Abstract
Awareness, short-term memory, and long-term memory are interrelated cognitive abilities that influence orthographic acquisition under Individual Differences. Connectionists ignore the role of biological grammar in language acquisition and consider external inputs or interventions as factors that shape abstract grammar through network mapping architecture. As new intervention methods, the use of computers in connectionist modeling of language acquisition has increasingly been developed through Artificial Neural Networks. New optimized and accurate computational models have not been applied to investigate foreign language orthography acquisition. The purpose of the present study is to use Multi-Layer Perceptron Neural Network-Gray Wolf Optimizer computational model to simulate and evaluate individualized cognitive abilities including orthographic awareness, orthographic short-term memory-related, and orthographic long-term memory-related abilities of Iranian English as Foreign Language learners in orthography acquisition. Eighteen Iranian learners were randomly selected and non-randomly assigned into three low-labeled, mean-labeled, and high-labeled groups for the experiment. In a mixed-methods design, three Multi-Layer Perceptron Neural Network-Gray Wolf Optimizers were established to solve the mapping problem i.e., English plural noun variation orthography. ANOVA was used for analyzing Mean Squared Errors and content analysis was used for interpreting interviews and observations. Results indicated that not only the orthographic awareness and short-term memory-related abilities are more important in predicting acquisition competence than the orthographic long-term memory-related ability but also they give compensatory support to long-term memory-related ability. The proposed valid and reliable model can be used to generate hypotheses related to Individual Differences. Findings are discussed and relevant implications are drawn for teachers and practitioners.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated during and / or analyzed during the current study are available from corresponding author on reasonable request.
Notes
- Numbers in the neuron cell are not real; they are only for binary (0-1) demonstration.
Colors in the legend of the figures indicate the following: red refers to the performance in the first week, pink refers to the performance in the second week, blue refers to the performance in the third week, green refers to the performance in the fourth week, cyan refers to the performance in the fifth week, yellow refers to the performance in sixth week, brown refers to the performance in seventh week, purple refers to the performance in the eighth week, and gray refers to MLPNN index. The learner’s code and tasks’ numbers are abbreviated. For example, S1T1, S1T2, and S1T3 refer to the first EFL learner who performed the Task (A), Task (B), and Task (C) respectively.
References
Abu-Rabia, S., & Siegel, L. S. (2002). Reading, syntactic, orthographic, and working memory skills of bilingual Arabic-English speaking Canadian children. Journal of Psycholinguistics Research, 31, 661–678. https://doi.org/10.1023/A:1021221206119
Alhama, R., & Zuidema, W. (2019). A review of computational models of basic rule learning: The neural-symbolic debate and beyond. Psychonomic Bulletin and Review, 26, 1174–1194. https://doi.org/10.3758/s13423-019-01602-z
Alishahi, A. (2011). Computational models of language learning. In Computational Modeling of Human Language Acquisition. Synthesis Lectures on Human Language Technologies. Springer. https://doi.org/10.1007/978-3-031-02140-4_2.
Anderson, J. (1990). The adaptive character of thought. Erlbaum.
Ary, D., Jacobs, L. C., Sorensen, C., & Razavieh, A. (2010). Introduction to research in education. Cengage Learning.
Bachman, L. F. (1990). Fundamental considerations in language testing. Oxford University Press.
Bailly, G., & Barbour, W-S. (2011). Synchronous reading: Learning French orthography by audiovisual training. Inter speech, 12th Annual Conference of the International Speech Communication Association (pp. 1153–1156). Aug 2011, Florence, Italy. https://hal.archives-ouvertes.fr/hal-0061878
Berninger, V. (1987). Global, component, and serial processing of printed words in beginning readers. Journal of Experimental Child Psychology, 43, 387–418. https://doi.org/10.1016/0022-0965(87)90015-4
Berninger, V., Cartwright, A., Yates, C., Swanson, H., & Abbott, B. (1994). Developmental skills related to writing and reading acquisition in the intermediate grades. Reading and Writing: An Interdisciplinary Journal, 6, 161–196. https://doi.org/10.1007/BF01026911
Berninger, V., Yates, C., Cartwright, A., Rutberg, J., Remy, E., & Abbott, R. (1992). Lower-level developmental skills in beginning writing. Reading and Writing: An Interdisciplinary Journal, 4, 257–280. https://doi.org/10.1007/BF01027151
Berninger, V. W., Abbot, R. D., Nagy, W., & Calrlisle, J. (2010). Growth in phonological, orthographic, and morphological awareness in grades 1 to 6. World Journal of Psycholinguist Research, 39, 141–163. https://doi.org/10.1007/s10936-009-9130-6
Bilcu, E. B., Suontausta, J., & Saarinen, J. (2003). A study on different neural network architectures applied to text-to-phoneme mapping (vol. 2, pp. 892–896). 3rd International Symposium on Image and Signal Processing and Analysis, Sep 2003. Proceedings of the ISPA. https://doi.org/10.1109/IPSA.2003.1296405
Bisson, M.-J. (2022). learning words with unfamiliar orthography: The role of cognitive abilities. Studies in Second Language Acquisition, 07, 1–15. https://doi.org/10.1017/S0272263122000390
Born, J., Nikolov, N. I., Rosenkranz, A., Schabmann, A., & Schmidt, B. M. (2022). A computational investigation of inventive spelling and the “Lesendurch Schreiben” method. Computers and Educations: Artificial Intelligence, 3, 100063. https://doi.org/10.3929/ethz-b-000543428.
Bosch, T., Van Hame, H., Boves, L., & Moore, R. K. (2009). A computational model of language acquisition: The emergence of words. Fundamenta Informaticae, 90, 229–249.
Bosse, M. L., Chaves, N., Largy, P., & Valdois, S. (2015). Orthographic learning during reading: The role of whole-word visual processing. Journal of Research in Reading, 38, 141–158. https://doi.org/10.1111/j.1467-9817.2012.01551.x
Borleffs, E., Maassen, B., Lyytinen, H., & Zwarts, F. (2019). Cracking the code: The impact of orthographic transparency and morphological-syllabic complexity on reading and developmental dyslexia. Frontiers in Psychology, 9, 1–19.
Brown, J. D. (2005). Testing in language programs: a comprehensive guide to English language assessment. Prentice Hall Regents.
Buchwald, A., & Rapp, B. (2009). Distinctions between orthographic long-term memory and working memory. Cognitive Neuropsychology, 26(8), 724–751.
Cai, C., Xu, Y., Dengfegng, K., & Su, K. (2015). A fast learning method for multilayer perceptrons in automatic speech recognition system. Journal of Robotics, 1–7. https://doi.org/10.1155/2015/797083.
Cao, Y., Huang, T., Huang, J., Xie, X., & Wang, Y. (2020). Effects and moderators of computer-based training on children’s executive function: a systematic review and meta-analysis. Frontiers in Psychology, 11, 580329. https://doi.org/10.3389/fpsyg.2020.58.0329.
Chang, L-Y, Plaut, D. C., & Perfetti, C. A. (2015). Visual complexity in orthography learning: modelling learning across writing system variations. Scientific Studies on Reading, 1–22. https://doi.org/10.1080/10888438.2015.1104688.
Chang, Y-N. (2022). Individual differences in a computational model of reading: the influence of oral vocabulary knowledge. Research Square, 1–22. https://doi.org/10.21203/rs.3.rs-1772735/v1.
Chen, M. & Li, W. (2022). The influence of form-focused instruction on the L2 Chinese oral production of Korean native speakers. Frontiers in Psychology, 13, 790424. https://doi.org/10.3389/fpsyg.2022.790424.
Chomsky, N., Gallego, A. J., & Ott, D. (2019). Generative grammar and the faculty of language: insights questions, and challenges. Catalan Journal of Linguistics Special Issue, 229–26. https://doi.org/10.5565/rev/catjl.288.
Colclasure, B. C., Durham, B. T., Helikar, T., King, S. J., & Webb, A. (2022). The effect of a modeling and computational thinking professional development on STEM educators’ perceptions toward teaching science. Education Science, 12, 570. https://doi.org/10.3390/edusci12080570
Colmar, S. & Double, K. (2017). Working memory interventions with children: classroom or computers? Journal of Psycholinguistic and Counselors in Schools, 27(2), 264-177.https://doi.org/10.1017/jgc.2017.11.
Cordewener, K. A. H., Hasselman, F., Verhoeven, L., & Bosman, A. M. (2018). The role of instruction for spelling performance and spelling consciousness. Journal of Experimental Education, 86(2), 135–153.
Cowan, N. (2019). Short-term memory based on activated long-term memory: A review in response to Norris (2017). Psychological Bulleting, 145(8), 822–847. https://doi.org/10.1037/bul0000199
Dai, C-P, & Ke, F. (2022). Educational application of artificial intelligence in simulation-based learning: A systematic mapping review. Computers and Education: Artificial Intelligence, 3, 100087. https://doi.org/10.1016/j.caeai.2022.100087.
Dekeyser, R. (2007). Introduction: situating the concept of practice. In R. Dekeyser, (Ed.), Practice in a Second Language. Cambridge University Press.
De Mooij, J., Dell’Anna, D., Bhattacharya, P., Dastani, M., Logan, B., Swarup, S. (2022). Using agent-based simulation to investigate behavioral intervention in a pandemic simulating behavioral interventions in a pandemic. CEUR Workshop Proceedings in 1st Workshop on Agent-Based Modeling and Policy-Making, AMPM (vol. 3182). Dec 2021 - Virtual, Vulnius, Lithuania.
Dijkstra, T., & Van Heuven, W. J. B. (2002). The architecture of the bilingual word recognition system: From identification to decision. Bilingualism: Language and Cognition, 5(3), 175–197. https://doi.org/10.1017/S1366728902003012.
Dijkstra, T., & Van Heuven, W. J. B. (1998). The BIA model and bilingual word recognition. In J. Grainger & A. M. Jacobs (Eds.), Localist Connectionist Approaches to Human Cognition (pp. 189–225). Lawrence Erlbaum Associates.
Dijkstra, T., Wahl, A., Buytenhuijs, F., Halem, N. V., Al-jiboury,Z., De Korte, M., & Rekke, S. (2019). Multilink: a computational model for bilingual word recognition and word translation. Bilingualism: Language and Cognition, 22(4), 657–679. https://doi.org/10.1017/s11366728918000287.
Dornyei, Z. (2005). The psychology of the language learner: Individual differences in second language acquisition. Erlbaum.
Erbeli, F., & Pizorn, K. (2012). Reading ability, reading fluency, and orthographic skills: the case of L1 Slovene English as foreign language students. C.E.P.S. Journal, 2(3), 119–139.
Ellis, R. (2015). Understanding second language acquisition. Oxford University Press.
Field, J. (2003). Psycholinguistics, a resource book for student. Routledge.
Ghayoomi, M. (2022). Application of computational linguistics to predicting language proficiency of Persian learners’ textbooks. Language Horizon, 6(1), 29–52. https://doi.org/10.22051/LGHOR.2021.32656.1354
Granato, G., Borghi, A. M., & Baldassare, G. (2020). A computational model of language functions in flexible goal-directed behavior. Scientific Reports, 10, 21623. https://doi.org/10.1038/s41598-020-78252-y.
Hall, T. (2020). Review of experimental social behavioral interventions for preschool children: an evidential-based synthesis. Review Article, Sage Open, 1–13. https://doi.org/10.1177/2158244019899420.
Hare, M., Elman, J. L., & Daugherty, K. G. (1995). Default generalization in connectionist networks. Language and Cognitive Processes, 10(6), 601–630. https://doi.org/10.1080/01690969508407115
Hayes-Harb, R., & Barrios, S. (2021). The influence of orthography in second language phonological acquisition. Language Teaching, 54, 297–326. https://doi.org/10.1017/S0261444820000658
Herra, A., & Kulinska, A. (2018). The role of feedback in the process of learning English as a foreign language. Forum Filologiczne Areneum, 1(6), 127–143. https://doi.org/10.36575/2353-2912/1(6)2018.127.
Hulstijn, J. H. (2003). Connectionist models of language processing and the training of listening skills with the aid of multimedia software. Computer Assisted Language Learning, 16(5), 413–425. https://doi.org/10.1076/call.16.5.413.29488
Joanisse, M. F., & McClelland, J. L. (2015). Connectionist perspective on language learning, representation and processing. WIREs Cognitive Science, 1–12. https://doi.org/10.1002/WCS.1340.
Johnson, N. (1978). Coding processing in memory. In W. K. Estes (Ed.), Handbook of Learning and Cognitive Process (Vol. 6, pp. 87–129). Erlbaum.
Johnson, N. (1986). On looking letters within words: Do we “see” them in the memory? Journal of Memory and Language, 25, 558–570.
Kahraman, N., & Yildirim, T. (2003). The effects of training algorithms in MLP neural network on image classification. Proceedings of the International Joint Conference on Neural Networks, 2, 1223–1226. https://doi.org/10.1109/IJCNN.2003.122.3867.
Kartal, E. (2019). The artificial neural network modeling of language learning challenges of French-Speaking students learning Turkish as a foreign language: the case of France. Educational Science: Theory and Practice, 19(1), 55–77. https://doi.org/10.12738/estp.2019.1.0264.
Khaksar Boldaji, M. A., Abdollahi, M. H., Kadivar, P., Hasan Abdi, H. R., & Arjomandnia, A. (2018). The effectiveness of computer-based cognitive training on working memory, attention, response control, and central executive of working memory of children with specific learning disabilities. Social Cognition, 7(2), 173–186. https://doi.org/10.30473/sc.2018.41194.2223.
Khishe, M., & Parvizi, G. R. (2020). Neural networks: History and application. In D. Alexander (Ed.), Computer Science, Technology, and Application (pp. 1–30). Nova Publication.
Khoshaim, B. A., Moustafa, E. B., Bafakeeh, O. T., & Elsheikh, A. H. (2021). An optimized multilayer perceptrons model using gray wolf optimizer to predict mechanical and microstructural properties of friction stir processed aluminum alloy reinforced by nanoparticles. Coating, 11, 1476. https://doi.org/10.3390/coatings11121476
Kohil, M., Magoulas, G. D., & Thomas, M. S. C. (2020). Evolving connectionist models to capture population variability across language development: Modeling children’s past tense formation. Artificial Life, 26(2), 217–241. https://doi.org/10.1162/artl_a_00316
Kroll, J. F., van Hell, J. G., Tokowicz, N., & Green, D. W. (2010). The revised hierarchical model: A critical review and assessment. Bilingualism, 13(3), 373–381. https://doi.org/10.1017/S136672891000009X
Lamb, R., Vallet, D. B., Akmal, T., & Baldwin, K. (2014). A computational modeling of student cognitive process in science education. Computers and Education, 79, 116–125. https://doi.org/10.1016/j.compedu.2014.07.014
Lee, D., Derrible, S., Pereira, F. C. (2018). Comparisons of four types of artificial neural network and a multinomial logit model for travel mode choice modeling. Journal of the Transportation Research Board, 2672(49). https://doi.org/10.1177/0361198118796971.
Lin, C. X., & Marinove, M. (1993). Answering the connectionist challenge: A symbolic model of learning the past tense of English verbs. Cognition, 49(3), 235–290. https://doi.org/10.1016/0010-0277(93)90006-h
Lio, X., Loh, E. K. Y., & Cai, M. (2022). Lexical orthographic knowledge mediates the relationship between character reading and reading comprehension among learners with Chinese as a second language. Frontier in Psychology, 13, 779905. https://doi.org/10.3389/fpsyg.2022.779905.
Lumacad, G. S., & Namoco, R. A. (2022). Multilayer perceptrons neural network approach to classifying learning modalities under the new normal. IEEE Transaction and Computational Social System, 1–10. https://doi.org/10.36227/techrxiv.20428230.
Luque, A., & Morgan-Short, K. (2021). The relationship between cognitive control and second language proficiency. Journal of Neurolinguistics, 57, 1–17. https://doi.org/10.1016/j.jneuroling.2020.100956
Marcus, G. F. (1995). The acquisition of the English past tense in children and multilayered connectionist network. Cognition, 56(3), 271–279. https://doi.org/10.1016/0010-0277(94)00656-6
Marcus, G. F., Pinker, S., Ullman, M., Hollander, M., Rosen, T.G., & Xu, F. (1992). Over regularization in language acquisition. Monograph of Society for Research in Child Development, 67(4). https://doi.org/10.2307/1166115.
Margo, L. O., Majerus, S., Attot, L., Poncelet, M., Smalle, E. H. M., & Szmalec, A. (2021). Do serial order short-term memory and long-term learning abilities predict spelling skills in school-age children? Cognition, 206, 1–47. https://doi.org/10.1016/j.cognition.2020.104479
Marjou, X. (2021). OTEANN: estimating the transparency of orthographies with an artificial neural network. Computation and Language, 1–9. https://doi.org/10.18653/v1/2021.sigtyp-1.1.
Miller, R. T. (2019). English orthography and reading. In J. I. Liontas (Ed.), The TESOL encyclopedia of english language teaching: Teaching reading (vol. 1, pp. 1–7). Wiley Online Library. https://doi.org/10.1002/9781118784235.eelt0461
Mo, J., McBride, C., & Yip, L. (2018). Identifying the unique role of orthographic working memory in a computational model of Hong Kong kindergarteners’ Chinese writing spelling. Reading and Writing, 31, 1083–1108. https://doi.org/10.1007/s11145-018-9829-6
Mohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P. (2014). The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. Journal of medical Internet research, 16(6), e146. https://doi.org/10.2196/jmir.3077
Monaghan, P., & Ellis, A. W. (2010). Modeling reading development: Cumulative, incremental learning in a computational model of word naming. Journal of Memory and Language, 63, 506–525. https://doi.org/10.1016/j.jml.2010.08.003
Musso, M. F., & Cascallar, E. C. (2009). New approaches for improved quality in educational assessments: using automated predictive systems in reading and mathematics. Journal of Problems in the 21st Century, 17, 134–151. http://oaji.net/articles/2014/457-1399915068.pdf.
Musso, M. F., Kyndt, E., Cascallar, E. S., & Dochy, F. (2013). Predicting general academic performance and identifying the differential contribution of participating variables using artificial networks. Frontline Learning Research, 1, 31–42. https://doi.org/10.14786/flr/vlil.13
Norris, D. (2017). Short-term memory and long-term memory are still different. Psychological Bulletin, 143, 992–1009.
Olabe, J. C., Basogain, X., & Olabe, M. A. (2019). Modern education with a computational model of the mind. Proceedings of the 2019 3rd International Conference on Education and E Learning (ICEEL) (pp. 41–45). ACM Digital Library. https://doi.org/10.1145/3371647/3371666
Oller, J. W., Jr. (1978). The language factor in the evaluation of bilingual education. In J. Alatis (Ed.), International Dimension of Bilingual Education (pp. 410–422). Georgetown University Press.
Oxford, R. L. (1990a). Language learning strategies: What every teacher should know. Heinle & Heinle.
Oxford, R. L. (1990a). Language learning strategies and beyond: A look at strategies in the context of style. In S. S. Magnan (Ed.), Shifting the Instructional Focus to the Learner (pp. 35–55). Northeast Conference on the Teaching of Foreign Languages.
Oxford, R. L. (1992). Research on second language learning strategies. Annual Review of Applied Linguistics, 13, 174–187. https://doi.org/10.1017/s026719500002452
Packard, J. L., Chen, X., Li, W., Wu, X., Gaffney, J. S., Li, H., & Anderson, R. C. (2006). Explicit instruction in orthographic structure and word morphology helps Chinese children learn to write characters. Reading and Writing, 19, 457–487. https://doi.org/10.1007/s11145-006-9003-4
Perry, C., Ziegler, J. C., & Zorzi, M. (2013). A computational and empirical investigation of graphemes in reading. Cognitive Science, 1–29. https://doi.org/10.1111/cogs.12030.
Persuh, M., LaRock, E., & Berger, J. (2018). Working memory and consciousness: the current state of play. Frontiers in Human Neuroscience, 12(78). https://doi.org/10.3389/fnhum.2018.00078.
Plunket, K., & Marchman, V. (1992). U-shaped learning and frequency effect in a multi-layer perceptron: Implication for child language acquisition. Cognition, 38(1), 43–102. https://doi.org/10.1016/0010-0277(91)90022-v
Plunket, K., & Marchman, V. (1993). From rote learning to system building: Acquiring verb morphology in children and connectionist nets. Cognition, 48(1), 21–69. https://doi.org/10.1016/0010-0277(93)90057-3
Pronskikh, V. (2019). Computer modeling and simulation: Increasing reliability by disentangling verification and validation. Minds and Machines., 29, 169–186.
Robinson, M. M., & Steyvers, M. (2022). Linking computational models of two core tasks of cognitive control. Psychological Review. Advance Online Publication. https://doi.org/10.1037/rev0000395.
Rodriguez-Hernandez, C. F., Musso, M., Kyndt, E., & Cascallar, E. (2021). Artificial neural networks in academic performance prediction: systematic implementation and predictor evaluation. Computers and Education: Artificial Intelligence, 2, 100018. https://doi.org/10.1016/j.ca.eai.2021.100018
Rohde, D. L. T., & Plaut, D. C. (2003). Connectionist models of language processing. Cognitive Studies, 10(1), 10–28. https://ni.cmu.edu/~plaut/papers/pdf/RohdePlaut03CogStu.connModelsLang.pdf
Romer, R., Graben, P. B., Huber-Liebl, M., & Wolf, M. (2022). Unifying interaction, linguistic communication, and language acquisition of cognitive agents by minimalist grammar. Frontier in Computer Science, 4, 733596. https://doi.org/10.3389/fcomp.2022.733596.
Rothman, J., & Slabakova, R. (2018). The generative approach to SLA and its place in modern second language studies. Studies in Second Language Acquisition, 40(2), 417–442. https://doi.org/10.1017/s0272263117000134
Sankararaman, S., & Mahadevan, S. (2013). Assessing the reliability of computational models under uncertainty. Collection of Technical Papers – 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference. Boston, Massachusetts. Research Gate. https://doi.org/10.2514/6.2013-1873
Sipra, M. A. (2013). Impact of orthography on L2 acquisition. English Language Teaching, 6(3), 116–124. https://doi.org/10.5539/elt.v6np116
Schmidt, R. (2001). Attention. In P. Robinson (Ed.), Cognition and Second Language Instruction. Cambridge University Press.
Shao, M-K, Mang, J., Li, P-I, Wang, J., Deng, T., Xu, Z-x. (2015). Computer-based cognitive programs for improvement of memory, processing speed and executive function during age-related cognitive decline: A meta-analysis. PLoS ONE, 10(6), e0130831. https://doi.org/10.1371/journal.pone.0130831
Staels, E., & Van den Broeck, W. (2013). Orthographic learning and the role of text-to-speech software in Dutch disabled readers. Journal of LearningDisabilities,48(1), 1–12. https://doi.org/10.1177/0022219413487470.
Thorne, S.L., & Smith, B. (2011). Second language development theories and technology-mediated language learning. CALCIO Journal, 28(2), 268-277.https://doi.org/10.11139/cj/28.2.268-277.
Van Patten, B. (1996). Input processing and grammar instruction in second language acquisition. Ablex.
Walczak, S., & Cerpa, N. (2003). Artificial neural networks. In R. A. Meyers (Ed.), Encyclopedia of physical science and technology (3rd., pp. 631–645). Ramtech, Inc., Tarzana, California: Academic Press. https://doi.org/10.1016/B0-12-227410-5/00837-1
Welby, P., Spinelli, E., & Burki, A. (2022). Spelling provides a precise (but sometimes misplaced) phonological target orthography and acoustic variability in second language word learning. Journal of Phonetics, 94, 1–16. https://doi.org/10.1016/j.wocn.2022.101172
Wong, Y. K. (2020). The relationship between orthographic awareness and Chinese reading in Hong Kong’s young CSL learners. ECNU Review of Education, 3(4), 678–693. https://doi.org/10.1177/2096531120930485
Xie, Y., Xu, Q., Liu, L., Yao, M., Liu, P., Tong, M., Hong, Q., & Chi, X. (2022). Development and evaluation of orthographic knowledge awareness scale for children aged 6–12 years. Frontiers in Psychology, 12(13), 874891. https://doi.org/10.3389/fpsyg.2022.874891
Yang, J., & Thomas, M. S. C., & Liu, H. T. (2017). Role extraction from autoencoder-based connectionist computational models. Concurrency and Computation: Practice & Experience. https://doi.org/10.1002/cpe.4262.
Yang, J., Thomas, M. S. C., Qi, X., & Liu, X. (2019). Using an ANN-based computational model to simulate and evaluate Chinese students’ individualized cognitive abilities important in their English acquisition. Computer Assisted Language Learning, 32(4), 1–32. https://doi.org/10.1080/09588221.2018.1517125
Yang, Y., & Piantadosi, S. T. (2022). One model for the learning language. PNAS, 119(5), e2021865119. https://doi.org/10.1073//pnas.2021865119.
Zarić, J., Hasselhorn, M., & Nagler, T. (2021). Orthographic knowledge predicts reading and spelling skills over and above general intelligence and phonological awareness. European Journal of Psychology of Education, 36, 21–43. https://doi.org/10.1007/s10212-020-00464-7
Zebardast, B., Maleki, I., & Maroufi, A. (2014). A novel multilayer perceptron artificial neural network based on recognition for Kurdish manuscript. Indian Journal of Science and Technology, 7(3), 343–351.
Zhang, W., Zhang, L., Liu, L., & Zhang, S. (2020). Improving orthographic awareness and reading fluency in Chinese children with dyslexia: A case study. Reading and Writing Quarterly, 37(1), 1–6. https://doi.org/10.1080/10573569.2019.1707731
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no competing interests to declare that are relevant to the content of this article.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendices
Appendix A
Dataset1 (English Nouns and its Plural Variation Orthographies Used in Task A)
Week Number | English Nouns | Binary Code Used | Plural Making Orthography | Binary Code Used |
|---|---|---|---|---|
1 | Pen | 010000000000000000000000000000000 | [s] | 10111 |
Stamp | 001000000000000000000000000000000 | [s] | 10111 | |
Ticket | 000100000000000000000000000000000 | [s] | 10111 | |
Dinner | 000010000000000000000000000000000 | [s] | 10111 | |
Socks | 000001000000000000000000000000000 | [ɸ] | 00000 | |
Study | 000000100000000000000000000000000 | [es] | 10011 | |
Pilot | 000000010000000000000000000000000 | [s] | 10111 | |
2 | Nephew | 000000001000000000000000000000000 | [s] | 10111 |
Pants | 000000000100000000000000000000000 | [ɸ] | 00000 | |
August | 100000000000000000000000000000000 | [s] | 10111 | |
Taxi | 000000000010000000000000000000000 | [es] | 10011 | |
Kitchen | 000000000001000000000000000000000 | [s] | 10111 | |
Mouse | 000000000000100000000000000000000 | [vc] | 11111 | |
Plant | 000000000000010000000000000000000 | [s] | 10111 | |
3 | Sugar | 000000000000001000000000000000000 | [s] | 10111 |
Stairs | 000000000000000100000000000000000 | [ɸ] | 00000 | |
Candy | 000000000000000010000000000000000 | [es] | 10011 | |
Street | 000000000000000001000000000000000 | [s] | 10111 | |
Foot | 000000000000000000100000000000000 | [vc] | 11111 | |
Daughter | 000000000000000000010000000000000 | [s] | 10111 | |
April | 000000000000000000001000000000000 | [s] | 10111 | |
4 | Singer | 000000000000000000000100000000000 | [s] | 10111 |
Ferry | 000000000000000000000010000000000 | [es] | 10011 | |
Parrot | 000000000000000000000001000000000 | [s] | 10111 | |
Tooth | 000000000000000000000000100000000 | [vc] | 11111 | |
Jacket | 000000000000000000000000010000000 | [s] | 10111 | |
Shorts | 000000000000000000000000001000000 | [ɸ] | 00000 | |
Skirt | 000000000000000000000000000100000 | [s] | 10111 | |
5 | Raincoat | 000000000000000000000000000010000 | [s] | 10111 |
Model | 000000000000000000000000000001000 | [s] | 10111 | |
Scarf | 000000000000000000000000000000100 | [es] | 10011 | |
Earrings | 000000000000000000000000000000010 | [ɸ] | 00000 | |
Start | 000000000000000000000000000000001 | [s] | 10111 | |
Finish | 110000000000000000000000000000000 | [es] | 10011 | |
Goose | 101000000000000000000000000000000 | [vc] | 11111 | |
6 | Tiger | 100100000000000000000000000000000 | [s] | 10111 |
Woman | 100010000000000000000000000000000 | [vc] | 11111 | |
Blank | 100001000000000000000000000000000 | [s] | 10111 | |
World | 100000100000000000000000000000000 | [s] | 10111 | |
Anthem | 100000010000000000000000000000000 | [s] | 10111 | |
Police | 100000001000000000000000000000000 | [ɸ] | 00000 | |
Bus | 100000000100000000000000000000000 | [es] | 10011 | |
7 | Teacher | 100000000010000000000000000000000 | [s] | 10111 |
Drink | 100000000001000000000000000000000 | [s] | 10111 | |
Curtains | 100000000000100000000000000000000 | [ɸ] | 00000 | |
Garage | 100000000000010000000000000000000 | [s] | 10111 | |
Mother | 100000000000001000000000000000000 | [s] | 10111 | |
Toilet | 100000000000000100000000000000000 | [s] | 10111 | |
Factory | 100000000000000010000000000000000 | [es] | 10011 | |
8 | Box | 100000000000000001000000000000000 | [es] | 10011 |
Sister | 100000000000000000100000000000000 | [s] | 10111 | |
Ceiling | 100000000000000000010000000000000 | [s] | 10111 | |
Man | 100000000000000000001000000000000 | [vc] | 11111 | |
Potato | 100000000000000000000100000000000 | [es] | 10011 | |
Scissors | 100000000000000000000010000000000 | [ɸ] | 00000 | |
Pajamas | 100000000000000000000001000000000 | [ɸ] | 00000 | |
Cactus | 100000000000000000000000100000000 | [vc] | 11111 | |
Square | 100000000000000000000000010000000 | [s] | 10111 | |
Water | 100000000000000000000000001000000 | [s] | 10111 |
Dataset2 (English Nouns and its Plural Variation Orthographies Used in Task B)
Week Number | English Nouns | Binary Code Used | Plural Making Orthography | Binary Code Used |
|---|---|---|---|---|
1 | Classroom | 010000000000000000000000000000000 | [s] | 10111 |
Board | 001000000000000000000000000000000 | [s] | 10111 | |
Book | 000100000000000000000000000000000 | [s] | 10111 | |
Calculator | 000010000000000000000000000000000 | [s] | 10111 | |
Boots | 000001000000000000000000000000000 | [ɸ] | 00000 | |
City | 000000100000000000000000000000000 | [es] | 10011 | |
Clock | 000000010000000000000000000000000 | [s] | 10111 | |
2 | Pencil | 000000001000000000000000000000000 | [s] | 10111 |
Jeans | 000000000100000000000000000000000 | [ɸ] | 00000 | |
Notebook | 100000000000000000000000000000000 | [s] | 10111 | |
Address | 000000000010000000000000000000000 | [es] | 10011 | |
Chair | 000000000001000000000000000000000 | [s] | 10111 | |
Salesman | 000000000000100000000000000000000 | [vc] | 11111 | |
Briefcase | 000000000000010000000000000000000 | [s] | 10111 | |
3 | Table | 000000000000001000000000000000000 | [s] | 10111 |
Parents | 000000000000000100000000000000000 | [ɸ] | 00000 | |
Family | 000000000000000010000000000000000 | [es] | 10011 | |
Camera | 000000000000000001000000000000000 | [s] | 10111 | |
Saleswoman | 000000000000000000100000000000000 | [vc] | 11111 | |
Map | 000000000000000000010000000000000 | [s] | 10111 | |
Friend | 000000000000000000001000000000000 | [s] | 10111 | |
4 | Cap | 000000000000000000000100000000000 | [s] | 10111 |
Tennis | 000000000000000000000010000000000 | [es] | 10011 | |
Language | 000000000000000000000001000000000 | [s] | 10111 | |
Policeman | 000000000000000000000000100000000 | [vc] | 11111 | |
Student | 000000000000000000000000010000000 | [s] | 10111 | |
Clothes | 000000000000000000000000001000000 | [ɸ] | 00000 | |
Suit | 000000000000000000000000000100000 | [s] | 10111 | |
5 | Blouse | 000000000000000000000000000010000 | [s] | 10111 |
Conference | 000000000000000000000000000001000 | [s] | 10111 | |
Gallery | 000000000000000000000000000000100 | [es] | 10011 | |
Physics | 000000000000000000000000000000010 | [ɸ] | 00000 | |
Walk | 000000000000000000000000000000001 | [s] | 10111 | |
Lobby | 110000000000000000000000000000000 | [es] | 10011 | |
Policewoman | 101000000000000000000000000000000 | [vc] | 11111 | |
6 | Breakfast | 100100000000000000000000000000000 | [s] | 10111 |
Snowman | 100010000000000000000000000000000 | [vc] | 11111 | |
House | 100001000000000000000000000000000 | [s] | 10111 | |
Subway | 100000100000000000000000000000000 | [s] | 10111 | |
Down town | 100000010000000000000000000000000 | [s] | 10111 | |
Butter | 100000001000000000000000000000000 | [ɸ] | 00000 | |
Country | 100000000100000000000000000000000 | [es] | 10011 | |
7 | Elevator | 100000000010000000000000000000000 | [s] | 10111 |
Closet | 100000000001000000000000000000000 | [s] | 10111 | |
Headphones | 100000000000100000000000000000000 | [ɸ] | 00000 | |
Cashier | 100000000000010000000000000000000 | [s] | 10111 | |
Lawyer | 100000000000001000000000000000000 | [s] | 10111 | |
Cake | 100000000000000100000000000000000 | [s] | 10111 | |
Brush | 100000000000000010000000000000000 | [es] | 10011 | |
8 | Mango | 100000000000000001000000000000000 | [es] | 10011 |
Hockey | 100000000000000000100000000000000 | [s] | 10111 | |
Guitar | 100000000000000000010000000000000 | [s] | 10111 | |
Snow woman | 100000000000000000001000000000000 | [vc] | 11111 | |
Party | 100000000000000000000100000000000 | [es] | 10011 | |
Shirts | 100000000000000000000010000000000 | [ɸ] | 00000 | |
Glasses | 100000000000000000000001000000000 | [ɸ] | 00000 | |
Louse | 100000000000000000000000100000000 | [vc] | 11111 | |
Picnic | 100000000000000000000000010000000 | [s] | 10111 | |
Message | 100000000000000000000000001000000 | [s] | 10111 |
Dataset3 (English Nouns and its Plural Variation Orthographies Used in Task C)
Week Number | English Nouns | Binary Code Used | Plural Making Orthography | Binary Code Used |
|---|---|---|---|---|
2 | City | 000000100000000000000000000000000 | [es] | 10,011 |
Clock | 000000010000000000000000000000000 | [s] | 10,111 | |
Classroom | 010000000000000000000000000000000 | [s] | 10,111 | |
Board | 001000000000000000000000000000000 | [s] | 10,111 | |
Book | 000100000000000000000000000000000 | [s] | 10,111 | |
Calculator | 000010000000000000000000000000000 | [s] | 10,111 | |
Boots | 000001000000000000000000000000000 | [ɸ] | 00,000 | |
3 | Salesman | 000000000000100000000000000000000 | [vc] | 11,111 |
Briefcase | 000000000000010000000000000000000 | [s] | 10,111 | |
Address | 000000000010000000000000000000000 | [es] | 10,011 | |
Chair | 000000000001000000000000000000000 | [s] | 10,111 | |
Pencil | 000000001000000000000000000000000 | [s] | 10,111 | |
Jeans | 000000000100000000000000000000000 | [ɸ] | 00,000 | |
Notebook | 100000000000000000000000000000000 | [s] | 10,111 | |
4 | Saleswoman | 000000000000000000100000000000000 | [vc] | 11,111 |
Map | 000000000000000000010000000000000 | [s] | 10,111 | |
Friend | 000000000000000000001000000000000 | [s] | 10,111 | |
Parents | 000000000000000100000000000000000 | [ɸ] | 00,000 | |
Family | 000000000000000010000000000000000 | [es] | 10,011 | |
Camera | 000000000000000001000000000000000 | [s] | 10,111 | |
Table | 000000000000001000000000000000000 | [s] | 10,111 | |
5 | Clothes | 000000000000000000000000001000000 | [ɸ] | 00,000 |
Suit | 000000000000000000000000000100000 | [s] | 10,111 | |
Policeman | 000000000000000000000000100000000 | [vc] | 11,111 | |
Student | 000000000000000000000000010000000 | [s] | 10,111 | |
Tennis | 000000000000000000000010000000000 | [es] | 10,011 | |
Language | 000000000000000000000001000000000 | [s] | 10,111 | |
Cap | 000000000000000000000100000000000 | [s] | 10,111 | |
6 | Walk | 000000000000000000000000000000001 | [s] | 10,111 |
Lobby | 110000000000000000000000000000000 | [es] | 10,011 | |
Policewoman | 101000000000000000000000000000000 | [vc] | 11,111 | |
Conference | 000000000000000000000000000001000 | [s] | 10,111 | |
Gallery | 000000000000000000000000000000100 | [es] | 10,011 | |
Physics | 000000000000000000000000000000010 | [ɸ] | 00,000 | |
Blouse | 000000000000000000000000000010000 | [s] | 10,111 | |
7 | Downtown | 100000010000000000000000000000000 | [s] | 10,111 |
Butter | 100000001000000000000000000000000 | [ɸ] | 00,000 | |
Country | 100000000100000000000000000000000 | [es] | 10,011 | |
Breakfast | 100100000000000000000000000000000 | [s] | 10,111 | |
Snowman | 100010000000000000000000000000000 | [vc] | 11,111 | |
House | 100001000000000000000000000000000 | [s] | 10,111 | |
Subway | 100000100000000000000000000000000 | [s] | 10,111 | |
8 | Lawyer | 100000000000001000000000000000000 | [s] | 10,111 |
Cake | 100000000000000100000000000000000 | [s] | 10,111 | |
Brush | 100000000000000010000000000000000 | [es] | 10,011 | |
Closet | 100000000001000000000000000000000 | [s] | 10,111 | |
Headphones | 100000000000100000000000000000000 | [ɸ] | 00,000 | |
Cashier | 100000000000010000000000000000000 | [s] | 10,111 | |
Elevator | 100000000010000000000000000000000 | [s] | 10,111 | |
9 | Louse | 100000000000000000000000100000000 | [vc] | 11,111 |
Picnic | 100000000000000000000000010000000 | [s] | 10,111 | |
Message | 100000000000000000000000001000000 | [s] | 10,111 | |
Party | 100000000000000000000100000000000 | [es] | 10,011 | |
Shirts | 100000000000000000000010000000000 | [ɸ] | 00,000 | |
Glasses | 100000000000000000000001000000000 | [ɸ] | 00,000 | |
Mango | 100000000000000001000000000000000 | [es] | 10,011 | |
Hockey | 100000000000000000100000000000000 | [s] | 10,111 | |
Guitar | 100000000000000000010000000000000 | [s] | 10,111 | |
Snowwoman | 100000000000000000001000000000000 | [vc] | 11,111 |
Appendix B
Appendix C
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Parvizi, GR., Tavakoli, M., Amiryousefi, M. et al. Simulating and evaluating individualized cognitive abilities of Iranian EFL learners in orthography acquisition using multi-layer perceptron neural network–gray wolf optimizer computational model. Educ Inf Technol 29, 5753–5806 (2024). https://doi.org/10.1007/s10639-023-11825-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-023-11825-2