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A differentiated learning environment in domain model for learning disabled learners

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Abstract

This paper presents the ontological design and implementation of the differentiated learning environment in the domain model of an intelligent tutoring system for children with specific learning disabilities. It addresses the learners need for differentiated instruction in a preferential learning environment. The proposed model helps to identify the most affected learning domains and related multiple-criteria’s which effects the learners. The learning resources and problems diagnosis questionnaires are organized and used with various learning strategies to create various learning environments such as case-based learning environment, game-based learning environment, practice-based learning environment and visual-based learning environment. Different techniques can define a set of rules to decide the most preferred learning environment. Here, multiple criteria decision analysis approach map the information, learning resources and learning environments to create a differentiated learning environment for the learning disabled. The contribution of proposed model is to reduce the gap between learner and learning habits with special needs. Our model is implemented as domain model of an intelligent tutoring system to develop learner-centric learning environment. In the designed intelligent tutoring system (ITS), the differentiated learning environment domain model is further evaluated and validated by a set of fuzzy rules. The pilot test result shows that proposed model enables an ITS to improve the implementation of appropriate learning strategies with high accuracy and sensitivity for both learning and non-learning-disabled users.

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References

  • Abbas, M. A., Ahmad, W. F., & Kalid, K. (2015). A semantic web based framework for preschool cognitive skills tutoring system, 30(3), 835–851

  • Akanksha, B., & Neelu, J. A. (2020). Design and development of competency-based instructional model for instruction delivery for learning disabled using case based reasoning. International Journal of Recent Technology and Engineering (IJRTE), 8(6), 1847–1858. https://doi.org/10.35940/ijrte.F7998.038620.

    Article  Google Scholar 

  • Bakhtyari, P. (2006). Automatic feedback generation-using ontology in an intelligent tutoring system for both learner and author based on student model. In ICEIS 2006-8th international conference on enterprise information systems, proceedings (pp. 116–123).

  • Belland, B., Walker, A., & Kim, N. (2017). A bayesian network meta-analysis to synthesize the influence of contexts of scaffolding use on cognitive outcomes in STEM education. Review of Educational Research, 87, 003465431772300. https://doi.org/10.3102/0034654317723009.

    Article  Google Scholar 

  • Bojar, O., Diatka, V., Rychlý, P., Straňák, P., Suchomel, V., Tamchyna, A., & Zeman, D. (2014). HindEnCorp—Hindi-English and Hindi-only corpus for machine translation (pp. 3550–3555).

  • Caro, M. F., Josyula, D. P., & Jiménez, J. A. (2015). Modelo pedagógico multinivel para la personalización de estrategias pedagógicas en sistemas tutoriales inteligentes. DYNA, 82(194), 185–193

    Article  Google Scholar 

  • Centola, V., & Orciuoli, F. (2016). ITSEGO: An ontology for game-based intelligent tutoring systems. In 8th International conference on computer supported education (pp. 238–245). https://doi.org/10.5220/0005756602380245.

  • Cheng, C. L., Wu, S. H., Tu, L. Y., & Hsu, W. L. (2004). The design of an intelligent tutoring system based on the ontology of procedural knowledge (pp. 525–529). https://doi.org/10.1109/ICALT.2004.1357470.

  • Crealock, C., & Kronick, D. (1993). Children and young people with specific learning disabilities. Association of Canada, Guide Special Education (Vol. 9). http://www.unesco.org/education/pdf/281_72.pdf.

  • de Medeiros, L. F., Junior, A., & Moser, A. (2019). A cognitive assistant that uses small talk in tutoring conversation. International Journal of Emerging Technologies in Learning (iJET), 14, 138. https://doi.org/10.3991/ijet.v14i11.10288.

    Article  Google Scholar 

  • Dermeval, D., Leite, G., Almeida, J., Albuquerque, J., Bittencourt, Ig., Siqueira, S., Isotani, S., & Silva, A. (2017). An ontology-driven software product line architecture for developing gamified intelligent tutoring systems. International Journal of Knowledge and Learning, 12, 27. https://doi.org/10.1504/IJKL.2017.088181.

    Article  Google Scholar 

  • Geng, X., Qin, S., Chang, H., & Yang, Y. (2011). A hybrid knowledge representation for the domain model of intelligent flight trainer. https://doi.org/10.1109/CCIS.2011.6045026.

  • Geng, X., Qin, S., Chang, H., & Yang, Y. (2011). A hybrid knowledge representation for the domain model of intelligent flight trainer (pp. 29–33) https://doi.org/10.1109/CCIS.2011.6045026.

  • Heeren, B., & Jeuring, J. (2014). Feedback services for stepwise exercises. Science of Computer Programming, 88, 110–129. https://doi.org/10.1016/j.scico.2014.02.021.

    Article  Google Scholar 

  • Kazi, H., Haddawy, P., & Suebnukarn, S. (2012). Employing UMLS for generating hints in a tutoring system for medical problem-based learning. Journal of Biomedical Informatics, 45, 557–565. https://doi.org/10.1016/j.jbi.2012.02.010.

    Article  Google Scholar 

  • Kumar, A., Singh, N., & Ahuja, N. J. (2017). Learning styles based adaptive intelligent tutoring systems: document analysis of articles published between 2001 and 2016. International Journal of Cognitive Research in Science, Engineering and Education. https://doi.org/10.5937/IJCRSEE1702083K.

    Article  Google Scholar 

  • Luong, M. T., & Manning, C. (2016). Achieving open vocabulary neural machine translation with hybrid word-character models, 1054–1063. https://doi.org/10.18653/v1/P16-1100.

  • Mills, C., Australia, F., & Dalgarno, B. (2007). A conceptual model for game based intelligent tutoring systems. In ASCILITE 2007-the australasian society for computers in learning in tertiary education.

  • Mohammad, A. (2011). An overview: Intelligent tutoring systems, https://www.researchgate.net/publication.

  • Moore, J. L., & Sleeman, D. (1988). Enhancing PIXIE’s tutoring capabilities. International Journal of Man-Machine Studies, 28, 605–623. https://doi.org/10.1016/S0020-7373(88)80063-4.

    Article  Google Scholar 

  • Nagyova, I. (2017). E-learning environment as intelligent learning environment. AIP Conference Proceedings, 1863, 1. https://doi.org/10.1063/1.4992244.

    Article  Google Scholar 

  • Nair, M. K. C., Prasad, C., Unni, J., Bhattacharya, A., Kamath, S. S., & Dalwai, S. (2017). Consensus statement of the Indian Academy of Pediatrics on evaluation and management of learning disability. Indian Pediatrics, 54(7), 574–580. https://doi.org/10.1007/s13312-017-1071-9.

    Article  Google Scholar 

  • Nkambou, R. (2006) A framework for affective intelligent tutoring systems. In 7th International conference on information technology based higher education and training, ITHET-2006. https://doi.org/10.1109/ITHET.2006.339720.

  • Panagiotopoulos, I., Kalou, A., Pierrakeas, C., & Kameas, A. (2012). An ontology-based model for student representation in intelligent tutoring systems for distance learning. In L. Iliadis, I. Maglogiannis, & H. Papadopoulos (Eds.), Artificial intelligence applications and innovations. AIAI 2012. IFIP advances in information and communication technology. (Vol. 381)Berlin: Springer. https://doi.org/10.1007/978-3-642-33409-2_31.

    Chapter  Google Scholar 

  • Peters, C., Arroyo, I., Burleson, W., Woolf, B., & Muldner, K. (2018). Predictors and outcomes of gaming in an intelligent tutoring system. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-91464-0_41.

    Article  Google Scholar 

  • Piñeres, M., Josyula, D., & Jiménez-Builes, J. (2015). Multi-level pedagogical model for the personalization of pedagogical strategies in intelligent tutoring systems. Dyna (medellin, Colombia), 82, 185–193. https://doi.org/10.15446/dyna.v82n194.49279.

    Article  Google Scholar 

  • Predrag, D., Jovan, D., Bojan, C., & Veis, S. (2016). A review of intelligent tutoring systems in e-learning. Annals of the University of Oradea Fascicle of Management and Technological Engineering. https://doi.org/10.15660/AUOFMTE.2016-3.3276.

    Article  Google Scholar 

  • Psyché, V., Daniel, B., & Bourdeau, J. (2019). Adaptive learning spaces with context-awareness. ITS 2019. LNCS, 11528, 7–13. https://doi.org/10.1007/978-3-030-22244-4_2.

    Article  Google Scholar 

  • Rajendran, R. (2014). Enriching the student model in intelligent tutoring system. PhD Thesis, IIT Bombay and Monash University, Australia.

  • Roscoe, R., Allen, L., Weston, J., & McNamara, D. (2014). The writing pal intelligent tutoring system: Usability testing and development. Computers and Composition, 34, 39–59. https://doi.org/10.1016/j.compcom.2014.09.002.

    Article  Google Scholar 

  • Rosset, C., Mitra, B., Xiong, C., Craswell, N., Song, X., & Tiwary, S. (2019). An axiomatic approach to regularizing neural ranking models. In 42nd international ACM SIGIR conference (pp. 981–984). https://doi.org/10.1145/3331184.3331296.

  • Russell, A., Bryant, L., & House, A. (2017). Identifying people with a learning disability: An advanced search for general practice. British Journal of General Practice. https://doi.org/10.3399/bjgp17X693461.

    Article  Google Scholar 

  • Šakele, V., & Grundspenkis, J. (2005). The role of ontologies in agent-based simulation of intelligent tutoring systems. In Simulation in wider Europe-19th European conference on modelling and simulation, ECMS 2005.

  • Salman, A. R. (2013). The use of intelligent tutoring system for developing web-based learning communities. IJCSI International Journal of Computer Science., 6(1), 157–161

    Google Scholar 

  • Searson, R., & Dunn, R. (2001). The learning-style teaching model. Science and Children, 38, 22–26

    Google Scholar 

  • Soh, L. K., Blank, T., Miller, L. D., & Person, S. (2005). ILMDA: An intelligent learning materials delivery agent and simulation. In 2005 IEEE international conference on electro information technology (p. 6). https://doi.org/10.1109/EIT.2005.1627023.

  • Vanlehn, K., Lynch, C., Schulze, K., Shapiro, J., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., & Wintersgill, M. (2005). The Andes physics tutoring system: Lessons learned. International Journal of Artificial Intelligence in Education, 15, 147–204

    Google Scholar 

  • Vasiliki, K., & Paraskeva, F. (2020). Smart learning environments: A blend of ICT achievements and smart pedagogy for the world sustainable development. Advances in Intelligent Systems and Computing. https://doi.org/10.1007/978-3-030-25629-6_75.

    Article  Google Scholar 

  • Yu, S. (2009). Ontology-based knowledge management in intelligent tutoring systems. International Conference on Management and Sevice Sciences. https://doi.org/10.1109/ICMSS.2009.5304960.

    Article  Google Scholar 

  • Zhang, B., Xiong, D., Su, J., & Duan, H. (2017). A context-aware recurrent encoder for neural machine translation. IEEE/ACM Transactions on Audio, Speech, and Language Processing. https://doi.org/10.1109/TASLP.2017.2751420.

    Article  Google Scholar 

  • Zouaq, A., & Nkambou, R. (2008). Building domain ontologies from text for educational purposes. IEEE Transactions on Learning Technologies, 1, 49–62. https://doi.org/10.1109/TLT.2008.12.

    Article  Google Scholar 

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Funding

The work performed at the University of Petroleum and Energy Studies (UPES), Dehradun, under project reference number SEED/TIDE/133/2016. The authors thankfully recognise the funding support received from Science for Equity Empowerment and Development (SEED) Division, Department of Science and Technology (DST) for the project. The authors thank the University of Petroleum and Energy Studies administration for promoting the work and grant approvals.

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Correspondence to Neelu Jyothi Ahuja.

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Thapliyal, M., Ahuja, N.J., Shankar, A. et al. A differentiated learning environment in domain model for learning disabled learners. J Comput High Educ 34, 60–82 (2022). https://doi.org/10.1007/s12528-021-09278-y

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