Machine learning in Education

Dear Colleagues,

Machine learning and data mining have transformed the way students, educators, and institutions interact through intelligent systems to monitor and optimize student’s success. The ever-increasing amount of student data available in e-learning management systems allows one to track information about student activities, participation, and interactions in multiple courses. This allows one to resolve arising challenges in education, such as automatic course recommendations, predicting a student’s academic performance, detecting a student’s withdrawal from courses early, detecting a student’s dropout from the academic programs, and course activities optimization, among others. The flood of real-time and historical data help to identify patterns among students and facilitate educators regarding early intervention if a student is found in need of educational support. The ultimate goal of machine learning in education is to foster student success and increase graduation and retention rates. Nevertheless, these intelligent systems need to be fully supported by the educational institution, which is responsible for providing educators with the tools and resources necessary to intervene. The revolution of new online courses, and particularly massive open online courses (MOOCs), calls for new machine learning approaches that can resolve the new challenges of education in the 2020s. This Special Issue presents some of the most recent innovative research works in machine learning and data mining for education.

References

• Baker, R.S.J.D., 2010. Data mining for education. International encyclopedia of education, 7(3), pp.112-118.
• Siemens, G. and d Baker, R.S., 2012. Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
• Romero, C. and Ventura, S., 2010. Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), pp.601-618.
• Siemens, G. and d Baker, R.S., 2012. Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
• Ferguson, R., 2012. Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6), pp.304-317.
• Romero, C. and Ventura, S., 2013. Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), pp.12-27.
• Baker, R.S. and Inventado, P.S., 2014. Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer, New York, NY.
• Ciolacu, M., Tehrani, A.F., Beer, R. and Popp, H., 2017. Education 4.0—Fostering student's performance with machine learning methods. In 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME) (pp. 438-443). IEEE.
• Romero, C. and Ventura, S., 2017. Educational data science in massive open online courses. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(1), p.e1187.
• Dutt, A., Ismail, M.A. and Herawan, T., 2017. A systematic review on educational data mining. IEEE Access, 5, pp.15991-16005.

Dr. Alberto Cano
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Education Sciences is an international peer-reviewed open access monthly journal published by MDPI.

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• Machine learning
• Educational data science
• Educational data mining
• Learning analytics