Abstract
Several studies have investigated the need for learning difficulties identification specifically Dyslexia, Dysgraphia and Dyscalculia. The identification of these difficulties among children is a multiple screening process under psychologist’s supervision. Learning difficulties identification is a difficult task; it affects the learning process and the academic achievements of a child. The introduction of an Intelligent Tutoring System (ITS) to identify learning problems and teach the learning disabled through ITS is an unexplored domain. An ITS in education is extensively considered for the teaching and learning process as it is an adaptive and learner specific computer system. The capabilities of an ITS in integration with AI methodologies have put together promising results. The ITS framework implemented in this study is developed for learning disabilities identification and we have assessed total 24 participants (with or without Learning Disabilities) for the experiment. This ITS framework design is based on a pretest analysis through initial screening and then system based screening of a child response for Learning Difficulties (LDs) identification. The system based screening is implemented using neural network classifiers to identify learning difficulties. The fuzzy min-max neural network (FMNN) classification is applied to determine learner profile, learning disabled, and present learner-centered content. Fuzzy sets as pattern classes are introduced in supervised learning neural network classification for learner profiling of learning Disabled in an ITS. The results are generated based on the classification applied to the input provided during the pre-test. The results indicate that the integration of fuzzy with the neural network has significantly increased the ITS accuracy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahmed, M. R., Zhang, Y., Liu, Y., & Liao, H. (2020). Single Volume Image Generator and Deep Learning-based ASD Classification. IEEE Journal of Biomedical and Health Informatics, 1–1. https://doi.org/10.1109/jbhi.2020.2998603
Alchalabi, A. E., Shirmohammadi, S., Eddin, A. N., & Elsharnouby, M. (2018). FOCUS: Detecting ADHD patients by an EEG-based serious game. IEEE Transactions on Instrumentation and Measurement, 67(7), 1512–1520. https://doi.org/10.1109/tim.2018.2838158.
Alhroob, E., Mohammed, M. F., Lim, C. P., & Tao, H. (2019). A critical review on selected fuzzy min-max neural networks and their significance and challenges in pattern classification. IEEE Access, 7, 56129–56146. https://doi.org/10.1109/access.2019.2911955.
Bernard, J., Chang, T.-W., Popescu, E., & Graf, S. (2017). Learning style identifier: Improving the precision of learning style identification through computational intelligence algorithms. Expert Systems with Applications, 75, 94–108. https://doi.org/10.1016/j.eswa.2017.01.021.
Bortone, I., Leonardis, D., Mastronicola, N., Crecchi, A., Bonfiglio, L., Procopio, C., et al. (2018). Wearable haptics and immersive virtual reality rehabilitation training in children with neuromotor impairments. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(7), 1469–1478.
Callear, D. (1999). Intelligent tutoring environments as teacher substitutes: Use and feasibility. Educational Technology, 39(5), 6–8.
Chen, J.-F., & Do, Q. H. (2014). A cooperative cuckoo search - hierarchical adaptive neuro-fuzzy inference system approach for predicting student academic performance. Journal of Intelligent and Fuzzy Systems, 27, 2551–2561. https://doi.org/10.3233/IFS-141229.
Cohen, P., Beal, C., & Adams, N. (2008). The design, deployment and evaluation of the animal watch intelligent tutoring system. 663–667. https://doi.org/10.3233/978-1-58603-891-5-663.
Dagar, P., Jatain, A., & Gaur, D. (2015). Medical diagnosis system using fuzzy logic toolbox. International Conference on Computing, Communication & Automation. https://doi.org/10.1109/ccaa.2015.7148370.
Das, R., Ahmed, U., Karkare, A., & Gulwani, S. (2016). Prutor: A System for Tutoring CS1 and Collecting Student Programs for Analysis.
Dimauro, G., Bevilacqua, V., Colizzi, L., & Di Pierro, D. (2020). TestGraphia, a software system for the early diagnosis of dysgraphia. IEEE Access, 8, 1–1. https://doi.org/10.1109/ACCESS.2020.2968367.
Du, Z., Lin, T., & Zhao, T. (2015). Fuzzy robust tracking control for uncertain nonlinear time-delay system. International Journal of Computers Communications & Control, 10, 52. https://doi.org/10.15837/ijccc.2015.6.2072.
Gabrys, B., & Bargiela, A. (2000). General fuzzy min-max neural network for clustering and classification. IEEE Transactions on Neural Networks, 11(3), 769–783.
Goswami, U., Wang, H. L., Cruz, A., Fosker, T., Mead, N., & Huss, M. (2011). Language-universal sensory deficits in developmental dyslexia: English, Spanish, and Chinese. Journal of Cognitive Neuroscience, 23(2), 325–337. https://doi.org/10.1162/jocn.2010.21453.
Grosan C., Abraham A. (2011) Rule-Based Expert Systems. In: Intelligent Systems. Intelligent Systems Reference Library, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21004-4_7.
Hu, J., & Luo, Y. (2017). Integration of learning algorithm on fuzzy min-max neural networks. Journal of Shanghai Jiaotong University (Science), 22(6), 733–741. https://doi.org/10.1007/s12204-017-1894-5.
Jawarkar, N., Holambe, R., & Basu, T. (2011). Use of fuzzy min-max neural network for speaker identification. International Conference on Recent Trends in Information Technology, ICRTIT. https://doi.org/10.1109/ICRTIT.2011.5972455.
Joshi, A., Ramakrishman, N., Houstis, E. N., & Rice, J. R. (1997). On neurobiological, neuro-fuzzy, machine learning, and statistical pattern recognition techniques. IEEE Transactions on Neural Networks, 8(1), 18–31. https://doi.org/10.1109/72.554188.
Kaufmann, L. (2008). Dyscalculia: neuroscience and education. Educational Research, 50(2), 163–175. https://doi.org/10.1080/00131880802082658.
Khalid, A., & Beg, I. (2018). Incomplete interval-valued hesitant fuzzy preference relations in decision making. Iranian Journal of Fuzzy Systems, 15, 107–120. https://doi.org/10.22111/ijfs.2018.3710.
Khuat, T., Chen, F., & Gabrys, B. (2019). An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network.
Khuat, T. T., Ruta, D., & Gabrys, B. (2020). Hyperbox-based machine learning algorithms: a comprehensive survey. Soft Computing. https://doi.org/10.1007/s00500-020-05226-7.
Le Meur, O., Nebout, A., Cherel, M., & Etchamendy, E. (2020). From Asperger autism to Kanner syndromes, the difficult task to predict where ASD people look at. IEEE Access, 1–1. https://doi.org/10.1109/access.2020.3020251.
Liang, F., & Li, P. (2019). Characteristics of cognitive in children with learning difficulties. Translational Neuroscience, 10, 141–146. https://doi.org/10.1515/tnsci-2019-0024.
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123.
McCloskey, M., & Rapp, B. (2017). Developmental dysgraphia: An overview and framework for research. Cognitive Neuropsychology, 34, 1–18. https://doi.org/10.1080/02643294.2017.1369016.
Mitrpanont, J., Bousai, B., Soonthornchart, N., Tuanghirunvimon, K., & Mitrpanont, T. (2018). iCare-ADHD: A Mobile Application Prototype for Early Child Attention Deficit Hyperactivity Disorder. 2018 Seventh ICT International Student Project Conference (ICT-ISPC). https://doi.org/10.1109/ict-ispc.2018.8523973.
Mohammed, M., & Lim, C. (2015). An enhanced fuzzy min–max neural network for pattern classification. IEEE transactions on neural networks and learning systems, 26, 417–429. https://doi.org/10.1109/TNNLS.2014.2315214.
Nădăban, S., & Dzitac, I. (2014). Atomic decompositions of fuzzy normed linear spaces for wavelet applications. Informatica, 25(4), 643–662. https://doi.org/10.15388/Informatica.2014.33.
Norris, M., Hammond, J., Williams, A., & Walker, S. (2019). Students with specific learning disabilities experiences of pre-registration physiotherapy education: A qualitative study. BMC Medical Education, 20(1), 2. https://doi.org/10.1186/s12909-019-1913-3.
Norton, E., & Wolf, M. (2010). Rapid automatized naming (RAN) and Reading fluency: Implications for understanding and treatment of Reading disabilities. Annual Review of Psychology, 63, 427–452. https://doi.org/10.1146/annurev-psych-120710-100431.
Perera, H., Shiratuddin, M. F., Wong, K. W., & Fullarton, K. (2017). EEG signal analysis of passage reading and rapid automatized naming between adults with dyslexia and normal controls. 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS). https://doi.org/10.1109/icsess.2017.8342874.
Rao, C., T A, S., Midha, R., Oberoi, G., Kar, B., Khan, M., Vaidya, K., Midya, V., Raman, N., Gajre, M., & Singh, N. C. (2021). Development and standardization of the DALI-DAB (dyslexia assessment for languages of India - dyslexia assessment battery). Annals of Dyslexia, https://doi.org/10.1007/s11881-021-00227-z. Advance online publication.
Shelke, M., Malhotra, A., & Mahalle, P. (2018). Fuzzy-based dynamic packet priority determination and queue management method for wireless sensor network. International Journal of Internet Technology and Secured Transactions, 8, 433. https://doi.org/10.1504/IJITST.2018.093666.
Simpson, P. K. (1992). Fuzzy min-max neural networks. I. Classification. IEEE Transactions on Neural Networks, 3(5), 776–786. https://doi.org/10.1109/72.159066.
Soofi, A., & Uddin, M. (2019). A systematic review of domains, techniques, delivery modes and validation methods for intelligent tutoring systems. International Journal of Advanced Computer Science and Applications, 10. https://doi.org/10.14569/IJACSA.2019.0100312.
Tlili, A., Najjar, R., Essalmi, F., Jemni, M., Chang, M., Huang, R., & Chang, T.-W. (2020). Unobtrusive monitoring of learners’ game interactions to identify their dyslexia level. 2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT). https://doi.org/10.1109/icalt49669.2020.00040
Tresser, S. (2012). Case study: Using a novel virtual reality computer game for occupational therapy intervention. Teleoperators and Virtual Environments - Presence, 21, 359–371. https://doi.org/10.1162/PRES_a_00118.
Woolf, B. & Arroyo, I. (2015). A mentor for every student: One challenge for instructional software. IBM Journal of Research and Development 59. 9:1–9:13. https://doi.org/10.1147/JRD.2015.2463611.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/s0019-9958(65)90241-x.
Zhang, H., Liu, J., Ma, D., & Wang, Z. (2011). Data-Core-based fuzzy min–max neural network for pattern classification. IEEE Transactions on Neural Networks, 22(12), 2339–2352. https://doi.org/10.1109/tnn.2011.2175748.
Acknowledgments
This research work has been carried out at the University of Petroleum and Energy Studies (UPES) with Project No SEED/TIDE/133/2016. The authors gratefully acknowledge the funding support received from the Technology Interventions for Disabled and Elderly (TIDE) scheme under the Department of Science and Technology (DST). The authors express their gratitude towards the management of UPES for their support in research work.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Annexure 1: A set of Pre-test Questions
Quiz number | Pre-test Questions |
Grade 1 | |
1 | Click on the letter matching given letter: V |
2 | Click on the letter matching given letter: B |
3 | Listen to audio and fill in the blanks: C__P |
4 | Arrange the letter to form a Word shown in the picture: L T I L T E / Y L O E L W / D C U K |
5 | Which picture best describes the word: PLAY |
6 | Count the fish and click the number |
7 | ADD: 5 + 3 |
8 | Subtract: 8–2 |
9 | Write the missing number: 5, 7, __,11 |
10 | Click on the left picture: |
11 | a. Say CUPCAKE without CAKE b. Say MATCHBOX without BOX c. Pronounce and Write INTERPRETATION |
12 | a. Count backwards from: 30–21 b. True/False: 2 > 3 c. Write in words: 97 d. Write the digits shown: 5 2 2 7 e. Subtract: 28–2 f. Addition: 28 + 2 |
13 | Connect the dotted fish imageand upload. |
14 | a. Pronounce the words given correctly:af am ab zikzab at am of see look b. Name the colors shown below? |
15 | Copy the lines in .txt file and upload |
16 | a. A bee has 6 legs. How many legs do 2 bees have? b. You have 7 cookies and you ate 2 of them. How many cookies do you have left? |
17 | Write the given line on a blank sheet, scan and upload. |
18 | a. Do they rhyme? HIT and HEIGHT say yes or no b. Do they rhyme? CUP and PUP say yes or no c. Merge the words PENCIL |
19 | Read the passage below aloud: |
Grade 2 | |
1 | Click on the tab matching given letters: Bfn |
2 | Which word rhyme with the word: FISH |
3 | Listen to audio and fill in the blanks: |
4 | Arrange the letter to form a Word shown in the picture: (Hint: I am the national flower of India.) |
5 | Arrange the words to form a meaningful sentence: RED ROSES ARE |
6 | What is the value of four in the number? 890,465 |
7 | Solve the given problem: 23 + 17–19 =? |
8 | Solve the given problem: 19*4 =? |
9 | Divide: 333 by 9 |
10 | Which dinosaur is in cylinder? |
11 | a. Say Confront without Con b. Pronounce and Write INTERSTELLAR c. Say Trustworthy without worthy |
12 | a. Count Backwards from: 130 to 120 b. True/False: 2550 > 2569 c. Write in words: 997 d. Write the digits shown: 6 5 5 2 2 7 e. Subtract: 528–72 f. Addition: 828 + 28 |
13 | Help the bee to reach flowers and upload image: |
14 | a. Pronounce the words given correctly: zikzabziszobzam see look monkey through dark b. Name the colours shown below? |
15 | Type the given text in .txt file and upload. |
16 | a. Ellen had 380 eggs, but she lost 57 of them. How many eggs does she have now? b. Arthur baked 35 muffins. How many more muffins does Arthur have to bake to have 83 muffins? |
17 | Write the given line on a blank sheet, scan and upload. |
18 | a. Do they rhyme? Six and Sticks say yes or no b. Do they rhyme? Seven and Heaven say yes or no c. Merge the words Net,working |
19 | Read the passage below aloud: |
Annexure 2: A Sample of Student profiles
Gender | Age | Learning Disabilities | |
Learner 1 | Male | 6 | Dyslexia |
Learner 2 | Male | 7 | Dyslexia,Dysgraphia, and Dyscalculia |
Learner 3 | Female | 7 | Non learning disabled |
Learner 4 | Male | 7 | Dyslexia,Dysgraphia |
Learner 5 | Male | 8 | Dyslexia |
Learner 6 | Male | 6 | Dyslexia |
Learner 7 | Female | 7 | Non learning disabled |
Learner 8 | Female | 8 | Dyscalculia |
Learner 9 | Male | 7 | Dyslexia |
Rights and permissions
About this article
Cite this article
Dutt, S., Ahuja, N.J. & Kumar, M. An intelligent tutoring system architecture based on fuzzy neural network (FNN) for special education of learning disabled learners. Educ Inf Technol 27, 2613–2633 (2022). https://doi.org/10.1007/s10639-021-10713-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-021-10713-x