pith. sign in

arxiv: 2605.23823 · v1 · pith:PFKZKUQBnew · submitted 2026-05-22 · 💻 cs.HC

"I can't read your mind": A Study of Neurodivergent Computing Students' Experiences with Collaborative Active Learning

Cynthia Zastudil , Srishty Muthusekaran , Rayhona Nasimova , Stephen MacNeil This is my paper

Pith reviewed 2026-05-25 03:06 UTC · model grok-4.3

classification 💻 cs.HC
keywords neurodivergent studentscollaborative active learningcomputing educationautismADHDteam dynamicsassignment structureaccessibility
0
0 comments X

The pith

Neurodivergent computing students report discomfort with unstructured collaborative assignments and prefer smaller teams with explicit roles.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper surveyed 24 neurodivergent computing students who are autistic or have ADHD, plus 20 neurotypical peers, and interviewed four neurodivergent students. Neurodivergent participants described discomfort when assignments lack clear structure or carry ambiguous expectations. They indicated stronger comfort with smaller teams that meet frequently and use explicitly defined roles. Interviews surfaced coping approaches such as students choosing their own roles and deciding whether to disclose their neurodivergence. The results frame recommendations for making collaborative active learning more accessible through deliberate design choices.

Core claim

A survey of neurodivergent computing students and interviews establish that team dynamics and assignment structure affect comfort, with neurodivergent students expressing discomfort with assignments that lack structure or have ambiguous expectations and preferring smaller teams that work together frequently with explicitly defined roles.

What carries the argument

Survey and interview data on how team size, role definition, and assignment clarity shape neurodivergent students' comfort in collaborative active learning.

If this is right

  • Collaborative assignments benefit from explicit expectations and clear structure to reduce discomfort.
  • Smaller teams that collaborate frequently with assigned roles increase comfort for neurodivergent students.
  • Allowing students to self-select roles offers one practical coping strategy.
  • Options for self-disclosure can help neurodivergent students manage discomfort in group settings.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same structural preferences may appear in collaborative settings outside computing if examined in other fields.
  • Clearer team structures could affect course persistence or completion rates among neurodivergent students.
  • Instructor guidance on implementing defined roles might translate the findings into classroom practice.

Load-bearing premise

The self-reported experiences of the 24 surveyed neurodivergent students accurately reflect broader patterns and can inform design recommendations for all such students.

What would settle it

A larger-scale study in which most neurodivergent computing students report equal or greater comfort with unstructured collaborative tasks than with structured ones would undermine the central claim.

Figures

Figures reproduced from arXiv: 2605.23823 by Cynthia Zastudil, Rayhona Nasimova, Srishty Muthusekaran, Stephen MacNeil.

Figure 1
Figure 1. Figure 1: Percentage distributions of participants’ responses to our Likert-scale survey questions on team dynamics. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
read the original abstract

Computing courses often feature active learning techniques that promote collaboration and social interaction between students. However, neurodivergent students' preferences and experiences with these techniques are not well understood. We conducted a survey of neurodivergent computing students (n=24), specifically autistic students or students with ADHD, and neurotypical computing students (n=20) to understand how the structure of collaborative active learning affects their comfort in computing courses. We also interviewed four computing students on the autism spectrum or with ADHD to gain more contextualized insights into their experiences and accessibility recommendations. Our survey surfaces how team dynamics and assignment structure can impact neurodivergent students' comfort in computing courses. Neurodivergent students expressed discomfort with assignments that lack structure or have ambiguous expectations. Neurodivergent students prefer smaller teams that work together frequently with explicitly defined roles. Our interviews identified ways that neurodivergent students cope with discomfort in collaborative active learning, including self-selecting roles and self-disclosure. While preliminary, our results highlight how instructors can design collaborative active learning to be more equitable and accessible for neurodivergent students.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper claims that through a survey of 24 neurodivergent computing students (autistic or with ADHD) and 20 neurotypical students, along with 4 interviews, neurodivergent students experience discomfort with unstructured or ambiguous collaborative active learning assignments and prefer smaller teams with frequent collaboration and explicitly defined roles. It identifies coping strategies such as self-selecting roles and self-disclosure, and offers preliminary recommendations for designing such activities to be more equitable and accessible.

Significance. If the results hold, this work addresses an important gap in computing education by documenting neurodivergent students' experiences with active learning techniques. The mixed-methods design (survey plus interviews) provides direct student-reported preferences and contextual coping mechanisms that can inform instructor practices. The explicit acknowledgment that results are preliminary is a strength.

major comments (1)
  1. [Abstract and survey description] Abstract and survey description: The design recommendations for collaborative active learning (more structure, smaller teams, explicit roles) are framed as applicable to neurodivergent computing students generally, but are based on self-reported data from a self-selected n=24 sample. No response rate, recruitment method, power analysis, or selection-bias checks are described, which limits support for extrapolating the reported preferences beyond the sample even as preliminary findings.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback on the scope and framing of our preliminary findings. We address the single major comment below and will revise the manuscript accordingly to strengthen clarity around sample limitations.

read point-by-point responses

  1. Referee: The design recommendations for collaborative active learning (more structure, smaller teams, explicit roles) are framed as applicable to neurodivergent computing students generally, but are based on self-reported data from a self-selected n=24 sample. No response rate, recruitment method, power analysis, or selection-bias checks are described, which limits support for extrapolating the reported preferences beyond the sample even as preliminary findings.

    Authors: We agree that greater transparency is needed. The manuscript already qualifies all recommendations as preliminary (abstract and discussion), but we will revise the methods section to explicitly describe recruitment (online neurodivergent computing communities and university accessibility services) and add a dedicated limitations subsection addressing self-selection, potential bias, and the lack of a calculable response rate due to anonymous distribution. No formal power analysis was performed because the study is exploratory mixed-methods rather than confirmatory; we will state this rationale. These revisions will ensure the framing remains tied to the sample without overgeneralization. revision: partial

Circularity Check

0 steps flagged

No circularity: direct reporting of survey and interview data

full rationale

The paper contains no derivations, equations, fitted parameters, predictions, or mathematical claims. All findings are presented as direct summaries of self-reported survey responses (n=24 neurodivergent) and interview insights (n=4). No self-citation chains, ansatzes, uniqueness theorems, or renamings of known results appear. The central claims rest on empirical participant data rather than reducing to inputs by construction. This is a standard qualitative HCI study with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on empirical data collection using standard qualitative methods; no free parameters, new entities, or non-standard axioms are introduced.

axioms (1)
  • domain assumption Participants provide accurate self-reports of their experiences and preferences.
    The study depends on honest responses from the surveyed and interviewed students.

pith-pipeline@v0.9.0 · 5739 in / 1106 out tokens · 30434 ms · 2026-05-25T03:06:39.594670+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

54 extracted references · 54 canonical work pages · 1 internal anchor

  1. [1]

    Kevin M Antshel and Natalie Russo. 2019. Autism spectrum disorders and ADHD: Overlapping phenomenology, diagnostic issues, and treatment considerations. Current psychiatry reports21, 5 (2019). https://doi.org/10.1007/s11920-019-1020-5

    work page doi:10.1007/s11920-019-1020-5 2019

  2. [2]

    Sheena K Au-Yeung, Megan Freeth, and Andrew R Thompson. 2025. ‘Am I gonna regret this?’: The experiences of diagnostic disclosure in autistic adults.Autism (2025). https://doi.org/10.1177/13623613251337504

    work page doi:10.1177/13623613251337504 2025

  3. [3]

    Sander Begeer, Saloua El Bouk, Wafaa Boussaid, et al. 2009. Underdiagnosis and referral bias of autism in ethnic minorities.Journal of autism and developmental disorders39, 1 (2009). https://doi.org/10.1007/s10803-008-0611-5

    work page doi:10.1007/s10803-008-0611-5 2009

  4. [4]

    Andrew Begel, James Dominic, Conner Phillis, et al. 2021. How a remote video game coding camp improved autistic college students’ self-efficacy in communi- cation. InProceedings of the 52nd ACM Technical Symposium on Computer Science Education. ACM. https://doi.org/10.1145/3408877.3432516

    work page doi:10.1145/3408877.3432516 2021

  5. [5]

    Like a Nesting Doll

    Seth Bernstein, Paul Denny, Juho Leinonen, et al. 2024. "Like a Nesting Doll": Analyzing Recursion Analogies Generated by CS Students Using Large Language Models. InProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 1. ACM. https://doi.org/10.1145/3649217.3653533

    work page doi:10.1145/3649217.3653533 2024

  6. [6]

    Intersectionality Variable

    Rachel N Bonnette, Samuel Abramovich, Adrienne Decker, and Gregory A Fabi- ano. 2025. The Need for an “Intersectionality Variable”: Examining Differences in Challenging Experiences of Multiply Marginalised Neurodivergent Students in Higher Education STEM Programs.International Journal of Disability, Devel- opment and Education(2025). https://doi.org/10.10...

    work page doi:10.1080/1034912x.2025.2571758 2025

  7. [7]

    1991.Active learning: Creating excitement in the classroom

    Charles C Bonwell and James A Eison. 1991.Active learning: Creating excitement in the classroom. 1991 ASHE-ERIC higher education reports.ERIC

    work page 1991

  8. [8]

    Valeria Borsotti, Andrew Begel, and Pernille Bjørn. 2024. Neurodiversity and the accessible university: Exploring organizational barriers, access labor and opportunities for change.Proceedings of the ACM on Human-Computer Interaction 8, CSCW1 (2024). https://doi.org/10.1145/3641011

    work page doi:10.1145/3641011 2024

  9. [9]

    Virginia Braun and Victoria Clarke. 2019. Reflecting on reflexive thematic analysis. Qualitative research in sport, exercise and health11, 4 (2019). https://doi.org/10. 1080/2159676X.2019.1628806 Neurodivergent Computing Students’ Experiences with Collaborative Active Learning ITiCSE 2026, July 10–15, 2026, Madrid, Spain

    work page arXiv 2019

  10. [10]

    rightful presence

    Angela Calabrese Barton and Edna Tan. 2020. Beyond equity as inclusion: A framework of “rightful presence” for guiding justice-oriented studies in teach- ing and learning.Educational researcher49, 6 (2020). https://doi.org/10.3102/ 0013189X20927363

    work page 2020

  11. [11]

    Lynn Clouder, Mehmet Karakus, Alessia Cinotti, et al. 2020. Neurodiversity in higher education: a narrative synthesis.Higher Education80, 4 (2020). https: //doi.org/10.1007/s10734-020-00513-6

    work page doi:10.1007/s10734-020-00513-6 2020

  12. [12]

    OPEN CODING. 2004. Anselm L. Strauss and Juliet Corbin.Social Research Methods: A Reader(2004)

    work page 2004

  13. [13]

    Francesco Craig, Anna Linda Lamanna, Francesco Margari, et al. 2015. Overlap between autism spectrum disorders and attention deficit hyperactivity disorder: searching for distinctive/common clinical features.Autism research8, 3 (2015). https://doi.org/10.1002/aur.1449

    work page doi:10.1002/aur.1449 2015

  14. [14]

    Mary Anne L Egan. 2005. Students with Asperger’s syndrome in the CS class- room. InProceedings of the 36th SIGCSE technical symposium on computer science education. ACM. https://doi.org/10.1145/1047344.1047369

    work page doi:10.1145/1047344.1047369 2005

  15. [15]

    Cisneros, Jason Lee Weber, Wendy Sanka, Karen Phan, Jennifer Wong-Ma, and Stacy M

    Isabela Figueira, Josahandi M. Cisneros, Jason Lee Weber, Wendy Sanka, Karen Phan, Jennifer Wong-Ma, and Stacy M. Branham. 2026. Where are the Disabled Students?: A Literature Review of Disability Inclusion in Computing Education Research. InProceedings of the 57th ACM Technical Symposium on Computer Science Education V.1. https://doi.org/10.1145/3770762.3772561

    work page doi:10.1145/3770762.3772561 2026

  16. [16]

    Logan E Gin, Frank A Guerrero, et al. 2020. Is active learning accessible? Exploring the process of providing accommodations to students with disabilities.CBE—Life Sciences Education19, 4 (2020). https://doi.org/10.1187/cbe.20-03-0049

    work page doi:10.1187/cbe.20-03-0049 2020

  17. [17]

    Xiaoshan Zhu Gordy, Elizabeth O Carr, Lei Zhang, and Jessica H Bailey. 2020. A Multi-Disciplinary Mixed-Methods Study of Group Dynamics in Active Learning Space.Journal of the Scholarship of Teaching and Learning20, 3 (2020)

    work page 2020

  18. [18]

    Tyler Greer, Qiang Hao, et al. 2019. On the Effects of Active Learning Environ- ments in Computing Education. InProceedings of the 50th ACM Technical Sympo- sium on Computer Science Education. https://doi.org/10.1145/3287324.3287345

    work page doi:10.1145/3287324.3287345 2019

  19. [19]

    Jessica Rose Hirst, Eleni Akrida, and Steven Bradley. 2025. Neurodivergence in Computing Education: Prevalence and Future Research Directions. InProceedings of the 25th Koli Calling International Conference on Computing Education Research. ACM. https://doi.org/10.1145/3769994.3770053

    work page doi:10.1145/3769994.3770053 2025

  20. [20]

    Sins Invalid. 2017. Skin, tooth, and bone–the basis of movement is our people: a disability justice primer

    work page 2017

  21. [21]

    Mara Kirdani-Ryan and Amy J. Ko. 2024. Neurodivergent Legitimacy in Comput- ing Spaces.ACM TOCE24, 4 (2024). https://doi.org/10.1145/3690651

    work page doi:10.1145/3690651 2024

  22. [22]

    It Broadens My Mind

    Varsha Koushik and Shaun K Kane. 2019. "It Broadens My Mind" Empowering People with Cognitive Disabilities through Computing Education. InProceedings of the 2019 CHI conference on human factors in computing systems. ACM. https: //doi.org/10.1145/3290605.3300744

    work page doi:10.1145/3290605.3300744 2019

  23. [23]

    Celine Latulipe, Stephen MacNeil, and Brian Thompson. 2018. Evolving a Data Structures Class Toward Inclusive Success. In2018 IEEE Frontiers in Education Conference (FIE). 1–9. https://doi.org/10.1109/FIE.2018.8659334

    work page doi:10.1109/fie.2018.8659334 2018

  24. [24]

    Celine Latulipe, Andrew Rosen, Audrey Rorrer, et al. 2025. Investigating the Im- pact of Classroom Structure, Sociality, and Inclusivity on Student Perceptions of Mastery. InProceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1. ACM. https://doi.org/10.1145/3724363.3729082

    work page doi:10.1145/3724363.3729082 2025

  25. [25]

    Christopher Luchs. 2021. Considering neurodiversity in learning design and technology.TechTrends65, 6 (2021). https://doi.org/10.1007/s11528-021-00667-9

    work page doi:10.1007/s11528-021-00667-9 2021

  26. [26]

    Barbara J Lutz and Barbara J Bowers. 2005. Disability in everyday life.Qualitative Health Research15, 8 (2005). https://doi.org/10.1177/1049732305278631

    work page doi:10.1177/1049732305278631 2005

  27. [27]

    accessibility research

    Kelly Mack, Emma McDonnell, Dhruv Jain, et al . 2021. What do we mean by “accessibility research”? A literature survey of accessibility papers in CHI and ASSETS from 1994 to 2019. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems. ACM. https://doi.org/10.1145/3411764.3445412

    work page doi:10.1145/3411764.3445412 2021

  28. [28]

    Monica M McGill, Sarah Heckman, Christos Chytas, et al . 2023. Conducting sound, equity-enabling computing education research. InProceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education. ACM. https://doi.org/10.1145/3623762.3633495

    work page doi:10.1145/3623762.3633495 2023

  29. [29]

    David McKinney, Chloe Morton, Brenda Tuohy, et al. 2024. Iterative Design of a Socially-Relevant and Engaging Middle School Data Science Unit. InProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1. ACM. https://doi.org/10.1145/3626252.3630886

    work page doi:10.1145/3626252.3630886 2024

  30. [30]

    Rachel Michaela Mettenbrink and Stephen Cooper. 2025. Prevalence of Autism Spectrum Characteristics in Students Taking Undergraduate Computing Courses. J. Comput. Sci. Coll.41, 1 (Dec. 2025). https://doi.org/10.5555/3778123.3778137

    work page doi:10.5555/3778123.3778137 2025

  31. [31]

    Joel Michael. 2006. Where’s the evidence that active learning works?Advances in physiology education(2006). https://doi.org/10.1152/advan.00053.2006

    work page doi:10.1152/advan.00053.2006 2006

  32. [32]

    Renee A Monson. 2019. Do they have to like it to learn from it? Students’ experiences, group dynamics, and learning outcomes in group research projects. Teaching Sociology47, 2 (2019). https://doi.org/10.1177/0092055X18812549

    work page doi:10.1177/0092055x18812549 2019

  33. [33]

    Sukanya Kannan Moudgalya, Chris Mayfield, Aman Yadav, et al. 2021. Measuring Students’ Sense of Belonging in Introductory CS Courses. InProceedings of the 52nd ACM Technical Symposium on Computer Science Education. https: //doi.org/10.1145/3408877.3432425

    work page doi:10.1145/3408877.3432425 2021

  34. [34]

    Dinah Murray, Damian Milton, Jonathan Green, et al. 2023. The human spectrum: A phenomenological enquiry within neurodiversity.Psychopathology56, 3 (2023). https://doi.org/10.1159/000526213

    work page doi:10.1159/000526213 2023

  35. [35]

    Yong-Hwee Nah and Hillary Ler-Lee Lim. 2025. Preferences for Identity-First Versus Person-First Language in a Sample of University Students with Special Educational Needs (SEN) in Singapore.Journal of Autism and Developmental Disorders(2025), 1–5

    work page 2025

  36. [36]

    Kaia Newman, Sarah Snay, Madeline Endres, et al. 2025. Disclosure of Neurodi- vergence in Software Workplaces: a Mixed Methods Study of Forum and Survey Perspectives. InProceedings of the 27th International ACM SIGACCESS Conference on Computers and Accessibility. ACM. https://doi.org/10.1145/3663547.3746334

    work page doi:10.1145/3663547.3746334 2025

  37. [37]

    Courtenay Frazier Norbury and Alison Sparks. 2013. Difference or disorder? Cultural issues in understanding neurodevelopmental disorders.Developmental psychology49, 1 (2013). https://doi.org/10.1037/a0027446

    work page doi:10.1037/a0027446 2013

  38. [38]

    Alannah Oleson, Benjamin Xie, Jean Salac, et al. 2022. A decade of demographics in computing education research: A critical review of trends in collection, report- ing, and use. InProceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1. ACM. https://doi.org/10.1145/3501385.3543967

    work page doi:10.1145/3501385.3543967 2022

  39. [39]

    Zacharoula Papamitsiou, Michail Giannakos, Simon, et al . 2020. Computing education research landscape through an analysis of keywords. InProceedings of the 2020 ACM conference on international computing education research. ACM. https://doi.org/10.1145/3372782.3406276

    work page doi:10.1145/3372782.3406276 2020

  40. [40]

    Mary Pilotte and Diana Bairaktarova. 2016. Autism spectrum disorder and engineering education-needs and considerations. In2016 ieee frontiers in education conference (fie). IEEE. https://doi.org/10.1109/FIE.2016.7757566

    work page doi:10.1109/fie.2016.7757566 2016

  41. [41]

    2022.Unmasking autism: Discovering the new faces of neurodiversity

    Devon Price. 2022.Unmasking autism: Discovering the new faces of neurodiversity

    work page 2022

  42. [42]

    Becker, et al

    Shamima Nasrin Runa, Anna Markella Antoniadi, Brett A. Becker, et al . 2023. Student Sense of Belonging: The Role of Gender Identity and Minoritisation in Computing and Other Sciences. InProceedings of the 25th Australasian Computing Education Conference. ACM. https://doi.org/10.1145/3576123.3576133

    work page doi:10.1145/3576123.3576133 2023

  43. [43]

    Not a cookie cutter situation

    Sophia Salvatore, Claudia White, and Stephen Podowitz-Thomas. 2024. “Not a cookie cutter situation”: how neurodivergent students experience group work in their STEM courses.International Journal of STEM Education11, 1 (2024). https://doi.org/10.1186/s40594-024-00508-0

    work page doi:10.1186/s40594-024-00508-0 2024

  44. [44]

    Kate Sanders, Jonas Boustedt, Anna Eckerdal, et al. 2017. Folk pedagogy: Nobody doesn’t like active learning. InProceedings of the 2017 ACM Conference on Inter- national Computing Education Research. https://doi.org/10.1145/3105726.3106192

    work page doi:10.1145/3105726.3106192 2017

  45. [45]

    Andrew Sears and Vicki Hanson. 2011. Representing users in accessibility re- search. InProceedings of the SIGCHI conference on Human factors in computing systems. ACM. https://doi.org/10.1145/1978942.1979268

    work page doi:10.1145/1978942.1979268 2011

  46. [46]

    Kate Seers and Rachel C Hogg. 2021. ’You don’t look autistic’: A qualitative exploration of women’s experiences of being the ‘autistic other’.Autism25, 6 (2021). https://doi.org/10.1177/1362361321993722

    work page doi:10.1177/1362361321993722 2021

  47. [47]

    disabled people

    Ather Sharif, Aedan Liam McCall, and Kianna Roces Bolante. 2022. Should I say “disabled people” or “people with disabilities”? Language preferences of disabled people between identity-and person-first language. InProceedings of the 24th international ACM SIGACCESS conference on computers and accessibility. https://doi.org/10.1145/3517428.3544813

    work page doi:10.1145/3517428.3544813 2022

  48. [48]

    Moushumi Sharmin, Jordan Archer, Adrian Heffelman, et al . 2024. Towards Understanding the Challenges, Needs, and Opportunities Pertaining to Assess- ment Techniques for Autistic College Students in Computing. In2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE. https://doi.org/10.1109/COMPSAC61105.2024.00122

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1109/compsac61105.2024.00122 2024

  49. [49]

    Prateek Shekhar, Maura Borrego, Matt DeMonbrun, et al . 2020. Negative student response to active learning in STEM classrooms: A systematic re- view of underlying reasons.Journal of College Science Teaching49, 6 (2020). https://doi.org/10.1080/0047231X.2020.12290664

    work page doi:10.1080/0047231x.2020.12290664 2020

  50. [50]

    Dara Shifrer and Daniel Mackin Freeman. 2021. Problematizing perceptions of STEM potential: Differences by cognitive disability status in high school and postsecondary educational outcomes.Socius7 (2021). https://doi.org/10.1177/ 2378023121998116

    work page 2021

  51. [51]

    Sylvia Stuurman, Harrie JM Passier, Frédérique Geven, and Erik Barendsen. 2019. Autism: Implications for inclusive education with respect to software engineering. InProceedings of the 8th Computer Science Education Research Conference. ACM. https://doi.org/10.1145/3375258.3375261

    work page doi:10.1145/3375258.3375261 2019

  52. [52]

    Sri Yash Tadimalla, Mary Lou Maher, Audrey Rorrer, et al. 2025. Connecting the Dots: Intersectionality across Active Learning, Classroom Climate, and Introduc- tory Computer Science Courses. InProceedings of the 56th ACM Technical Sym- posium on Computer Science Education. https://doi.org/10.1145/3641554.3701930

    work page doi:10.1145/3641554.3701930 2025

  53. [53]

    Cynthia Zastudil, David H Smith IV, Yusef Tohamy, et al. 2025. Neurodiversity in Computing Education Research: A Systematic Literature Review. InProceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1. ACM. https://doi.org/10.1145/3724363.3729088

    work page doi:10.1145/3724363.3729088 2025

  54. [54]

    Annuska Zolyomi, Anne Spencer Ross, Arpita Bhattacharya, et al. 2018. Values, identity, and social translucence: Neurodiverse student teams in higher education. InProceedings of the 2018 chi conference on human factors in computing systems. ACM. https://doi.org/10.1145/3173574.3174073

    work page doi:10.1145/3173574.3174073 2018