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Automated reading level classification model based on improved orbital pattern

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Abstract

Automatic reading level for detection and classification is a challenging problem in machine learning. A multilevel feature extraction-based self-organized model may be useful to overcome this hurdle without using deep learning, which requires an ultra-large sample size. In this work, a novel speech dataset was collected from 57 primary school students by reading a fixed paragraph, and experts labeled these speeches as good, moderate, or bad. We then developed a handcrafted, self-organized learning model. We constructed a novel method using a multilevel feature extraction method, termed improved orbital pattern (IOP) and wavelet packet decomposition (WPD). The proposed IOP generates textural features from the speeches and the used wavelet bands. These extracted features are input to neighborhood components analysis (NCA) to reduce feature dimension. Then the feature set is input to the support vector machine (SVM) classifier to obtain loss values. The output of ten feature vectors of the NCA and SVM classifiers are merged to provide the final feature vector. The most significant 512 features were selected using the NCA feature selection function. These 512 features are classified via the SVM classifier with tenfold cross-validation (CV) and leave-one-subject-out (LOSO) validation strategies. The proposed IOP and WPD-based model yielded an accuracy of 92.75% with a tenfold CV and a 76.18% accuracy using LOSO validation strategies in classifying bad, intermediate, and good reading levels. Our developed model is ready to be validated with more data before its actual usage in schools to aid the teachers.

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References

  1. Khumsamart S (2022) Innovative Management Strategies for the Office of Non-Formal and Informal Education in the Digital Age. J Manag Bus Healthcare, Educ 1(2):1–21

    Google Scholar 

  2. Ribble M, Park M (2022) The digital citizenship handbook for school leaders: Fostering positive interactions online. Int Soc Technol Educ, Washington, DC.

  3. Liu C, Chung KKH, Tang PM (2022) Contributions of orthographic awareness, letter knowledge, and patterning skills to Chinese literacy skills and arithmetic competence. Educ Psychol 42(5):530–548

    Article  Google Scholar 

  4. Pae HK (2022) Toward a script relativity hypothesis: Focused research agenda for psycholinguistic experiments in the science of reading. J Cult Cogn Sci 6:1–21

    Article  Google Scholar 

  5. Petersen SE, Ostendorf M (2009) A machine learning approach to reading level assessment. Comput Speech Lang 23(1):89–106

    Article  Google Scholar 

  6. Ecalle J, Magnan A, Auphan P, Gomes C, Cros L, Suchaut B (2022) Effects of targeted interventions and of specific instructional time on reading ability in French children in grade 1. Eur J Psychol Educ 37(3):605–625

    Article  Google Scholar 

  7. Middleton AE, Farris EA, Ring JJ, Odegard TN (2022) Predicting and evaluating treatment response: Evidence toward protracted response patterns for severely impacted students with dyslexia. J Learn Disabil 55(4):272–291

    Article  Google Scholar 

  8. Kwon S (2021) Att-Net: Enhanced emotion recognition system using lightweight self-attention module. Appl Soft Comput 102:107101

    Article  Google Scholar 

  9. Kwon S (2020) CLSTM: Deep feature-based speech emotion recognition using the hierarchical ConvLSTM network. Mathematics 8(12):2133

    Article  Google Scholar 

  10. Ahmad F, Ikram S, Ahmad J, Ullah W, Hassan F, Khattak SU, Rehman IU (2020) GASPIDs Versus Non-GASPIDs-Differentiation Based on Machine Learning Approach. Curr Bioinform 15(9):1056–1064

    Article  Google Scholar 

  11. Ullah W, Muhammad K, UlHaq I, Ullah A, UllahKhattak S, Sajjad M (2021) Splicing sites prediction of human genome using machine learning techniques. Multimed Tools Appl 80(20):30439–30460

    Article  Google Scholar 

  12. Shaikh AA, Kumar A, Jani K, Mitra S, García-Tadeo DA, Devarajan A (2022) The Role of Machine Learning and Artificial Intelligence for making a Digital Classroom and its sustainable Impact on Education during Covid-19. Mater Today: Proceed 56:3211–3215

    Google Scholar 

  13. Alam A (2022) Employing Adaptive Learning and Intelligent Tutoring Robots for Virtual Classrooms and Smart Campuses: Reforming Education in the Age of Artificial Intelligence. In: Advanced Computing and Intelligent Technologies. Springer, pp 395–406. https://doi.org/10.1007/978-981-19-2980-9_32

  14. Luo Q, Su J, Yang C, Silven O, Liu L (2022) Scale-selective and noise-robust extended local binary pattern for texture classification. Pattern Recogn 132:108901

    Article  Google Scholar 

  15. Wei J, Lu G, Yan J, Liu H (2022) Micro-expression recognition using local binary pattern from five intersecting planes. Multimed Tools Appl 81:1–26. https://doi.org/10.1007/s11042-022-12360-x

    Article  Google Scholar 

  16. Nayak SK, Jarzębski M, Gramza-Michałowska A, Pal K (2022) Automated Detection of Cannabis-Induced Alteration in Cardiac Autonomic Regulation of the Indian Paddy-Field Workers Using Empirical Mode Decomposition, Discrete Wavelet Transform and Wavelet Packet Decomposition Techniques with HRV Signals. Appl Sci 12(20):10371

    Article  Google Scholar 

  17. Chaabane SB, Hijji M, Harrabi R, Seddik H (2022) Face recognition based on statistical features and SVM classifier. Multimed Tools Appl 81(6):8767–8784

    Article  Google Scholar 

  18. Goldberger J, Hinton GE, Roweis S, Salakhutdinov RR (2004) Neighbourhood components analysis. Adv Neural Inf Process Syst 17:513–520

    Google Scholar 

  19. Bolaños D, Cole RA, Ward WH, Tindal GA, Hasbrouck J, Schwanenflugel PJ (2013) Human and automated assessment of oral reading fluency. J Educ Psychol 105(4):1142

    Article  Google Scholar 

  20. Kodan H (2017) Determination of Reading Levels of Primary School Students. Univ J Educ Res 5(11):1962–1969

    Google Scholar 

  21. Xiao Y, Hu J (2019) Assessment of optimal pedagogical factors for Canadian ESL learners’ reading literacy through artificial intelligence algorithms. Int J English Linguist 9(4):1–14

    Article  Google Scholar 

  22. Babayigit O (2018) Evaluation of Reading Rates according to Word Length of Primary School Students. Mehmet Akif Ersoy University Journal of Education Faculty 46:409–427. https://doi.org/10.21764/maeuefd.330831

    Article  Google Scholar 

  23. Ulu M (2017) The Effect of Reading Comprehension and Problem Solving Strategies on Classifying Elementary 4th Grade Students with High and Low Problem Solving Success. J Educ Train Stud 5(6):44–63

    Article  Google Scholar 

  24. Hoskins WH, Hobbs WI, Eason MJ, Decker S, Tang J (2021) The design and implementation of the Carolina Automated Reading Evaluation for reading deficit screening. Comput Human Behav Rep 4:100123. https://doi.org/10.1016/j.chbr.2021.100123

    Article  Google Scholar 

  25. Kaggle (2021) Reading Sounds of According to Reading Levels. http://www.kaggle.com/dataset/9b7cfc0b0ad942b79585629b5c66d17687830c6f5f388bdb4ae5c7dd8a066d23. Accessed 10.05.2022

  26. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  27. Ahonen T, Hadid A, Pietikäinen M (2004) Face recognition with local binary patterns. European conference on computer vision. Springer, pp 469–481

    Google Scholar 

  28. Raju G (2008) Wavelet transform and projection profiles in handwritten character recognition-A performance analysis. In: 2008 16th International conference on advanced computing and communications. IEEE, pp 309–314. https://doi.org/10.1109/ADCOM.2008.4760466

  29. Abo-Zahhad M, Rajoub BA (2002) An effective coding technique for the compression of one-dimensional signals using wavelet transforms. Med Eng Phys 24(3):185–199

    Article  Google Scholar 

  30. Tuncer T, Ertam F, Dogan S, Subasi A (2020) An automated daily sports activities and gender recognition method based on novel multikernel local diamond pattern using sensor signals. IEEE Trans Instrum Meas 69(12):9441–9448

    Article  Google Scholar 

  31. Tariq A, Yan J, Gagnon AS, Riaz Khan M, Mumtaz F (2022) Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-spatial Inf Sci 1:1–19. https://doi.org/10.1080/10095020.2022.2100287

    Article  Google Scholar 

  32. Khan MT, Sha'ameri AZ, Zabidi MMiA, Chia CC (2022) FHSS Signals Classification by Linear Discriminant in a Multi-signal Environment. In: Proceedings of the International e-Conference on Intelligent Systems and Signal Processing, Springer, pp 143–155. https://doi.org/10.1007/978-981-16-2123-9_11

  33. Uddin S, Haque I, Lu H, Moni MA, Gide E (2022) Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep 12(1):1–11

    Article  Google Scholar 

  34. Sharma SK, Vijayakumar K, Kadam VJ, Williamson S (2022) Breast cancer prediction from microRNA profiling using random subspace ensemble of LDA classifiers via Bayesian optimization. Multimed Tools Appl 81(29):1–21. https://doi.org/10.1007/s11042-021-11653-x

    Article  Google Scholar 

  35. Gururaj N, Vinod V, Vijayakumar K (2022) Deep grading of mangoes using convolutional neural network and computer vision. Multimed Tools Appl 82:39525–39550. https://doi.org/10.1007/s11042-021-11616-2

    Article  Google Scholar 

  36. Liu H, Setiono R Chi (1995) 2: Feature selection and discretization of numeric attributes. In: Proceedings of 7th IEEE International conference on tools with artificial intelligence. IEEE, pp 388–391. https://doi.org/10.1109/TAI.1995.479783

  37. Kononenko I (1994) Estimating attributes: Analysis and extensions of RELIEF. European conference on machine learning. Springer, pp 171–182

    Google Scholar 

  38. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  39. Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR (2022) Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Comput Methods Programs Biomed 226:107161. https://doi.org/10.1016/j.cmpb.2022.107161

    Article  Google Scholar 

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Correspondence to Sengul Dogan.

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Institutional Review Board Statement

This research has been approved on ethical grounds by the Social and Human Sciences Research Ethics Committee, Firat University, Social and Human Sciences Research Centre Ethics Board on 23 November 2021 (23–5).

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Appendix 1

Appendix 1

The used stable reading text and translation of this text are given below.

Reading text in Turkish.

“Bir kartal denizden uzak bir dağ yolunun kenarında yuva kurdu. Kartalın orada yavruları oldu. Bir gün pençesinde kocaman bir balıkla yuvasına geldi kartal. Yuva yaptığı ağacın çevresinde çalışan insanlar vardı. Balığı gördüklerinde ağacın etrafında toplanıp bağırmaya, kartala taş atmaya başladılar. Balık sonunda kartalın pençesinden kayıp yere düştü. Adamlar balığı alıp gittiler. Kartal yuvasının bir köşesine çekilip tünedi. Yavruları ise havaya başlarını dikip yiyecek, yiyecek diye bağrışmaya başladılar. Oysa kartal çok yorulmuş denize kadar uçacak gücü kalmamıştı. Yuvasına iyice yerleşip yavrularını kanatlarının altına aldı. Onları sevdi, okşadı ve küçücük tüylerini düzeltti. Sanki “Ne olur birazcık sabredin!” diye yalvarıyordu onlara. Fakat yavrular okşandıkça seslerini daha da yükseltip bağrışmaya devam ettiler. Kartal uçtu ve daha yüksek bir dala kondu. Yavrular, anneleri uçup gidince daha da acıklı bir sesle bağrıştılar. Sonunda kartal çaresizlik içinde acı bir çığlık attı ve kanatlarını açıp ağır ağır denize doğru uçtu. Anne kartal akşam olup geç vakit yuvaya dönerken ağır ağır ve alçaktan uçmaktaydı. Yine pençelerinde kocaman bir balık vardı. Ağaca yaklaşırken çevrede başkaları var mı diye etrafı kolaçan etti bu kez. Güven içinde olduğunu hissettikten sonra kanatlarını kısıp yuvasının bir ucuna kondu. Yavru kartallar gagalarını açıp boyunlarını uzattılar. Anne kartal ise balığı parçaladı ve başladı yavrularını doyurmaya.”

The English translation of the above paragraph is given below.

"An eagle nested by the side of a mountain road far from the sea. The eagle had hatchlings there. One day, an eagle came to its nest with a huge fish in its talons. There were people working around the tree where he made his nest. When they saw the fish, they gathered around the tree and started shouting at the eagle and throwing stones. The fish eventually slipped from the eagle's talons and fell to the ground. The men took the fish and left. The eagle stepped back and perched on a corner of its nest. The hatchlings, on the other hand, raised their heads and started shouting for food, food. However, the eagle was so tired that it did not have the strength to fly into the sea. She settled in her nest and took her hatchlings under her wing. She loved them, touched them, and straightened their tiny feathers. "Please be patient!" she begged them. But as the hatchlings were attended, they kept raising their voices and shouting. The eagle flew and landed on a higher branch. The hatchlings cried out even more pathetically as their mother flew away. Finally, the eagle let out a cry of despair and spread its wings and flew slowly towards the sea. The mother eagle was flying slowly and low, as it was late in the evening, and was returning to her nest. It also had a huge fish in its paws. As she approached the tree, this time she looked around to see if anyone else was around. Feeling secure, she folded her wings and perched on one end of her nest. The baby eagles opened their beaks and stretched their necks. The mother eagle crushed the fish and began to feed her young."

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Abed, R.Q., Dikmen, M., Aydemir, E. et al. Automated reading level classification model based on improved orbital pattern. Multimed Tools Appl 83, 52819–52840 (2024). https://doi.org/10.1007/s11042-023-17535-8

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