Abstract
Deaf and hard of hearing students often lag behind their hearing peers in STEM classes, in part because of a lack of STEM learning resources available in sign language. Past research shows the benefits of embodied cognition through iconic gestures for hearing students. We investigated whether signed lessons that emphasized connections to English or to concepts supported embodied learning of STEM topics. In Study 1, we developed and validated pairs of lessons in two signing styles: English-motivated (EM) and concept-motivated (CM). In Study 2, we compared learning from those two signing styles. Participants’ scores increased from pre- to post-test, indicating learning, but there were no differences based on signing style. However, when we examined participants’ signed summaries, we found that increased production of CM signs, but not EM signs, was related to higher post-test scores. This result suggests that the benefits from embodied learning emerge when learners produce the concept-motivated signs themselves.
Data availability
Due to the small nature of the DDBHH communities, the researchers are not able to publicly share all behavioral data collected to protect participant confidentiality and privacy. Supplementary information, an appendix of materials, and preprint versions of this manuscript are available through the Open Science Framework through PsyArXiv: osf.io/djwqe. Researchers should contact the corresponding author to request access to raw data or data that includes demographic information. We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study. This study’s design and its analysis were not pre-registered.
Code availability
The R code used for analysis will also be available through the Open Science Framework osf.io/djwqe. The code was written using R 4.4.1 (2024-06-14).
References
US Department of Education. National Assessment of Educational Progress (NAEP): 2022 Mathematics Assessment. National Center for Education Statistics (2022).
US Department of Education. National Assessment of Educational Progress (NAEP): 2019 Science Assessment. National Center for Education Statistics (2019).
US Department of Education. National Assessment of Educational Progress (NAEP): 2018 Technology and Engineering Literacy Assessment. National Center for Education Statistics (2018).
Raven, S. & Whitman, G. M. Science in silence: how educators of the deaf and hard-of-hearing teach science. Res. Sci. Educ. 49, 1001–1012 (2019).
Spencer, P. E. & Marschark, M. Achievement in mathematics and science in Evidence-Based Practice in Educating Deaf and Hard-of-Hearing Students 135–151 (Oxford University Press, 2010).
Ali Shakroum, M., Wai Wong, K., Chun Che Fung, L. et al. The effectiveness of the Gesture-Based Learning System (GBLS) and its impact on learning experience. J. Inf. Technol. Educ. Res. 15, 191–210 (2016).
Alibali, M. W. et al. Students learn more when their teacher has learned to gesture effectively. Gesture 13, 210–233 (2013).
Chu, M. & Kita, S. The nature of gestures’ beneficial role in spatial problem solving. J. Exp. Psychol. Gen. 140, 102–116 (2011).
Rueckert, L., Church, R. B., Avila, A. & Trejo, T. Gesture enhances learning of a complex statistical concept. Cogn. Res. Princ. Implic. 2, 2 (2017).
Lualdi, C. P., Spiecker, B., Wooten, A. K. & Clark, K. Advancing scientific discourse in American Sign Language. Nat. Rev. Mater. 1–6 (2023).
Solomon, C. M. Challenges in developing STEM sign language for inclusive education. Nat. Hum. Behav. https://doi.org/10.1038/s41562-024-01993-7. (2024).
Hrastinski, I. & Wilbur, R. B. Academic achievement of deaf and hard-of-hearing students in an ASL/English bilingual program. J. Deaf Stud. Deaf Educ. 21, 156–170 (2016).
Santos, S. & Cordes, S. Math abilities in deaf and hard of hearing children: The role of language in developing number concepts. Psychol. Rev. 129, 199–211 (2022).
Walker, K., Carrigan, E. & Coppola, M. Deaf children’s number mapping skills: later language exposure, not deafness, explains delays in Proceedings of the 2021 AERA Annual Meeting 1–11 (AERA, Virtual, 2021).
Gottardis, L., Nunes, T. & Lunt, I. A synthesis of research on deaf and hearing children’s mathematical achievement. Deaf. Educ. Int. 13, 131–150 (2011).
Cawthon, S., Barker, E., Daniel, J., Cooc, N. & Vielma, A. Longitudinal models of reading and mathematics achievement in deaf and hard of hearing students. J. Deaf Stud. Deaf Educ. 28, 115–123 (2023).
Kurz, C., Spiecker, B. & Reis, J. Ideologies and attitudes toward American Sign Language: processes of academic language and academic vocabulary coinage in Sign Language Ideologies in Practice (eds Kusters, A., Green, M., Moriarty, E. & Snoddon, K.) 287–308 (De Gruyter, 2020).
Chua, M. et al. Does ‘affordance’ mean ‘thing-inform’?: case studies in seeing engineering meaning differently through the process of technical ASL vocabulary creation. Proc. ASEE Annual Conference & Exposition 1–23 (ASEE, 2019).
Kurz, K. B., Schick, B. & Hauser, P. C. Deaf children’s science content learning in direct instruction versus interpreted instruction. J. Sci. Educ. Stud. Disabil. 18, 23–37 (2015).
Kurz, K. & Kiselgof, D. STEM and assessment resources from NTID Sign Language Assessment and Resources Center. Presented at Global Year of STEM Sign Language Summit (2023).
Lang, H. G., LaPorta Hupper, M., Brown, S. W., Babb, I. & Scheifele, P. M. Study of technical signs in science: Implications for lexical database development. J. Deaf Stud. Deaf Educ. 12, 65–79 (2006).
Marzano, R. J. & Rogers, K. Vocabulary for the New Science Standards. (Marzano Research, s.l, 2012).
Bigham, J. P. et al. ASL-STEM Forum: a bottom-up approach to enabling American Sign Language to grow in STEM fields. Proc. AAAI Conf. Artif. Intell. 176–177 (AAAI Press, 2008).
Clark, K. et al. Sign language incorporation in chemistry education (SLICE): Building a lexicon to support the understanding of organic chemistry. J. Chem. Educ. 99, 122–128 (2022).
Reis, J. E., Solovey, E., Henner, J., Johnson, K. & Hoffmeister, R. ASL Clear: STEM education tools for deaf students. ASSETS 15 Proc. 17th Int. ACM SIGACCESS Conf. Comput. Access. 441, 442 (2015).
Rugh, Michael. S. et al. STEM Language can be the Stem of the Problem. 2018 IEEE Frontiers in Education Conference (FIE) 1–7 (IEEE, 2018).
Hayes, J. C. & Kraemer, D. J. M. Grounded understanding of abstract concepts: The case of STEM learning. Cogn. Res. Princ. Implic. 2, 7 (2017).
Shapiro, L. & Stolz, S. A. Embodied cognition and its significance for education. Theory Res. Educ. 17, 19–39 (2019).
Hyatt, K. J. Brain Gym®: Building stronger brains or wishful thinking?. Remedial Spec. Educ. 28, 117–124 (2007).
Petrick, C. J. Every body move: learning mathematics through embodied actions. PhD Thesis at the University of Texas at Austin (2012).
Nathan, M. J. et al. Building cohesion across representations: a mechanism for STEM integration. J. Eng. Educ. 102, 77–116 (2013).
Valenzeno, L., Alibali, M. W. & Klatzky, R. Teachers’ gestures facilitate students’ learning: a lesson in symmetry. Contemp. Educ. Psychol. 28, 187–204 (2003).
Goldin-Meadow, S., Nusbaum, H., Kelly, S. D. & Wagner, S. Explaining math: gesturing lightens the load. Psychol. Sci. 12, 516–522 (2001).
Ping, R. et al. Unpacking the gestures of chemistry learners: what the hands tell us about correct and incorrect conceptions of stereochemistry. Discourse Process 58, 213–232 (2021).
Yu, Y. & Uttal, D. H. Gestures, embodiment, and learning the rate of change. Math. Think. Learn. 24, 203–229 (2022).
Zhang, I.(Y. unyi), Givvin, K. B., Sipple, J. M., Son, J. Y. & Stigler, J. W. Instructed hand movements affect students’ learning of an abstract concept from video. Cogn. Sci. 45, e12940 (2021).
Breckinridge Church, R. & Goldin-Meadow, S. The mismatch between gesture and speech as an index of transitional knowledge. Cognition 23, 43–71 (1986).
Goldin-Meadow, S., Shield, A., Lenzen, D., Herzig, M. & Padden, C. The gestures ASL signers use tell us when they are ready to learn math. Cognition 123, 448–453 (2012).
Pine, K. J., Lufkin, N. & Messer, D. More gestures than answers: children learning about balance. Dev. Psychol. 40, 1059–1067 (2004).
Kontra, C., Lyons, D. J., Fischer, S. M. & Beilock, S. L. Physical experience enhances science learning. Psychol. Sci. 26, 737–749 (2015).
Scott, J. & Cohen, S. Multilingual, multimodal, and multidisciplinary: deaf students and translanguaging in content area classes. Languages 8, 55 (2023).
OpenAI. ChatGPT https://chat.openai.com (2024).
Mathis, W. S., Zhao, S., Pratt, N., Weleff, J. & De Paoli, S. Inductive thematic analysis of healthcare qualitative interviews using open-source large language models: How does it compare to traditional methods?. Comput. Methods Prog. Biomed. 255, 108356 (2024).
R. Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).
Bates, D., Maechler, M., Bolker, B. & Walker, S. Linear mixed-effects models using ‘Eigen’ and S4. J. Stat. Softw. 67, 1–48 (2015).
Sehyr, Z. S. & Emmorey, K. The perceived mapping between form and meaning in American Sign Language depends on linguistic knowledge and task: evidence from iconicity and transparency judgments. Lang. Cogn. 11, 208–234 (2019).
Wheeler, M. A. & Roediger, H. L. Disparate effects of repeated testing: reconciling Ballard’s (1913) and Bartlett’s (1932). Results Psychol. Sci. 3, 240–245 (1992).
Haber, J., Xu, H. & Priya, K. Harnessing virtual reality for management training: a longitudinal study. Organ. Manag. J. 20, 93–106 (2023).
Imashev, A., Kydyrbekova, A., Oralbayeva, N., Kenzhekhan, A. & Sandygulova, A. Learning sign language with mixed reality applications - the exploratory case study with deaf students. Educ. Inf. Technol. 29, 17261–17292 (2024).
GoReact. GoReact https://get.goreact.com (2025).
Qualtrics. Qualtrics (2025).
Bloom, B. S., Engelhart, M. D., Furst, E. J., Hill, W. H. & Krathwohl, D. R. Taxonomy of Educational Objectives: The Classification of Educational Goals Vol. 1 (Longman, 1986).
Peirce, J. W. et al. PsychoPy2: Experiments in behavior made easy. Behav. Res. Methods 51, 195–203 (2019).
Crasborn, O. & Sloetjes, H. Enhanced ELAN functionality for sign language corpora. Proc. 6th Int. Conf. Lang. Resour. Eval. (LREC 2008).
Gamer, M., Lemon, J. & Singh, P. irr: Various coefficients of interrater reliability and agreement. R package version 0.84.1 https://CRAN.R-project.org/package=irr (2022).
Acknowledgements
The authors wish to acknowledge the advice and contributions of colleagues who provided advice and support, including Dr. Colin Lualdi, Dr. Bradley White, Taylor Delorme, Bridget Lam, Autumn Bissett, and Kimberly MacLeod. The authors also acknowledge the deaf content experts who provided feedback on our videos and the participants who completed these studies. We are grateful for their willingness to contribute to research; this work would not be possible without their contribution. The authors wish to acknowledge the DHH and hearing participants who completed this study. The authors wish to acknowledge the funding support for this work: This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. 2444847 awarded to C.K. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Funding for the project was provided by Gallaudet University, including the Gallaudet Seed Fund.
Author information
Authors and Affiliations
Contributions
R.P. designed and conceptualized the study, supervised data collection, analysis design, and primary writing of the manuscript, and edits on manuscript drafts. R.S. also designed and conceptualized the study, designed the tasks, was responsible for data collection, data cleaning, statistical analyses, and writing and feedback on the manuscript. C.K. was responsible for collaborating on task design and feedback, writing, and feedback on the manuscript. T.G. and K.M. were involved with task design and development, data collection, and behavioral coding analysis. L.Q. and A.W. were responsible for collaboration on study concept, task design, feedback on various parts of the study, and manuscript feedback.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Sortino, R., Kim, C., Guettler, T. et al. Producing concept-motivated signs supports learning of STEM in American Sign Language. npj Sci. Learn. (2026). https://doi.org/10.1038/s41539-026-00418-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41539-026-00418-6