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Contextual Ontology-Based Feature Selection for Teachers

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Learning Technologies and Systems (ICWL 2022, SETE 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13869))

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Abstract

The context of teacher is indescribable without considering the multiple overlapping contextual situations. Teacher Context Ontology (TCO) presents a unified representation of data of these contexts. This ontology provides a relatively high number of features to consider for each context. These features result in a computational overhead during data processing in context-aware recommender systems. Therefore, the most relevant features must be favored over others without losing any potential ones using a feature selection approach. The existing approaches provide struggling results with high number of contextual features. In this paper, a new contextual ontology-based feature selection approach is introduced. This approach finds similar contexts for each insertion of new teacher using the ontology representation. Also, it selects relevant features from multiple contexts of a teacher according to their corresponding importance using a variance-based selection approach. This approach is novel in terms of representation, selection, and deriving implicit relationships for features in the multiple contexts of a teacher.

This work was funded by the French Research Agency (ANR) and by the company Vivocaz under the project France Relance - preservation of R &D employment (ANR-21-PRRD-0072-01) in collaboration with project Imhotep “Preventing teachers’ psychosocial risks through contextual support of educational resources”.

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Notes

  1. 1.

    https://protege.stanford.edu/.

  2. 2.

    http://d2rq.org/.

References

  1. Alelyani, S., Tang, J., Liu, H.: Feature selection for clustering: a review. In: Data Clustering: Algorithms and Applications, vol. 29, no. 1 (2013)

    Google Scholar 

  2. Alyahyan, E., Düştegör, D.: Predicting academic success in higher education: literature review and best practices. Int. J. Educ. Technol. High. Educ. 17(1), 1–21 (2020). https://doi.org/10.1186/s41239-020-0177-7

    Article  Google Scholar 

  3. Cabaleiro-Cerviño, G., Vera, C.: The impact of educational technologies in higher education. GIST Educ. Learn. Res. J. 20, 155–169 (2020)

    Article  Google Scholar 

  4. Chen, H., Finin, T., Joshi, A.: An ontology for context-aware pervasive computing environments. knowl. Eng. Rev. 18(3), 197–207 (2003)

    Article  Google Scholar 

  5. Dong, G., Liu, H.: Feature Engineering for Machine Learning and Data Analytics. CRC Press, Boca Raton (2018)

    Google Scholar 

  6. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(Mar), 1157–1182 (2003)

    MATH  Google Scholar 

  7. Haruna, K., et al.: Context-aware recommender system: a review of recent developmental process and future research direction. Appl. Sci. 7(12), 1211 (2017)

    Article  Google Scholar 

  8. Kamalov, F.: Orthogonal variance decomposition based feature selection. Expert Syst. Appl. 182, 115191 (2021)

    Article  Google Scholar 

  9. Kamalov, F., Moussa, S., El Khatib, Z., Mnaouer, A.B.: Orthogonal variance-based feature selection for intrusion detection systems. In: 2021 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1–5. IEEE (2021)

    Google Scholar 

  10. Kraskov, A., Stögbauer, H., Grassberger, P.: Estimating mutual information. Phys. Rev. E 69(6), 066138 (2004)

    Article  MathSciNet  Google Scholar 

  11. Kuhn, M., Johnson, K.: Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press, Boca Raton (2019)

    Book  Google Scholar 

  12. Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-6849-3

    Book  MATH  Google Scholar 

  13. Lakshmi Padmaja, D., Vishnuvardhan, B.: Variance-based feature selection for enhanced classification performance. In: Satapathy, S.C., Bhateja, V., Somanah, R., Yang, X.-S., Senkerik, R. (eds.) Information Systems Design and Intelligent Applications. AISC, vol. 862, pp. 543–550. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-3329-3_51

    Chapter  Google Scholar 

  14. Lindig, C.: Fast concept analysis. Work. Concept. Struct.-Contribut. ICCS 2000, 152–161 (2000)

    Google Scholar 

  15. Mabkhot, M.M., Al-Samhan, A.M., Hidri, L.: An ontology-enabled case-based reasoning decision support system for manufacturing process selection. Adv. Mater. Sci. Eng. 2019 (2019)

    Google Scholar 

  16. Nashed, N.N., Lahoud, C., Abel, M.H.: TCO: a teacher context ontology. In: 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 757–762. IEEE (2021)

    Google Scholar 

  17. Nashed, N.N., Lahoud, C., Abel, M.H., Andrès, F., Blancan, B.: Mood detection ontology integration with teacher context. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1710–1715. IEEE (2021)

    Google Scholar 

  18. Roberts, A.G., Catchpoole, D.R., Kennedy, P.J.: Variance-based feature selection for classification of cancer subtypes using gene expression data. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2018)

    Google Scholar 

  19. Ross, B.C.: Mutual information between discrete and continuous data sets. PLoS ONE 9(2), e87357 (2014)

    Article  Google Scholar 

  20. Sadeghyan, S.: A new robust feature selection method using variance-based sensitivity analysis. arXiv preprint: arXiv:1804.05092 (2018)

  21. Seger, C.: An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing (2018)

    Google Scholar 

  22. Solorio-Fernández, S., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: A review of unsupervised feature selection methods. Artif. Intell. Rev. 53(2), 907–948 (2020)

    Article  Google Scholar 

  23. Veisi, H., Aflaki, N., Parsafard, P.: Variance-based features for keyword extraction in Persian and English text documents. Scientia Iranica 27(3), 1301–1315 (2020)

    Google Scholar 

  24. Zaffar, M., Hashmani, M.A., Savita, K., Khan, S.A.: A review on feature selection methods for improving the performance of classification in educational data mining. Int. J. Inf. Technol. Manag. 20(1–2), 110–131 (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. HEUDIASYC, Université de Technologie de Compiègne, Compiègne, France

    Nader N. Nashed & Marie-Hélène Abel

  2. Université Française d’Égypte, Cairo, Egypt

    Nader N. Nashed & Christine Lahoud

Authors
  1. Nader N. Nashed
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  2. Christine Lahoud
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  3. Marie-Hélène Abel
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Corresponding author

Correspondence to Nader N. Nashed .

Editor information

Editors and Affiliations

  1. Universidad de La Laguna, Tenerife, Spain

    Carina S. González-González

  2. Universidad Complutense de Madrid, Madrid, Madrid, Spain

    Baltasar Fernández-Manjón

  3. Durham University, Durham, UK

    Frederick Li

  4. Universidad de Salamanca, Salamanca, Spain

    Francisco José García-Peñalvo

  5. Mercatorum University, Rome, Italy

    Filippo Sciarrone

  6. Université de Caen Normandie, Caen, France

    Marc Spaniol

  7. University of Salamanca, Salamanca, Spain

    Alicia García-Holgado

  8. Universidad de La Laguna, Tenerife, Spain

    Manuel Area-Moreira

  9. University of Hagen, Hagen, Germany

    Matthias Hemmje

  10. South China Normal University, Guangzhou, China

    Tianyong Hao

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Nashed, N.N., Lahoud, C., Abel, MH. (2023). Contextual Ontology-Based Feature Selection for Teachers. In: González-González, C.S., et al. Learning Technologies and Systems. ICWL SETE 2022 2022. Lecture Notes in Computer Science, vol 13869. Springer, Cham. https://doi.org/10.1007/978-3-031-33023-0_10

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  • DOI: https://doi.org/10.1007/978-3-031-33023-0_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-33022-3

  • Online ISBN: 978-3-031-33023-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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