Abstract
Our research work addresses the problem of educational and vocational orientation. Thus, the aims of this study is to set up a prediction model of the acceptance of the E-orientation system “Orientation-chabab.com” by users. We will determinate the factors related to the leads educational and professional orientation of the students. We established a qualitative questionnaire based in TAM theoretical model, sharing with social networks, SMS sending, individual interviews in collaborations with the experts in the field of orientation. After we collected the feedback from our study sample, we use the information gain attribute to reduce our data and we apply applied and compare several predictive Machine Learning algorithms to select the best one with the highest accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Guerss, F., et al.: Towards a new ontology of the Moroccan Post-baccalaureate learner profile for the E-orientation system “MMSyOrientation” (2015)
Orientation chabab. http://orientationchabab.com/. Accessed 04 Feb 2018
Ma, C., Zhang, H.H., Wang, X.: Machine learning for big data analytics in plants. Trends Plant Sci. 19(12), 798–808 (2014)
Davis, F.D.: A technology acceptance model for empirically testing new end-user information systems: theory and results. Dissertation Massachusetts Institute of Technology (1985)
Hall, M., et al.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10–18 (2009)
Chinnaswamy, A., Srinivasan, R.: Performance analysis of classifiers on filter-based feature selection approaches on microarray data. In: Bio-Inspired Computing for Information Retrieval Applications, pp. 41–70. IGI Global (2017)
Novaković, J.: Toward optimal feature selection using ranking methods and classification algorithms. Yugoslav J. Oper. Res. 21(1), 119–135 (2016)
Aggarwal, C.C. (ed.): Data Classification: Algorithms and Applications. CRC Press, Boca Raton (2014)
Korting, T.S.: C4. 5 algorithm and multivariate decision trees. Image Processing Division, National Institute for Space Research–INPE Sao Jose dos Campos–SP, Brazil (2006)
Landwehr, N., Hall, M., Frank, E.: Logistic model trees. Mach. Learn. 59(1–2), 161–205 (2005)
Zhang, H.: The optimality of Naive Bayes. AA 1(2), 3 (2004)
Cao, L.J., et al.: Parallel sequential minimal optimization for the training of support vector machines. IEEE Trans. Neural Netw. 17(4), 1039–1049 (2006)
Rakotomalala, R.: Pratique de la régression logistique. Régression Logistique Binaire et Polytomique, Université Lumière Lyon 2, 258p (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ihya, R., Namir, A., Elfilali, S., Guerss, F.Z., Ait Daoud, M. (2020). Modeling the Acceptance of the E-Orientation Systems by Using the Predictions Algorithms. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-36778-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36777-0
Online ISBN: 978-3-030-36778-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)