Neurodiverse Programmers and the Accessibility of Parsons Problems: An Exploratory Multiple-Case Study
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Parsons problems are drag-and-drop computer programming puzzles that require learners to place code blocks in the correct order and sometimes indentation. Introductory computer programming instructors use them to teach novice programmers how to code while optimizing problem-solving efficiency and cognitive load. While there is research on the design of Parsons problems for programmers without disabilities and programmers with visual or motor impairments, research regarding their accessibility for programmers with cognitive disabilities is scant. To identify the accessibility barriers and benefits of Parsons problems for neurodiverse programmers, an exploratory multiple-case study was conducted. Participants were asked to read eight chapters of an interactive eBook on Python and to solve Parsons problems. Within-case analyses of 15 retrospective think-aloud interviews with five novice programmers with disabilities led to four recommendations for improving the cognitive accessibility of Parsons problems. For example, programmers with seizure disorders may experience seizures when solving programming problems that require numeric calculations. Hence, creating a range of Parsons problems that do not require mental arithmetic could improve the learning experience for programmers with seizure disorders and those who struggle with mental calculations by lowering their cognitive load. Given this study's qualitative and exploratory approach, it does not offer conclusive, broadly generalizable results. Yet, it reveals detailed and promising avenues for exploration in computing education research that might elude many quantitative techniques.
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These three papers stood out due to the depth of their engagement with the disabled communities they targeted and for how they situated accessibility as an issue of power, identity, and justice, rather than solely one of technical compliance or awareness.
“These papers stood out due to the depth of their engagement with the disabled communities they targeted [22, 29] and for how they situated accessibility as an issue of power, identity, and justice [22, 37], rather than solely one of technical compliance or awareness.”
From Where are the Disabled Students?: A Literature Review of Disability Inclusion in Computing Education Research (2026) · cites this paper as [22]
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Experimental — accuracy not yet measuredOnly three papers in the dataset engaged with disability scholarship as a theoretical framing, specifically critical Disability Studies framings such as a social model of disability approach or a disability justice approach.
“Three papers explicitly engaged with critical Disability Studies framings, specifically taking a social model of disability approach [22, 29] or disability justice approach [37].”
From Where are the Disabled Students?: A Literature Review of Disability Inclusion in Computing Education Research (2026) · cites this paper as [22]
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Five misunderstandings about case-study research
Flyvbjerg, B. (2006) · Qualitative Inquiry · doi:10.1177/1077800405284363 · Cited by 14,266 · see citing works
The ICAP framework: Linking cognitive engagement to active learning outcomes
Chi, M. T. H., & Wylie, R. (2014) · Educational Psychologist · doi:10.1080/00461520.2014.965823 · Cited by 2,911 · see citing works
Case Study Research and Applications
Mizukoshi, K. (2023) · Quarterly Journal of Marketing · doi:10.7222/marketing.2023.045 · Cited by 1,535 · see citing works
Learning to program = learning to construct mechanisms and explanations
Soloway, E. (1986) · CACM · doi:10.1145/6592.6594 · Cited by 615 · see citing works
Neurodiversity in higher education: a narrative synthesis
Clouder, L., Karakus, M., Cinotti, A., Ferreyra, M. V., Fierros, G. A., & Rojo, P. (2020) · Higher Education · doi:10.1007/s10734-020-00513-6 · Cited by 385 · cited 1× · see citing works
“…in cognitive function, behavioral traits, and affect; it is an umbrella term considered a ‘moving target’ that is useful for how it helps us imagine the world from something other than a neurotypical perspective—thereby decentering cognitive norms about, for example, rates of learning [5, 9, 52].”
BackgroundCosijn, E., & Ingwersen, P. (2000) · Information Processing & Management · doi:10.1016/s0306-4573(99)00072-2 · Cited by 252 · see citing works
Tourette Syndrome and Tic Disorders: A Decade of Progress
Swain, J. E., Scahill, L. D., Lombroso, P. J., King, R. A., & Leckman, J. F. (2007) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1097/chi.0b013e318068fbcc · Cited by 238 · see citing works
Garcia, R., Falkner, K. E. K., & Vivian, R. (2018) · Computers & Education · doi:10.1016/j.compedu.2018.05.006 · Cited by 221 · see citing works
Hammer, D., & Berland, L. K. (2013) · Journal of the Learning Sciences · doi:10.1080/10508406.2013.802652 · Cited by 214 · see citing works
Neurodiversity Studies: A New Critical Paradigm
(2020) · doi:10.4324/9780429322297 · Cited by 211 · see citing works
Evaluating a new exam question
Denny, P. C., Luxton-Reilly, A., & Simon, B. (2008) · doi:10.1145/1404520.1404532 · Cited by 203 · see citing works
Distractibility in Attention/Deficit/ Hyperactivity Disorder (ADHD): The Virtual Reality Classroom
Adams, R., Finn, P., Moes, E. J., Flannery, K. A., & Rizzo, A. (2008) · Child Neuropsychology · doi:10.1080/09297040802169077 · Cited by 200 · see citing works
My program is ok – am I? Computing freshmen's experiences of doing programming assignments
Kinnunen, P., & Simon, B. (2012) · Computer Science Education · doi:10.1080/08993408.2012.655091 · Cited by 150 · see citing works
Stack Overflow Developer Survey 2015
Aldrich, C. (2015) · Cited by 139 · see citing works
Note-Taking for Self-Explanation and Problem Solving
Trafton, J. G., & Trickett, S. B. (2001) · Human-Computer Interaction · doi:10.1207/s15327051hci1601_1 · Cited by 81 · see citing works
"For all" in "computer science for all"
Ladner, R. E., & Israel, M. (2016) · Communications of the ACM · doi:10.1145/2971329 · Cited by 79 · see citing works
Wilkins, A. J., Zifkin, B. G., Andermann, F., & McGovern, E. (1982) · Annals of Neurology · doi:10.1002/ana.410110609 · Cited by 78 · see citing works
Mountapmbeme, A., Okafor, O., & Ludi, S. A. (2022) · ACM Transactions on Accessible Computing · doi:10.1145/3507469 · Cited by 66 · see citing works
Improving Instruction of Programming Patterns with Faded Parsons Problems
Weinman, N., Fox, A., & Hearst, M. A. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445228 · Cited by 62 · see citing works
Distractors in Parsons Problems Decrease Learning Efficiency for Young Novice Programmers
Harms, K. J., Chen, J., & Kelleher, C. L. (2016) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/2960310.2960314 · Cited by 60 · see citing works
Ericson, B. J., Denny, P. C., Prather, J. E., Duran, R., Hellas, A., Leinonen, J., Miller, C. S., Morrison, B. B., Pearce, J. L., & Rodger, S. H. (2022) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3571785.3574127 · Cited by 59 · see citing works
The impact of prior experience in an information technology programming course sequence
Holden, E. P., & Weeden, E. (2003) · Conference on Information Technology Curriculum (CITC) · doi:10.1145/947121.947131 · Cited by 59 · see citing works
Snodgrass, M. R., Israel, M., & Reese, G. C. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.04.011 · Cited by 58 · see citing works
Ingvar, D. H., & Nyman, G. E. (1962) · Neurology · doi:10.1212/wnl.12.4.282 · Cited by 58 · see citing works
Evaluating the Effectiveness of Parsons Problems for Block-based Programming
Zhi, R., Chi, M., Barnes, T., & Price, T. W. (2019) · Proceedings of the 2019 ACM Conference on International Computing Education Research · doi:10.1145/3291279.3339419 · Cited by 42 · see citing works
Emotions and programming learning: systematic mapping
Coto, M., Mora, S., Grass, B. E., & Murillo-Morera, J. (2021) · Computer Science Education · doi:10.1080/08993408.2021.1920816 · Cited by 41 · see citing works
Undesirable Difficulty Effects in the Learning of High-Element Interactivity Materials
Chen, O., Castro-Alonso, J. C., PAAS, F. G. W. C., & Sweller, J. (2018) · Frontiers in Psychology · doi:10.3389/fpsyg.2018.01483 · Cited by 32 · see citing works
Haynes-Magyar, C., & Ericson, B. J. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445292 · Cited by 32 · see citing works
Self-citationUsing Adaptive Parsons Problems to Scaffold Write-Code Problems
Hou, X., Ericson, B. J., & Wang, X. (2022) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3501385.3543977 · Cited by 32 · see citing works
Ericson, B. J., & Miller, B. (2020) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3328778.3366950 · Cited by 32 · see citing works
Ehtesham-Ul-Haque, M., Monsur, S. M., & Billah, S. M. (2022) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3526113.3545620 · Cited by 29 · see citing works
Cheng, N., & Harrington, B. (2017) · doi:10.1145/3017680.3017704 · Cited by 26 · see citing works
Koushik, V., & Kane, S. K. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300744 · Cited by 25 · see citing works
Mountapmbeme, A., & Ludi, S. A. (2021) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3441852.3471221 · Cited by 22 · see citing works
Accessible to Whom? Bringing Accessibility to Blocks
Stefik, A., Allee, W., Contreras, G., Kluthe, T., Hoffman, A., Blaser, B., & Ladner, R. (2024) · Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 · doi:10.1145/3626252.3630770 · Cited by 21 · see citing works
Number processing in temporal lobe epilepsy
Delazer, M. (2004) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.2003.023614 · Cited by 18 · see citing works
Hou, X., Ericson, B. J., & Wang, X. (2023) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3631802.3631832 · Cited by 16 · see citing works
Epilepsy in school‐aged children: more than just seizures?
Reilly, C. J., & BALLANTINE, R. (2011) · Support for Learning · doi:10.1111/j.1467-9604.2011.01501.x · Cited by 15 · see citing works
A Systematic Literature Review on Teaching Programming to People with Cognitive Disabilities
de Araujo, E. C. J., & Andrade, W. L. (2021) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie49875.2021.9637361 · Cited by 14 · see citing works
Exploring the Difficulty of Faded Parsons Problems for Programming Education
Fromont, F., Jayamanne, H., & Denny, P. C. (2023) · Australasian Computing Education Conference · doi:10.1145/3576123.3576136 · Cited by 12 · see citing works
Discovering, Autogenerating, and Evaluating Distractors for Python Parsons Problems in CS1
Smith, D. H., & Zilles, C. B. (2023) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3545945.3569801 · Cited by 11 · see citing works
Voice-Enabled Blockly: Usability Impressions of a Speech-driven Block-based Programming System
Okafor, O., & Ludi, S. A. (2022) · International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS) · doi:10.1145/3517428.3550382 · Cited by 10 · see citing works
The Impact of Solving Adaptive Parsons Problems with Common and Uncommon Solutions
Haynes-Magyar, C., & Ericson, B. J. (2022) · Koli Calling International Conference on Computing Education Research · doi:10.1145/3564721.3564736 · Cited by 8 · see citing works
Self-citationUsing the Karplus learning cycle to teach learners with ADHD introductory computer programming
Pilkington, C., & Gelderblom, H. (2010) · African Journal of Research in Mathematics Science and Technology Education · doi:10.1080/10288457.2010.10740674 · Cited by 8 · see citing works
Considering Neurodiversity in Learning Design and Technology
Luchs, C. (2021) · TechTrends · doi:10.1007/s11528-021-00667-9 · Cited by 7 · see citing works
Mack, K., McDonnell, E., Jain, D., Lu Wang, L., E. Froehlich, J., & Findlater, L. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445412 · Cited by 7 · see citing works
Computing in Support of Disciplinary Learning
Silvia, D. W., Caballero, M. D., Finzell, T., Frisbie, R. L. S., Hamerski, P. C., Bolger, E., Castle, S. D., Roca, R., & Tourangeau, P. (2022) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3545947.3573341 · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 11
- Brianna Blaser and Richard E Ladner. 2020. Why is Data on Disability so Hard to Collect and Understand?. In 2020 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT), Vol. 1. ieeexplore.ieee.org, 1-8.
- Robert Chapman. 2020. Defining neurodiversity for research and practice. In Neurodiversity studies. Routledge, 218-220.
- Barbara J Ericson and Bradley N Miller. 2020. Runestone: A Platform for Free, On- line, and Interactive Ebooks. In Proceedings of the 51st ACM Technical Symposium on Computer Science Education (Portland, OR, USA) (SIGCSE '20). Association for Computing Machinery, New York, NY, USA, 1012-1018.
- Terhi Kärpänen. 2021. A Literature Review on Cognitive Accessibility. Stud. Health Technol. Inform. 282 (June 2021), 259-270.
- I Scott MacKenzie. 2017. Fitts' Law. In The Wiley Handbook of Human Computer Interaction. John Wiley & Sons, Ltd, Chichester, UK, 347-370.
- Sharan B Merriam. 1995. N of I?: Issues of Validity and Reliability in. PAACE Journal of lifelong learning 4 (1995), 51-60.
- Lauren R Milne and Richard E Ladner. 2019. Position: Accessible Block-Based Programming: Why and How. In 2019 IEEE Blocks and Beyond Workshop (B&B). ieeexplore.ieee.org, 19-22.
- National Academies of Sciences, Engineering, and Medicine, Division of Behav- ioral and Social Sciences and Education, Board on Science Education, Board on Behavioral, Cognitive, and Sensory Sciences, and Committee on How People Learn II: The Science and Practice of Learning. 2018. How People Learn II: Learners, Contexts, and Cultures. National Academies Press.
- Parsons and Haden. 2006. Parson's programming puzzles: a fun and effective learning tool for first programming courses. of the 8th Australasian Conference on . . . (2006).
- Scott Michael Robertson. 2010. Neurodiversity, Quality of Life, and Autistic Adults: Shifting Research and Professional Focuses onto Real-Life Challenges. Disabil. Stud. Q. 30, 1 (2010).
- Candace Walkington and Matthew L Bernacki. 2019. Personalizing Algebra to Students' Individual Interests in an Intelligent Tutoring System: Moderators of Impact. International Journal of Artificial Intelligence in Education 29, 1 (March 2019), 58-88.
Cited by
Sorted by citations
Neurodiversity in Computing Education Research: A Systematic Literature Review
Zastudil, C., Smith, D. H., Tohamy, Y., Nasimova, R., Montross, G., & MacNeil, S. (2025) · Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1 · doi:10.1145/3724363.3729088 · Cited by 15 · see citing works
Smith, D. H., Poulsen, S., Emeka, C., Wu, Z., Haynes-Magyar, C., & Zilles, C. B. (2024) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3632620.3671114 · Cited by 6 · see citing works
Self-citationFigueira, I., Cisneros, J. M., Weber, J. L., Sanka, W., Phan, K., Wong-Ma, J., & Branham, S. M. (2026) · Proceedings of the 57th ACM Technical Symposium on Computer Science Education V.1 · doi:10.1145/3770762.3772561 · Cited by 3 · see citing works
Perceptions about Teaching Programming in the Neurodiverse Students' Context
de Araujo, E. C. J., Andrade, W. L., & Oliveira, A. L. S. (2024) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie61694.2024.10892904 · Cited by 3 · see citing works
Applying Universal Design for Learning to Undergraduate Computer Science Education
Ghosh, H. (2026) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3770761.3777151
PyPal: Supporting Middle Schoolers with ADHD in Computing Education
Lim, P., Kumar, P., & Gerardo, H. (2026) · Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) · doi:10.1145/3796496.3811785
Accessible Keyboard Controls for Block Ordering Problems
Fowles, P., Stefik, A., Blaser, B., Smith, D. H., & Poulsen, S. (2026) · ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) · doi:10.1145/3803400.3809324
Neurodiverse spacetimemattering in a summer camp among neurodivergent youth: a mixed-methods study
Reutlinger, C., Koro‐Ljungberg, M., Pivovarova, M., Vasquez, A. M., Herneddo, O., & Thorn, S. D. (2026) · Computer Science Education · doi:10.1080/08993408.2026.2728966
Ortega, M. F., de Carvalho, L. S. G., Fernandes, D., & de Holanda, M. T. (2026) · Anais do ... Simpósio Brasileiro de Informática na Educação/Anais do Simpósio Brasileiro de Informática na Educação · doi:10.5753/sbie.2026.27469
- Authors
- Haynes-Magyar, C.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Haynes-Magyar, Carl
- ORCID
- Haynes-Magyar, Carl
- Venue
- Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1
- Publisher
- ACM
- Published
- 2024-03-07
- Pages
- 491-497
- OpenAlex
- W4392542369
- Cited by
- 9
- References
- 47
- Citations by year
- 2026: 6 · 2025: 1 · 2024: 2
- FWCI
- 3.95
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- ACM Technical Symposium on Computer Science Education (SIGCSE) (publishedVersion, cc-by)
- Funders
- University of Michigan (Rackham Graduate Student Research Grant)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Human Factors and Ergonomics › Digital Accessibility for Disabilities
- Social Sciences › Sociology and Political Science › Inclusion and Disability in Education and Sport
- Keywords
- Parsons problems, neurodiversity, cognitive disability, accessibility, novice programmers, cognitive load, epilepsy, mental arithmetic, Python, think-aloud interviews
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