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A Novel Approach to Identify Difficult Words in English to Promote Vocabulary Acquisition of Children Dually Diagnosed with Autism Spectrum Disorder and Hearing Loss

Yıl 2023, Cilt: 8 Sayı: 1, 25 - 31, 08.01.2023
https://doi.org/10.53850/joltida.1078232

Öz

Usage of complex words causes significant problems not only in reading but in writing as well and eventually leads to poor academic achievement of students, poorer particularly for hearing impaired children. The dual diagnosis of Autism Spectrum Disorder (ASD) and hearing impairment pose additional challenges mainly due to the difficulties that come with making accurate decisions. Hence, parents must be provided with the information about the signs and symptoms of ASD and deafness or partial hearing loss, as well as appropriate intervention strategies. Although different learning activities can be used to enlarge such children’s vocabulary, if the presented words are difficult to learn, it will be very hard to realize this. Identifying difficult words and replacing them with simple ones both make the readability of a text easier and help such children enhance their vocabulary knowledge in a shorter period of time. Therefore, in this study we propose a classification approach that identifies difficult words among a given set of words in English. The lexical and semantic features of the words in the dataset were extracted based on the language rules specific to hearing impaired children. In the classification approach, five popular classification algorithms were used and the algorithms' performance in identifying difficult words was evaluated using various performance metrics. As the results show, the K-Nearest Neighbors algorithm is the most suitable algorithm for identifying difficult words in English for the target group.

Kaynakça

  • Ansar, M., Qamar, U., Bibi, R., & Shaheen, A. (2019). Identification of Difficult English Words for Assisting Hearing Impaired Children in Learning Language. 2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA), 60-65. doi: 10.1109/SERA.2019.8886796.
  • Berent, G. P. (2001). English for deaf students: Assessing and addressing learners' grammar development. In D. Janáková (Ed.), International Seminar on Teaching English to Deaf and Hard-of-Hearing Students at Secondary and Tertiary Levels of Education: Proceedings (pp. 124-134). Prague, Czech Republic: Charles University, The Karolinum Press.
  • Bermejo, P., Gámez, J. A., & Puerta, J. M. (2011). Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets. Expert Systems with Applications, 38(3), 2072-2080. doi: 10.1016/j.eswa.2010.07.146
  • Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159. doi: 10.1016/S0031-3203(96)00142-2
  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Routledge. doi: 10.4324/9780203774441
  • Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297. doi: 10.1007/BF00994018
  • Han, N., Wu, J., Fang, X., Wen, J., Zhan, S., Xie, S., & Li, X. (2020). Transferable Linear Discriminant Analysis. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5630-5638. doi: 10.1109/TNNLS.2020.2966746
  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. doi: 10.1016/j.ijforecast.2006.03.001
  • Jiang, S., Pang, G., Wu, M., & Kuang, L. (2012). An improved K-nearest-neighbor algorithm for text categorization. Expert Systems with Applications, 39(1), 1503-1509. doi: 10.1016/j.eswa.2011.08.040
  • Li, Y., Liu, B., Yu, Y., Li, H., Sun, J., & Cui, J. (2021). 3E-LDA: Three Enhancements to Linear Discriminant Analysis. ACM Transactions on Knowledge Discovery from Data, 15(4), 57. doi: 10.1145/3442347
  • Ma, C., Du, X., & Cao, L. (2020). Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow. Electronics, 9(2), 324. Retrieved from http://dx.doi.org/10.3390/electronics9020324
  • Martínez-Camblor, P., Pérez-Fernández, S. & Díaz-Coto, S. (2021). The area under the generalized receiver-operating characteristic curve. The International Journal of Biostatistics, 20200091. doi: 10.1515/ijb-2020-0091
  • McHugh M. L. (2012). Interrater reliability: the kappa statistic. Biochemia medica, 22(3), 276-282.
  • McLachlan, G. J. (1992). Discriminant analysis and statistical pattern recognition. Hoboken, NJ, USA: John Wiley & Sons.
  • McTee, H. M., Mood, D., Fredrickson, T., Thrasher, A., & Bonino, A. Y. (2019). Using Visual Supports to Facilitate Audiological Testing for Children with Autism Spectrum Disorder. American journal of audiology, 28(4), 823-833. doi: 10.1044/2019_AJA-19-0047
  • Myck-Wayne, J., Robinson, S., & Henson, E. (2011). Serving and Supporting Young Children with a Dual Diagnosis of Hearing Loss and Autism: The Stories of Four Families. American Annals of the Deaf, 156(4), 379-90. doi: 10.1353/aad.2011.0032
  • Phillips, H., Wright, B., Allgar, V., McConachie, H., Sweetman, J., Hargate, R., Hodkinson, R., Bland, M., George, H., Hughes, A., Hayward, E., De Las Heras, V., & Le Couteur, A. (2022). Adapting and validating the Autism Diagnostic Observation Schedule Version 2 for use with deaf children and young people. Journal of autism and developmental disorders, 52(2), 553-568. doi: 10.1007/s10803-021-04931-y
  • Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Quigley, S. P., & King, C. M. (1980). Syntactic performance of hearing impaired and normal hearing individuals. Applied Psycholinguistics, 1(4), 329-356. doi: 10.1017/S0142716400000990
  • Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers. Szymanski, C., & Brice, P. J. (2008). When Autism and Deafness Coexist in Children: What We Know Now. Odyssey: New Directions in Deaf Education, 9(1), 10-15.
  • Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. doi: 10.1016/j.aci.2018.08.003
  • Trudeau, S., Anne, S., Otteson, T., Hopkins, B., Georgopoulos, R., & Wentland, C. (2021). Diagnosis and patterns of hearing loss in children with severe developmental delay. Am J Otolaryngol, 42(3), 102923. doi:10.1016/j.amjoto.2021.102923
  • VanDam, M, & Yoshinaga-Itano, C. (2019). Use of the LENA autism screen with children who are deaf or hard of hearing. Medicina (Kaunas), 55(8), 495. doi:10.3390/medicina55080495
  • Wiley, S., Gustafson, S., & Rozniak, J. (2014). Needs of parents of children who are deaf/hard of hearing with autism spectrum disorder. Journal of deaf studies and deaf education, 19(1), 40-49. doi: 10.1093/deafed/ent044
  • Xia, S., Xiong, Z., Luo, Y., Dong, L. & Zhang, G. (2015). Location difference of multiple distances based k-nearest neighbors algorithm. Knowledge-Based Systems, 90, 99-110. doi: 10.1016/j.knosys.2015.09.028
Yıl 2023, Cilt: 8 Sayı: 1, 25 - 31, 08.01.2023
https://doi.org/10.53850/joltida.1078232

Öz

Kaynakça

  • Ansar, M., Qamar, U., Bibi, R., & Shaheen, A. (2019). Identification of Difficult English Words for Assisting Hearing Impaired Children in Learning Language. 2019 IEEE 17th International Conference on Software Engineering Research, Management and Applications (SERA), 60-65. doi: 10.1109/SERA.2019.8886796.
  • Berent, G. P. (2001). English for deaf students: Assessing and addressing learners' grammar development. In D. Janáková (Ed.), International Seminar on Teaching English to Deaf and Hard-of-Hearing Students at Secondary and Tertiary Levels of Education: Proceedings (pp. 124-134). Prague, Czech Republic: Charles University, The Karolinum Press.
  • Bermejo, P., Gámez, J. A., & Puerta, J. M. (2011). Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets. Expert Systems with Applications, 38(3), 2072-2080. doi: 10.1016/j.eswa.2010.07.146
  • Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159. doi: 10.1016/S0031-3203(96)00142-2
  • Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd ed.). Routledge. doi: 10.4324/9780203774441
  • Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20(3), 273-297. doi: 10.1007/BF00994018
  • Han, N., Wu, J., Fang, X., Wen, J., Zhan, S., Xie, S., & Li, X. (2020). Transferable Linear Discriminant Analysis. IEEE Transactions on Neural Networks and Learning Systems, 31(12), 5630-5638. doi: 10.1109/TNNLS.2020.2966746
  • Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. doi: 10.1016/j.ijforecast.2006.03.001
  • Jiang, S., Pang, G., Wu, M., & Kuang, L. (2012). An improved K-nearest-neighbor algorithm for text categorization. Expert Systems with Applications, 39(1), 1503-1509. doi: 10.1016/j.eswa.2011.08.040
  • Li, Y., Liu, B., Yu, Y., Li, H., Sun, J., & Cui, J. (2021). 3E-LDA: Three Enhancements to Linear Discriminant Analysis. ACM Transactions on Knowledge Discovery from Data, 15(4), 57. doi: 10.1145/3442347
  • Ma, C., Du, X., & Cao, L. (2020). Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow. Electronics, 9(2), 324. Retrieved from http://dx.doi.org/10.3390/electronics9020324
  • Martínez-Camblor, P., Pérez-Fernández, S. & Díaz-Coto, S. (2021). The area under the generalized receiver-operating characteristic curve. The International Journal of Biostatistics, 20200091. doi: 10.1515/ijb-2020-0091
  • McHugh M. L. (2012). Interrater reliability: the kappa statistic. Biochemia medica, 22(3), 276-282.
  • McLachlan, G. J. (1992). Discriminant analysis and statistical pattern recognition. Hoboken, NJ, USA: John Wiley & Sons.
  • McTee, H. M., Mood, D., Fredrickson, T., Thrasher, A., & Bonino, A. Y. (2019). Using Visual Supports to Facilitate Audiological Testing for Children with Autism Spectrum Disorder. American journal of audiology, 28(4), 823-833. doi: 10.1044/2019_AJA-19-0047
  • Myck-Wayne, J., Robinson, S., & Henson, E. (2011). Serving and Supporting Young Children with a Dual Diagnosis of Hearing Loss and Autism: The Stories of Four Families. American Annals of the Deaf, 156(4), 379-90. doi: 10.1353/aad.2011.0032
  • Phillips, H., Wright, B., Allgar, V., McConachie, H., Sweetman, J., Hargate, R., Hodkinson, R., Bland, M., George, H., Hughes, A., Hayward, E., De Las Heras, V., & Le Couteur, A. (2022). Adapting and validating the Autism Diagnostic Observation Schedule Version 2 for use with deaf children and young people. Journal of autism and developmental disorders, 52(2), 553-568. doi: 10.1007/s10803-021-04931-y
  • Platt, J. (1998). Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Quigley, S. P., & King, C. M. (1980). Syntactic performance of hearing impaired and normal hearing individuals. Applied Psycholinguistics, 1(4), 329-356. doi: 10.1017/S0142716400000990
  • Quinlan, J. R. (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers. Szymanski, C., & Brice, P. J. (2008). When Autism and Deafness Coexist in Children: What We Know Now. Odyssey: New Directions in Deaf Education, 9(1), 10-15.
  • Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. doi: 10.1016/j.aci.2018.08.003
  • Trudeau, S., Anne, S., Otteson, T., Hopkins, B., Georgopoulos, R., & Wentland, C. (2021). Diagnosis and patterns of hearing loss in children with severe developmental delay. Am J Otolaryngol, 42(3), 102923. doi:10.1016/j.amjoto.2021.102923
  • VanDam, M, & Yoshinaga-Itano, C. (2019). Use of the LENA autism screen with children who are deaf or hard of hearing. Medicina (Kaunas), 55(8), 495. doi:10.3390/medicina55080495
  • Wiley, S., Gustafson, S., & Rozniak, J. (2014). Needs of parents of children who are deaf/hard of hearing with autism spectrum disorder. Journal of deaf studies and deaf education, 19(1), 40-49. doi: 10.1093/deafed/ent044
  • Xia, S., Xiong, Z., Luo, Y., Dong, L. & Zhang, G. (2015). Location difference of multiple distances based k-nearest neighbors algorithm. Knowledge-Based Systems, 90, 99-110. doi: 10.1016/j.knosys.2015.09.028
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri
Bölüm Research Article
Yazarlar

Ebru Efeoğlu 0000-0001-5444-6647

Ayşe Tuna 0000-0003-2666-1694

Yayımlanma Tarihi 8 Ocak 2023
Gönderilme Tarihi 23 Şubat 2022
Yayımlandığı Sayı Yıl 2023 Cilt: 8 Sayı: 1

Kaynak Göster

APA Efeoğlu, E., & Tuna, A. (2023). A Novel Approach to Identify Difficult Words in English to Promote Vocabulary Acquisition of Children Dually Diagnosed with Autism Spectrum Disorder and Hearing Loss. Journal of Learning and Teaching in Digital Age, 8(1), 25-31. https://doi.org/10.53850/joltida.1078232

Journal of Learning and Teaching in Digital Age 2023. © 2023. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. 19195

Journal of Learning and Teaching in Digital Age. All rights reserved, 2023. ISSN:2458-8350