Dyscalculia is a specific learning disability that affects a person’s ability to understand number-based information and arithmetic skills, particularly in children. Misconceptions about dyscalculia persist, but this disability has received significant attention from the research community, particularly in exploring techniques that utilise physiological data (eye tracking, electroencephalogram, electrocardiogram, etc.) to understand the underlying mechanisms of numerical processing. This interest highlights the need for a comprehensive review of this research direction.
Objective
This paper aims to review the application of techniques with physiological data in children with dyscalculia.
Methods
A thorough literature search was conducted using Web of Science, Scopus, Science Direct, and PubMed databases, adhering to PRISMA guidelines. After a rigorous selection process, 11 relevant articles were identified for this study.
Results
Our findings revealed that researchers have long recognised the potential of these techniques, as demonstrated by their use in relevant studies over time. While the number of studies is limited and has focused primarily on eye tracking and electroencephalogram, the review provides detailed analyses that highlight the significant effectiveness of these techniques in assessing numerical performance in children with dyscalculia. Furthermore, we not only investigated the methodologies and results of these techniques but also discussed their limitations and the need for further research. These limitations may be due to small sample sizes or the absence of certain potential research directions that have emerged in studies of other developmental disorders, such as dyslexia and autism.
Conclusions
This paper presents a clear direction for future research on utilising physiological data systems to support students struggling with mathematical learning disabilities.
KaufmannLvon AsterM. The diagnosis and management of dyscalculia. Dtsch Arztebl Int2012; 109(45): 767–777.
4.
SilverLB. The misunderstood child: understanding and coping with your child’s learning disabilities. Harmony, 2010.
5.
SchindlerMSchovenbergVSchabmannA. Enumeration processes of children with mathematical difficulties: an explorative eye-tracking study on subitizing, groupitizing, counting, and pattern recognition. J Learn Disabil2020; 18(2): 193–211.
6.
RoulstoneAMorsanyiKBahnmuellerJ. Performance on curriculum-based mathematics assessments in developmental dyscalculia: the effect of content domain and question format. Psychol Res2024; 88: 1–11.
7.
ShalevRVon AsterM. Identification, classification, and prevalence of developmental dyscalculia. Encyclopedia of language and literacy development2008, published-online.
8.
ShalevRSAuerbachJManorO, et al.Developmental dyscalculia: prevalence and prognosis. Eur Child Adolesc Psychiatr2000; 9: S58–S64.
9.
LanderlK. Development of numerical processing in children with typical and dyscalculic arithmetic skills—a longitudinal study. Front Psychol2013; 4: 459.
10.
WilliamsJWoLLewisS, et al.Mathematics assessment for learning and teaching: an overview of the age standardisation model ages 5-14. In: Proceedings of the British Society for Research into Learning Mathematics. British Society for Research into Learning Mathematics, 2005, pp. 93–98.
11.
WHO. International statistical classification of diseases and related health problems: alphabetical index. World Health Organization, 2004, vol 3.
12.
KhareSKBlanes-VidalVNadimiES, et al.Emotion recognition and artificial intelligence: a systematic review (2014–2023) and research recommendations. Inf Fusion2024; 102: 102019.
13.
BrihadiswaranGHaputhanthriDGunathilakaS, et al.EEG-based processing and classification methodologies for autism spectrum disorder: a review. J Comput Sci2019; 15(8): 1161.
14.
HarunNFHamzahNZainiN, et al.EEG classification analysis for diagnosing autism spectrum disorder based on emotions. J Telecommun Electron Comput Eng2018; 10(1-2): 87–93.
15.
LiJKongXSunL, et al.Identification of autism spectrum disorder based on electroencephalography: a systematic review. Comput Biol Med2024; 170: 108075.
16.
MohammadiMRKhaleghiANasrabadiAM, et al.EEG classification of ADHD and normal children using non-linear features and neural network. Biomed Eng Lett2016; 6: 66–73.
17.
AndrikopoulosDVassiliouGFatourosP, et al.Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study. BMC Psychiatry2024; 24(1): 547.
18.
CoenenLGrünkeMBecker-GenschowS, et al.A systematic review of eye-tracking technology in dyslexia diagnosis. Insights Learn Disabili2024; 21(1): 45–65.
19.
El HmimdiAEKapoulaZSainte Fare GarnotV. Deep learning-based detection of learning disorders on a large scale dataset of eye movement records. BioMedInformatics2024; 4(1): 519–541.
20.
PereraHShiratuddinMFWongKW. Review of EEG-based pattern classification frameworks for dyslexia. Brain Inform2018; 5: 1–14.
21.
MoellerKNeuburgerSKaufmannL, et al.Basic number processing deficits in developmental dyscalculia: evidence from eye tracking. Cogn Dev2009; 24(4): 371–386.
22.
CárdenasSYSilva-PereyraJPrieto-CoronaB, et al.Arithmetic processing in children with dyscalculia: an event-related potential study. PeerJ2021; 9: e10489.
23.
SoltészFSzűcsDDékányJ, et al.A combined event-related potential and neuropsychological investigation of developmental dyscalculia. Neurosci Lett2007; 417(2): 181–186.
24.
PageMJMcKenzieJEBossuytPM, et al.The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. bmj2021; 372: n71.
25.
Van ViersenSSlotEMKroesbergenEH, et al.The added value of eye-tracking in diagnosing dyscalculia: a case study. Front Psychol2013; 4: 679.
26.
van’t NoordendeJEvan HoogmoedAHSchotWD, et al.Number line estimation strategies in children with mathematical learning difficulties measured by eye tracking. Psychol Res2016; 80: 368–378.
27.
SoltészFSzűcsD. An electro-physiological temporal principal component analysis of processing stages of number comparison in developmental dyscalculia. Cogn Dev2009; 24(4): 473–485.
28.
HeineAWißmannJTammS, et al.An electrophysiological investigation of non-symbolic magnitude processing: numerical distance effects in children with and without mathematical learning disabilities. Cortex2013; 49(8): 2162–2177.
29.
WangEQinSChangM, et al.Digital memory encoding in Chinese dyscalculia: an event-related potential study. Res Dev Disabil2015; 36: 142–149.
30.
WangEDuCMaY. Old/new effect of digital memory retrieval in Chinese dyscalculia: evidence from ERP. J Learn Disabil2017; 50(2): 158–167.
31.
LazzaroGBattistiAVaruzzaC, et al.Boosting numerical cognition in children and adolescents with mathematical learning disabilities by a brain-based intervention: a study protocol for a randomized, sham-controlled clinical trial. Int J Environ Res Publ Health2021; 18(20): 10969.
32.
ChaoWWangEYuanT, et al.Characteristics inhibition defects of children with developmental dyscalculia: evidence from the ERP. Front Psychiatr2022; 13: 877651.
33.
PooleABallLJ. Eye tracking in HCI and usability research. In: Encyclopedia of human computer interaction. IGI global, 2006, pp. 211–219.
34.
RaynerK. Eye movements in reading: recent developments. Curr Dir Psychol Sci1993; 2(3): 81–86.
35.
NuraydinSStrickerJUgenS, et al.The number line estimation task is a valid tool for assessing mathematical achievement: a population-level study with 6484 Luxembourgish ninth-graders. J Exp Child Psychol2023; 225: 105521.
36.
BlinowskaKDurkaP. Electroencephalography (eeg). Wiley encyclopedia of biomedical engineering2006; 10: 9780471740360.
37.
AhireNAwaleRWaghA. Development of EEG-based identification of learning disability using machine learning algorithms. In: Data Modelling and Analytics for the Internet of Medical Things. CRC Press, 2022, pp. 141–152.
38.
DehaeneSPiazzaMPinelP, et al.Three parietal circuits for number processing. In: The handbook of mathematical cognition. Psychology Press, 2005, pp. 433–453.
39.
MarlairCCrollenVLochyA. A shared numerical magnitude representation evidenced by the distance effect in frequency-tagging EEG. Sci Rep2022; 12(1): 14559.
40.
RubinstenOTannockR. Mathematics anxiety in children with developmental dyscalculia. Behav Brain Funct2010; 6: 1–13.
41.
MammarellaICHillFDevineA, et al.Math anxiety and developmental dyscalculia: a study on working memory processes. J Clin Exp Neuropsychol2015; 37(8): 878–887.
42.
KucianKZuberIKohnJ, et al.Relation between mathematical performance, math anxiety, and affective priming in children with and without developmental dyscalculia. Front Psychol2018; 9: 318998.
43.
RosselliMMatuteEPintoN, et al.Memory abilities in children with subtypes of dyscalculia. Dev Neuropsychol2006; 30(3): 801–818.
44.
JudicaALucaMDSpinelliD, et al.Training of developmental surface dyslexia improves reading performance and shortens eye fixation duration in reading. Neuropsychol Rehabil2002; 12(3): 177–197.
45.
AblingerIvon HeydenKVorstiusC, et al.An eye movement based reading intervention in lexical and segmental readers with acquired dyslexia. Neuropsychol Rehabil2014; 24(6): 833–867.
46.
PetersJLCrewtherSGMurphyMJ, et al.Action video game training improves text reading accuracy, rate and comprehension in children with dyslexia: a randomized controlled trial. Sci Rep2021; 11(1): 18584.
47.
VandanaJNiraliN. A review of EEG signal analysis for diagnosis of neurological disorders using machine learning. Journal of Biomedical Photonics & Engineering2021; 7(4): 40201.
48.
DewanganAKNPGSSinghBK, et al.Determining dominant EEG channels for classification of LD and Non-LD children using machine learning approach. In: Proceedings of the 2024 13th International Conference on Software and Computer Applications. ACM, 2024, pp. 1–6.
49.
BoslWJTager-FlusbergHNelsonCA. EEG analytics for early detection of autism spectrum disorder: a data-driven approach. Sci Rep2018; 8(1): 6828.
50.
OliveiraJSFrancoFOReversMC, et al.Computer-aided autism diagnosis based on visual attention models using eye tracking. Sci Rep2021; 11(1): 10131.
