The Role of Educational Data Mining in the Quality of Educational Process

نوع المستند : المقالة الأصلية

المؤلفون

1 The Research Division of School and its Environment, National Institute for Research in Education, Algeria

2 The Research Division of Educational Governance, National Institute for Research in Education, Algeria

المستخلص

Artificial Intelligence has changed many aspects of lives since it provides the technological services and tools that help to make decisions in everyday life. The present paper aims to highlight the role of education data mining in improving the quality of the educational process. Education data mining techniques can discover information that can assist educators in establishing pedagogical decisions when designing or modifying an environment or teaching approach. Also, education data mining can help predict students' failure or dropout. So present paper going to define educational data mining, why educational data mining is important, the goals of using educationanl data mining, and where educational data mining can be applied. Later, the paper develops some recent examples of studies that have had utilized educational data mining to enhance the educational process and predictive models that help educators in making  educational decisions and building suitable prevention programs to improve the Quality of educational process.

الكلمات الرئيسية

الموضوعات الرئيسية

المراجع

  1. References:                                         

    Khanna, L., Narayan Singh , S., & Alam, M. (2016). Educational data mining and its role in determining factors affecting students academic performance: A systematic review. 1st India International Conference on Information Processing (IICIP), (pp. 1 - 7). Delhi, India. doi:doi: 10.1109/IICIP.2016.797

    Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated. WIREs Data Mining Knowl Discov. e1355. doi:https://doi.org/10.1002/widm.1355

    Aleem, A., & Gore, M. (2020). Educational Data Mining Methods: A Survey. IEEE 9th International Conference on Communication Systems and Network Technologies (CSNT)Gwalior,, (pp. 182-188). India. doi:doi: 10.1109/CSNT48778.2020.9115734.

    Fernández, D. B., & Luján-Mora, S. (2017). Comparison of applications for educational data mining in Engineering Education," 2017 IEEE World Engineering Education Conference (EDUNINE). IEEE World Engineering Education Conference (EDUNINE), (pp. 81-85). Santos, Brazil. doi:doi: 10.1109/EDUNINE.2017.7918187.

    Jones, K. M., Rubel, A., & LeClere, E. (2020). A matter of trust: Higher education institutions as information fiduciaries in an age of educational data mining and learning analytics. Journal of the Association for Information Science and Technolo, 7(10), 1227-1241. doi:https://doi.org/10.1002/asi.24327

    Yağcı, M. (2022). Educational data mining: Prediction of students' academic performance using machine learning algorithms. Smart Learning Environments, 9(1), 1-9. https://doi.org/10.1186/s40561-022-00192-z

    Renato Carauta Ribeiro & Edna Dias Canedo,Using Data Mining Techniques to Perform School Dropout Prediction: A Case Study, spring Nature Switzerland AG 2020, 17th International Conference on Information Technology–New Generations (ITNG 2020), Advances in Intelligent Systems and Computing v 1134, https://doi.org/10.1007/978-3-030-43020-7_28, p-p:1637–1667.

    Annalina Sarra, Lara Fontanella, Simone Di Zio, Identifying Students at Risk of Academic Failure Within the Educational Data Mining Framework, https://doi.org/10.1007/s11205-018-1901-8, Springer Science+Business Media B.V, part of Springer Nature 2018, published online 19 april 2018.

    1. Romero, S. Ventura, Educational data mining: A survey from 1995 to 2005, Volume 33, Issue 1, July 2007, Pages 135-146, 2006 Elsevier Ltd. All rights reserved, doi:10.1016/j.eswa.2006.04.005

     Kalyani M Raval (B.Com, MSc IT), Data Mining Techniques, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 10, October 2012, Research Paper Available online at: www.ijarcsse.com

    Kabathova, J.; Drlik, M. (2021). Towards Predicting Student’s Dropout in University Courses Using Different Machine Learning Techniques. Appl. Sci. 2021, 11, 3130. https://doi.org/10.3390/app11073130

    Janne Parri,(2006), Quality in Higher Education,Vadyba/ Management, Index Copernicus International,Vol.11,Iss:02,Poland ,p 107.

    James J.Jiang and all, (2002), Measuring Information System Service Quality: SERVQUAL from the Other Side, Journal of MIS Quarterly, Vol.26,No.2, p85.

    Ribeiro, R.C., Canedo, E.D. (2020). 17th International Conference on Information Technology–New Generations (ITNG 2020). Advances in Intelligent Systems and Computing, vol 1134. Springer, Cham. https://doi.org/10.1007/978-3-030-43020-7_28

ملفات

شارك

إرسال الاستشهاد إلى

الإحصائيات