Comparing the Effectiveness of Computerized and Non-Computerized Cognitive Training on the Cognitive Load Related to Math Lessons and the Executive Functions of Students with Specific Learning Disabilities in Mathematics

Badrypour , Zahra; Faramarzi , Salar; Sharifi , Ali

Volume 15 - Articles-1404 MEJDS (2025) 15: 70 | Back to browse issues page

Ethics code: IR.UI.REC.1402.138

Mendeley

Zotero

RefWorks

Badrypour Z, Faramarzi S, Sharifi A. Comparing the Effectiveness of Computerized and Non-Computerized Cognitive Training on the Cognitive Load Related to Math Lessons and the Executive Functions of Students with Specific Learning Disabilities in Mathematics. MEJDS 2025; 15 (0) :70-70
URL: http://jdisabilstud.org/article-1-3625-en.html

Comparing the Effectiveness of Computerized and Non-Computerized Cognitive Training on the Cognitive Load Related to Math Lessons and the Executive Functions of Students with Specific Learning Disabilities in Mathematics

Zahra Badrypour¹

, Salar Faramarzi ^*²

, Ali Sharifi³

1- MA Student in Psychology and Education of Children with Special Needs, Department of Psychology and Education of People with Special Needs, Faculty of Education and Psychology, University of Isfahan, Isfahan, Iran
2- Professor, Department of Psychology and Education of People with Special Needs, Faculty of Education and Psychology, University of Isfahan, Isfahan, Iran
3- Assistant Professor, Department of Psychology and Education of Children with Special Needs, Faculty of Education and Psychology, University of Isfahan, Isfahan, Iran

Abstract: (374 Views)

Abstract
Background & Objectives: A Mathematics learning disability is defined as a challenge in learning and acquiring basic mathematical abilities. This disorder is caused by factors such as genetics, heredity, environment, and defects in the central nervous system. Also, among the issues and challenges that can be caused by defects in executive functions and other cognitive processes, cognitive load is mentioned. Due to the problems faced by students with mathematical learning disabilities, there has been an increasing interest in the use of cognitive interventions to address the cognitive challenges experienced by these students in recent years. Computer–based cognitive training is used on digital platforms and software applications to provide interactive and adaptive exercises that can be accessed remotely. These exercises often include real–time feedback and progress tracking features. Non–computer–based cognitive training also includes pencil–and–paper exercises, physical activities, and interactive tasks that target specific cognitive functions. The present study was conducted to compare the effectiveness of computerized and non–computerized cognitive training on cognitive load related to math lessons and executive functions in students with specific learning disabilities in mathematics.
Methods: This study employed a quasi–experimental design with a pretest–posttest approach, comprising two experimental groups and one control group. The sample consisted of 45 female students aged 10 and 11, in the fourth and fifth grades of a public school in Isfahan City, Iran, during the academic year 2023–2024. They were selected via available sampling. After, visiting the school and talking to the teachers of those students who were having difficulty in the performance of mathematics lessons, they were considered and for more accurate diagnosis using the Diagnostic Questionnaire for Specific Learning Disorder (Alizadeh et al., 2023), the KeyMath Diagnostic Arithmetic Test (Price & Rogers, 1981), and the Raven's Progressive Matrices Test (Raven, 2000). The sample group was then randomly placed in two experimental groups and one control group. The inclusion criteria for the subjects in the study were as follows: having a normal IQ (between 90 and 110); receiving a diagnosis of specific learning disorder in mathematics; obtaining a score of less than 85 on the KeyMath Diagnostic Arithmetic Test (Price & Rogers, 1981); studying in the fourth and fifth grades of elementary school; lacking any physical, sensory, motor, or mental problems, and not having any other neurodevelopmental disorders such as autism spectrum disorder and attention deficit/hyperactivity disorder. The exclusion criteria for the subjects from the study were not completing the information related to the questionnaires and tests, unwillingness to cooperate in continuing the interventions, missing more than two sessions of the intervention program, and receiving other cognitive interventions simultaneously with the research. The intervention program consisted of 12 computer–based cognitive training sessions and 12 non–computer–based cognitive training sessions, each lasting 45 minutes and held three times a week. Group 1 participated in non–computer–based cognitive training, Group 2 participated in computer–based cognitive training, and the control group received no intervention. All three groups were evaluated for cognitive load and executive functions in the pretest and posttest stages using the Cognitive Load Questionnaire (Klepsch et al., 2017) and the Behavior Rating Inventory of Executive Function (BRIEF) (Gioia et al., 2000). Data analysis was performed using descriptive statistics (mean and standard deviation) and inferential statistics, as implemented in SPSS software version 24. In the inferential statistics section, analysis of covariance and Bonferroni post hoc test were used. The significance level was set at 0.05.
Results: Regarding executive functions, computer–based cognitive training was more effective than non–computer–based cognitive training in improving executive functions (p=0.004) and its subscales, including attention shifting (p<0.001), working memory (p<0.001), metacognition (p=0.043), and behavior modification (p<0.001). Regarding cognitive load focused on mathematics, computer–based cognitive training was more effective than non–computer–based cognitive training in reducing scores of germane cognitive load (p=0.007) and extraneous cognitive load (p<0.001).
Conclusion: According to the findings, the visual and motivational benefits of computer–based cognitive training suggest that these exercises be included as educational supplements in the curriculum for students with mathematical learning disabilities.

Keywords: Computer–based cognitive training, Non–computer–based cognitive training, Executive functions, Cognitive load, Mathematics learning disabilities

Full-Text [PDF 410 kb] (141 Downloads)

Type of Study: Original Research Article | Subject: Psychology

References

1. Mingozzi A, Tobia V, Marzocchi GM. Dyslexia and dyscalculia: which neuropsychological processes distinguish the two developmental disorders? Child Neuropsychol. 2024;30(1):1–21. [DOI]

2. Casali N, Meneghetti C, Tinti C, Maria Re A, Sini B, Passolunghi MC, et al. Academic achievement and satisfaction among university students with specific learning disabilities: the roles of soft skills and study-related factors. J Learn Disabil. 2024;57(1):16–29. [DOI]

3. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th edition. Arlington, VA: American Psychiatric Association; 2013.

4. National Center for Learning Disabilities. Learning disabilities statistics and information [Internet]; 2021. [Article]

5. Fletcher JM, Grigorenko EL. Neuropsychology of learning disabilities: the past and the future. J Int Neuropsychol Soc. 2017;23(9–10):930–40. [DOI]

6. Rubel LH, McCloskey AV. Contextualization of mathematics: which and whose world? Educ Stud Math. 2021;107(2):383–404. [DOI]

7. Georgitsi M, Dermitzakis I, Soumelidou E, Bonti E. The polygenic nature and complex genetic architecture of specific learning disorder. Brain Sci. 2021;11(5):631. [DOI]

8. Asiaee F, Yamini M, Mahdian H. The Effectiveness of perceptual skills reconstruction program on working memory, perceptual reasoning, and math performance of students with specific math learning disorder. Psychology of Exceptional Individuals. 2018;8(30):133–54. [Persian] [Article]

9. Traverso L, Viterbori P, Usai MC. Effectiveness of an executive function training in italian preschool educational services and far transfer effects to pre-academic skills. Front Psychol. 2019;10:2053. [DOI]

10. Schuiringa H, Van Nieuwenhuijzen M, Orobio De Castro B, Matthys W. Executive functions and processing speed in children with mild to borderline intellectual disabilities and externalizing behavior problems. Child Neuropsychol. 2017;23(4):442–62. [DOI]

11. Pope CN, Bell TR, Stavrinos D. Mechanisms behind distracted driving behavior: the role of age and executive function in the engagement of distracted driving. Accid Anal Prev. 2017;98:123–9. [DOI]

12. Walshe E, Ward McIntosh C, Romer D, Winston F. Executive function capacities, negative driving behavior and crashes in young drivers. Int J Environ Res Public Health. 2017;14(11):1314. [DOI]

13. Jackson SA, Kleitman S, Aidman E. Low cognitive load and reduced arousal impede practice effects on executive functioning, metacognitive confidence and decision making. Plos One. 2014;9(12):e115689. [DOI]

14. Sweller J, Ayres P, Kalyuga S. Altering element interactivity and intrinsic cognitive load. In Sweller J, Ayres P, Kalyuga S; editors. Cognitive load theory. New York: Springer New York; 2011. [DOI]

15. Nili Ahmadabadi MR. Designing instructional model based on cognitive load theory based on qualitative content analysis and internal and external validity. Educational Psychology. 2018;14(49):1–27. [Persian] [Article]

16. Sweller J, Van Merrienboer JJG, Paas FGWC. Cognitive architecture and instructional design. Educ Psychol Rev. 1998;10(3):251–96. [DOI]

17. Figueira PV, Freitas PM. The relationship between math anxiety, working memory, and inhibitory control: a meta-analysis. Bolema. 2020;34(67):678–96. [DOI]

18. Skulmowski A, Xu KM. Understanding cognitive load in digital and online learning: a new perspective on extraneous cognitive load. Educ Psychol Rev. 2022;34(1):171–96. [DOI]

19. Melby-Lervåg M, Redick TS, Hulme C. Working memory training does not improve performance on measures of intelligence or other measures of "far transfer": evidence from a meta-analytic review. Perspect Psychol Sci. 2016;11(4):512–34. [DOI]

20. Kheiry H, Moghtader L, Asadi Majreh S. The effect of cognitive empowerment on processing speed, working memory and visual-motor coordination of children with mathematics learning disorder. Journal of Applied Psychological Research. 2024. [Persian] [Article]

21. Nemati S, Badri-Gargari R, Vahedi S, Mehreganfard-Jirandeh Z. Implementation of cognitive rehabilitation intervention on the comprehension, generation, and calculations of numerical data among students suffering from a specific learning disability and mathematical impairment. Journal of Applied Psychological Research. 2024;15(1):53–72. [Persian] [Article]

22. Alipanah M, Pourmohamadreza-Tajrishi M, Nejati V, Vahedi M. The effectiveness of cognitive rehabilitative program on executive functions in children with dyscalculia. Journal of Rehabilitation. 2022;23(3):352–71. [Persian] [Article]

23. Wiest DJ, Wong EH, Bacon JM, Rosales KP, Wiest GM. The effectiveness of computerized cognitive training on working memory in a school setting. Applied Cognitive Psychology. 2020;34(2):465–71. [DOI]

24. Hawkins RO, Collins T, Hernan C, Flowers E. Using computer-assisted instruction to build math fact fluency: an implementation guide. Interv Sch Clin. 2017;52(3):141–7. [DOI]

25. Alizadeh H, Delavar A, Sadeghian Z, Sharifi A. Development and psychometric properties of DSM-5 Based Diagnostic Questionnaire for Specific Learning Disorder with Specifiers: READING, writing and mathematics (teacher form). Learning Disabilities. 2023;12(2):63–79. [Persian] [Article]

26. Raven J. The Raven's progressive matrices: change and stability over culture and time. J Cogn Psychol. 2000;41(1):1–48. [DOI]

27. Price MF, Rogers B. The Key Math Diagnostic Arithmetic Test. Measurement and Evaluation in Guidance. 1981;14(2):108–11. [DOI]

28. Klepsch M, Schmitz F, Seufert T. Development and validation of two instruments measuring intrinsic, extraneous, and germane cognitive load. Front Psychol. 2017;8:1997. [DOI]

29. Gioia GA, Isquith PK, Guy SC, Kenworthy L. Behavior Rating Inventory of Executive Function. Child Neuropsychology. 2000;6:235–8.

30. Mirmahdi R, Daei Alhosain P, Salehi N. Evaluation of the effectiveness of compound exercise training on executive functions and math performance of students with mathematic learning disorders. Pouyesh Journal. 2021;1400(15):124–44. [Persian] [Article]

31. Mohammadesmaeil E, Hooman HA. Adaptation and standardization of the IRAN KeyMath Test of Mathematics. Journal of Exceptional Children. 2003;2(4):323–32. [Persian] [Article]

32. Hosseini S, Younesi J, Minaei A, Rahimi R. Application of cognitive diagnostic model in determining the underlying cognitive skills of performance in Raven Progressive Matrix Test. Cognitive Strategies in Learning. 2021;9(17):53–69. [Persian] [Article]

33. Burke HR. Raven's progressive matrices: validity, reliability, and norms. J Psychol. 1972;82(2):253–7. [DOI]

34. Rasouli Foshtami A, Hashemi T, Kiamarsi A, Ghaffari A. Determination of psychometric indicators and standardization of Intelligence Test of Children's Raven Colored Progressive Matrices in elementary school students. J Child Ment Health. 2022;9(1):158–75. [Persian] [DOI]

35. Khalili T, Zareie HA, Mirhashmi M. Develop a model for students' academic achievement through executive functions, psychological capital and academic engagement. Medical Journal of Mashhad university of Medical Sciences. 2022;65(5). [Persian] [Article]

36. Nodei Kh, Sarami Gh, Keramati H. The relation between executive function and working memory capacity and students' reading performance: the role of age, sex and intelligence. Journal of Cognitive Psychology. 2016;4(3):11–24. [Persian] [Article]

37. Abdolmohamadi K, Alizadeh H, Ghadiri Sourman Abadi F, Taiebli M, Fathi A. Psychometric Properties of Behavioral Rating Scale of Executive Functions (BRIEF) in Children aged 6 to 12 Years. Quarterly of Educational Measurement. 2018;8(30):135–51. [Persian] [Article]

38. Zahed S, Kareshki H, Roshanghias P. The effect of extraneous cognitive load on cognitive engagement and germane cognitive load of students: the effect of desirable difficulty. Educational Psychology. 2023;19(70):180–201. [Persian] [Article]

39. Zahed S, Dartaj F, Asadzadeh H, Kadivar P, Farokhi N. Structure and validation of the Persian version of the Cognitive Load Questionnaire. Cognitive Psychology. 2020;9(1):39–54. [Persian] [Article]

40. Karimzadeh Sh, Alizadeh E, Ghanbari S. The effectiveness of computer games on students' attention dimensions: a case study of the attention brain game. In: The First National Conference on The Application of Modern Research in The Humanities [Internet]. Ghaem Shahr; 2017. [Persian]

41. Dehghan N, Faramarzi S. Cognitive games as prerequisites for school readiness. Tehran: Yarmana Publications; 2022. [Persian]

42. Chou CP, Bentler PM. Estimates and tests in structural equation modeling. In: Hoyle RH. editor. Structural equation modeling: issues and application. Newbury: SAGE Publications; 1995. p. 37-55.

43. Mattison RE, Mayes SD. Relationships between learning disability, executive function, and psychopathology in children with adhd. J Atten Disord. 2012;16(2):138–46. [DOI]

44. Kaneda M, Osaka N. Role of anterior cingulate cortex during semantic coding in verbal working memory. Neurosci Lett. 2008;436(1):57–61. [DOI]

45. Klingberg T, Fernell E, Olesen PJ, Johnson M, Gustafsson P, Dahlström K, et al. Computerized training of working memory in children with adhd-a randomized, controlled trial. J Am Acad Child Adolesc Psychiatry. 2005;44(2):177–86. [DOI]

46. Abedi A, Agha-Babaei S. The effectiveness of working memory training on improving the academic performance of children with dyscalculia. Journal of Clinical Psychology. 2011;2(4):73–81. [Persian] [Article]

47. Gray SA, Chaban P, Martinussen R, Goldberg R, Gotlieb H, Kronitz R, et al. Effects of a computerized working memory training program on working memory, attention, and academics in adolescents with severe LD and comorbid ADHD: a randomized controlled trial.J Child Psychol Psychiatry. 2012;53(12):1277–84. [DOI]

48. Takeuchi H, Sekiguchi A, Taki Y, Yokoyama S, Yomogida Y, Komuro N, et al. Training of working memory impacts structural connectivity. J Neurosci. 2010;30(9):3297–303. [DOI]

49. Sweller J. Cognitive load theory and educational technology. Educ Technol Res Dev. 2020;68(1):1–16. [DOI]

50. Esmailzadeh Roozbahani A, Behroozi N, Omidian M, Maktabi Gh. Effect of computerized cognitive rehabilitation on executive function and problem-solving of students with a mathematic learning disability. Journal of Exceptional Children Empowerment. 2022;12(4):87–98. [Persian] [Article]

51. Nikbin N, Rezaei M. The effectiveness of cognitive rehabilitation on improving executive functions and reading skills in dyslexic students: a systematic review of domestic research. Qualitative Research in Behavioral Sciences. 2025;3(2):235–58. [Persian] [Article]

52. Namati Sefide-Khan S, Khodadadian M, Kheradmand S, Rahbar M. Effectiveness of cognitive rehabilitation on executive functions in children with learning disorders. Journal of Psychological and Educational Studies. 2023;65:1-14. [Persian]

53. Seif E, Basharpoor S, Narimani M, Heidari F. The effectiveness of executive functions-based cognitive rehabilitation on improving cognitive deficits in children with dyslexia. Research in School and Virtual Learning. 2022;9(3):101–11. [Persian] [Article]

54. Rahnama S, Saeidmanesh M, Demehri F. Effectiveness of remote cognitive-equilibrium tasks based cognitive rehabilitation on improving executive functions and reducing behavioral symptoms of children with attention deficit hyperactivity disorder. Neuropsychology. 2023;9(1):55–66. [Persian] [Article]

55. Mohammadloo M, Sotoude Asl N, Ghorbani R, Talepasand S. Comparing the effectiveness of cognitive rehabilitation and cognitive therapy based on mindfulness on improving the selective attention of students with specific learning disorders. Psychological Sciences. 2024;22(132):149–68. [Persian] [Article]

56. Ranjbar MJ, Basharpoor S, Sobhi-Gharamaleki N, Narimani M. Comparing the Effectiveness of computerized cognitive rehabilitation and neuropsychological exercises on Improving working memory and continuous attention in students with dyslexia. Psychology of Exceptional Individuals. 2019;9(34):111–35. [Persian] [Article]

57. Dehghani Ashkezari S. The effectiveness of movement-based rehabilitation on working memory and attention in children with learning disabilities. In: 15th International Conference on Management and Humanities Research in Iran, Tehran; 2023. [Persian] [Article]

58. Aivazy S, Yazdanbakhsh K, Moradi A. The effectiveness of computer cognitive rehabilitation on improvement of executive function of response inhibition in children with attention deficit hyperactivity. Neuropsychology. 2018;4(14):9–22. [Persian] [Article]

59. Schnotz W, Fries S, Horz H. Some motivational aspects of cognitive load theory. In: Wosnitza S, Karabenick A, Efklides A, Nenninger P; editors. Contemporary motivation research: from global to local perspectives. Göttingen: Hogerefe Pub; 2009.

60. Van Merriënboer JJG, Ayres P. Research on cognitive load theory and its design implications for e-learning. Educ Technol Res Dev. 2005;53(3):5–13. [DOI]

61. De Jong T. Cognitive load theory, educational research, and instructional design: some food for thought. Instructional Science. 2010;38(2):105–34. [DOI]

62. Peterson RL, McGrath LM, Willcutt EG, Keenan JM, Olson RK, Pennington BF. How specific are learning disabilities? J Learn Disabil. 2021;54(6):466–83. [DOI]

63. Lin B, Liew J, Perez M. Measurement of self-regulation in early childhood: Relations between laboratory and performance-based measures of effortful control and executive functioning. Early Child Res Q. 2019;47:1–8. [DOI]

64. Barkley RA. Executive functions: what they are, how they work, and why they evolved. Guilford Press; 2012.

65. Stein M, Auerswald M, Ebersbach M. Relationships between motor and executive functions and the effect of an acute coordinative intervention on executive functions in kindergartners. Front Psychol. 2017;8:859. [DOI]

66. Zhang Q, Wang C, Zhao Q, Yang L, Buschkuehl M, Jaeggi SM. The malleability of executive function in early childhood: Effects of schooling and targeted training. Dev Sci. 2019;22(2):e12748. [DOI]

67. Partanen P. Assessment and remediation for children with special educational needs: The role of working memory, complex executive function and metacognitive strategy training [PhD dissertatin]. [London]: University College London; 2016.

68. Azizi A, Mirdarikvand F, Sepahvandi MA. Comparison of cognitive rehabilitation, neurofeedback and cognitive - behavioral play therapy on visual – motor perception in primary school students with specific learning disability. Neuropsychology. 2017;3(8):103-18. [Persian] [Article]

69. Robinson KE, Kaizar E, Catroppa C, Godfrey C, Yeates KO. Systematic review and meta-analysis of cognitive interventions for children with central nervous system disorders and neurodevelopmental disorders. J Pediatr Psychol. 2014;39(8):846–65. [DOI]

70. Ahn S, Hwang S. Cognitive rehabilitation with neurodevelopmental disorder: a systematic review. NeuroRehabilitation. 2017;41(4):707–19. [DOI]

71. Chatzivasileiou P, Drigas A. ICTs for the cognitive and metacognitive abilities of the students with specific learning disorder in mathematics. Technium Social Sciences Journal. 2022;31:256–79. [DOI]

72. Aquil MAI, Ariffin MM. The causes, prevalence and interventions for dyscalculia in Malaysia. Journal of Educational and Social Research. 2020;10(6):279. [DOI]

73. Tourva A, Spanoudis G. Speed of processing, control of processing, working memory and crystallized and fluid intelligence: Evidence for a developmental cascade. Intelligence. 2020;83:101503. [DOI]

74. Vieira FD, Ribeiro DO, Farias HB, Freitas PM. The working memory as predictor of performance in arithmetic of Brazilian students. Paidéia (Ribeirão Preto). 2021;31:e3119. [DOI]