Intelligent Modeling for In-Home Reading and Spelling Programs
Technology-based in-home reading and spelling programs have the potential to compensate for the lack of sufficient instructions provided at schools. However, the recent COVID-19 pandemic showed the immaturity of the existing remote teaching solutions. Consequently, many students did not receive the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/12/3/56 |
| _version_ | 1797612586169008128 |
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| author | Hossein Jamshidifarsani Samir Garbaya Ioana Andreea Stefan |
| author_facet | Hossein Jamshidifarsani Samir Garbaya Ioana Andreea Stefan |
| author_sort | Hossein Jamshidifarsani |
| collection | DOAJ |
| description | Technology-based in-home reading and spelling programs have the potential to compensate for the lack of sufficient instructions provided at schools. However, the recent COVID-19 pandemic showed the immaturity of the existing remote teaching solutions. Consequently, many students did not receive the necessary instructions. This paper presents a model for developing intelligent reading and spelling programs. The proposed approach is based on an optimization model that includes artificial neural networks and linear regression to maximize the educational value of the pedagogical content. This model is personalized, tailored to the learning ability level of each user. Regression models were developed for estimating the lexical difficulty in the literacy tasks of auditory and visual lexical decision, word naming, and spelling. For building these regression models, 55 variables were extracted from French lexical databases that were used with the data from lexical mega-studies. Forward stepwise analysis was conducted to identify the top 10 most important variables for each lexical task. The results showed that the accuracy of the models (based on root mean square error) reached 88.13% for auditory lexical decision, 89.79% for visual lexical decision, 80.53% for spelling, and 83.86% for word naming. The analysis of the results showed that word frequency was a key predictor for all the tasks. For spelling, the number of irregular phoneme-graphemes was an important predictor. The auditory word recognition depended heavily on the number of phonemes and homophones, while visual word recognition depended on the number of homographs and syllables. Finally, the word length and the consistency of initial grapheme-phonemes were important for predicting the word-naming reaction times. |
| first_indexed | 2024-03-11T06:43:12Z |
| format | Article |
| id | doaj.art-d7a500b579b94cde8f1f3c93d95fe37a |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-11T06:43:12Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-d7a500b579b94cde8f1f3c93d95fe37a2023-11-17T10:26:28ZengMDPI AGComputers2073-431X2023-03-011235610.3390/computers12030056Intelligent Modeling for In-Home Reading and Spelling ProgramsHossein Jamshidifarsani0Samir Garbaya1Ioana Andreea Stefan2END-ICAP Laboratory—INSERM, University of Versailles Saint-Quentin-en-Yvelines—Paris-Saclay, 78180 Montigny-le-Bretonneux, FranceArts et Metiers Institute of Technology, CNAM, LIFSE, END-ICAP-INSERM U1179, HESAM University, 75013 Paris, FranceAdvanced Technology Systems—ATS, 130029 Targoviste, RomaniaTechnology-based in-home reading and spelling programs have the potential to compensate for the lack of sufficient instructions provided at schools. However, the recent COVID-19 pandemic showed the immaturity of the existing remote teaching solutions. Consequently, many students did not receive the necessary instructions. This paper presents a model for developing intelligent reading and spelling programs. The proposed approach is based on an optimization model that includes artificial neural networks and linear regression to maximize the educational value of the pedagogical content. This model is personalized, tailored to the learning ability level of each user. Regression models were developed for estimating the lexical difficulty in the literacy tasks of auditory and visual lexical decision, word naming, and spelling. For building these regression models, 55 variables were extracted from French lexical databases that were used with the data from lexical mega-studies. Forward stepwise analysis was conducted to identify the top 10 most important variables for each lexical task. The results showed that the accuracy of the models (based on root mean square error) reached 88.13% for auditory lexical decision, 89.79% for visual lexical decision, 80.53% for spelling, and 83.86% for word naming. The analysis of the results showed that word frequency was a key predictor for all the tasks. For spelling, the number of irregular phoneme-graphemes was an important predictor. The auditory word recognition depended heavily on the number of phonemes and homophones, while visual word recognition depended on the number of homographs and syllables. Finally, the word length and the consistency of initial grapheme-phonemes were important for predicting the word-naming reaction times.https://www.mdpi.com/2073-431X/12/3/56educational technologyin-home learningliteracyneural networks |
| spellingShingle | Hossein Jamshidifarsani Samir Garbaya Ioana Andreea Stefan Intelligent Modeling for In-Home Reading and Spelling Programs Computers educational technology in-home learning literacy neural networks |
| title | Intelligent Modeling for In-Home Reading and Spelling Programs |
| title_full | Intelligent Modeling for In-Home Reading and Spelling Programs |
| title_fullStr | Intelligent Modeling for In-Home Reading and Spelling Programs |
| title_full_unstemmed | Intelligent Modeling for In-Home Reading and Spelling Programs |
| title_short | Intelligent Modeling for In-Home Reading and Spelling Programs |
| title_sort | intelligent modeling for in home reading and spelling programs |
| topic | educational technology in-home learning literacy neural networks |
| url | https://www.mdpi.com/2073-431X/12/3/56 |
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