology in computer science education. ACM, 39--44.

Digital Library

[43]

Rui Zhi, Min Chi, Tiffany Barnes, and Thomas W Price. 2019. Evaluating the Effectiveness of Parsons Problems for Block-based Programming. In Proceedings of the 2019 ACM Conference on International Computing Education Research. 51-- 59.

Digital Library

Cited By

Hou XWu ZWang XEricson BJoyner DKim MWang XXia M(2024)CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning ProgrammingProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662032(51-62)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3662032
Ericson BPearce JRodger SCsizmadia AGarcia RGutierrez FLiaskos KPadiyath AScott MSmith DWarriem JZavaleta Bernuy ALaakso MMonga MSimon Sheard J(2023)Multi-Institutional Multi-National Studies of Parsons ProblemsProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633498(57-107)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3623762.3633498
Reeves BSarsa SPrather JDenny PBecker BHellas AKimmel BPowell GLeinonen JLaakso MMonga MSimon Sheard J(2023)Evaluating the Performance of Code Generation Models for Solving Parsons Problems With Small Prompt VariationsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588805(299-305)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588805
Show More Cited By

Index Terms

Adaptive Parsons Problems as Active Learning Activities During Lecture
1. Applied computing
  1. Education

Recommendations

Parsons Problems and Beyond: Systematic Literature Review and Empirical Study Designs
ITiCSE-WGR '22: Proceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education

Programming is a complex task that requires the development of many skills including knowledge of syntax, problem decomposition, algorithm development, and debugging. Code-writing activities are commonly used to help students develop these skills, but ...
Evaluating the Efficiency and Effectiveness of Adaptive Parsons Problems
ICER '18: Proceedings of the 2018 ACM Conference on International Computing Education Research

Practice is essential for learning. There is evidence that solving Parsons problems (putting mixed up code blocks in order) is a more efficient, but just as effective, form of practice than writing code from scratch. However, not all students ...
Using Adaptive Parsons Problems to Scaffold Write-Code Problems
ICER '22: Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1

In this paper, we explore using Parsons problems to scaffold novice programmers who are struggling while solving write-code problems. Parsons problems, in which students put mixed-up code blocks in order, can be created quickly and already serve ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

ITiCSE '22: Proceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 1

July 2022

686 pages

ISBN:9781450392013

DOI:10.1145/3502718

General Chairs:
Brett A. Becker
University College Dublin, Ireland
,
Keith Quille
Technological University Dublin, Ireland
,
Mikko-Jussi Laakso
University of Turku, Finland
,
Program Chairs:
Erik Barendsen
Radboud University & Open University, The Netherlands
,
Simon
Unaffiliated, Australia

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]

Sponsors

SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ITiCSE 2022

Sponsor:

SIGCSE

ITiCSE 2022: Innovation and Technology in Computer Science Education

July 8 - 13, 2022

Dublin, Ireland

Acceptance Rates

Overall Acceptance Rate 552 of 1,613 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
180
Total Downloads

Downloads (Last 12 months)49
Downloads (Last 6 weeks)3

Reflects downloads up to 30 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Hou XWu ZWang XEricson BJoyner DKim MWang XXia M(2024)CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning ProgrammingProceedings of the Eleventh ACM Conference on Learning @ Scale10.1145/3657604.3662032(51-62)Online publication date: 9-Jul-2024
https://dl.acm.org/doi/10.1145/3657604.3662032
Ericson BPearce JRodger SCsizmadia AGarcia RGutierrez FLiaskos KPadiyath AScott MSmith DWarriem JZavaleta Bernuy ALaakso MMonga MSimon Sheard J(2023)Multi-Institutional Multi-National Studies of Parsons ProblemsProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3623762.3633498(57-107)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3623762.3633498
Reeves BSarsa SPrather JDenny PBecker BHellas AKimmel BPowell GLeinonen JLaakso MMonga MSimon Sheard J(2023)Evaluating the Performance of Code Generation Models for Solving Parsons Problems With Small Prompt VariationsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588805(299-305)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3587102.3588805
Bunde DButler ZHovey CTaylor C(2023)Conversations: Conversations with a Prominent Propagator: Barbara EricsonACM Inroads10.1145/358309114:1(16-19)Online publication date: 21-Feb-2023
https://dl.acm.org/doi/10.1145/3583091
Ericson BDenny PPrather JDuran RHellas ALeinonen JMiller CMorrison BPearce JRodger SQuille KAlshaigy BBecker BQuille KLaakso M(2022)Parsons Problems and BeyondProceedings of the 2022 Working Group Reports on Innovation and Technology in Computer Science Education10.1145/3571785.3574127(191-234)Online publication date: 27-Dec-2022
https://dl.acm.org/doi/10.1145/3571785.3574127

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents