The Applications of Machine Learning in Education Science Research
Main Article Content
Abstract
The article presents an overview of the application of machine learning techniques in education science research. The research process shows the use of technology in learning and teaching, collecting information, analyzing and processing data to provide high-accuracy answers or advice in solving educational issues is the trend and strength in education science research. Through this, the authors make recommendations on some research directions in the field of education approaching international publications.
Keywords:
Machine learning, data science, education science, international publication.
References
[1] Mitchell, Tom, Machine Learning, ISBN 0-07-042807-7, OCLC 36417892, New York: McGraw Hill, 1997.
[2] S. B. Kotsiantis, Use of Machine Learning Techniques for Educational Proposes: A Decision Support System for Forecasting Students’ Grades, Artificial Intelligence Review, Vol. 37, No. 4, 2012, pp. 331-344, https://doi.org/10.1007/s10462-011-9234-x.
[3] G. Erik, Introduction to Supervised Learning, Data Mining and Knowledge Discovery Handbook, 2014, pp. 149-164, https://doi.org/10.1007/0-387-25465-X8.
[4] P. M. Arsad, N. Buniyamin, J. A. Manan, A Neural Network Students’ Performance Prediction Model (NNSPPM), IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 2013, pp. 1-5.
[5] G. Gray, C. McGuinness, P. Owende, An Application of Classification Models to Predict Learner Progression in Tertiary Education, IEEE International Advance Computing Conference (IACC), 2014, pp. 549-554,
https://doi.org/10.1109/IAdCC.2014.6779384.
[6] M. Kumar, A. J. Singh, D. Handa, Literature Survey on Educational Dropout Prediction, J. Education and Management Engineering, Vol. 2, 2017, pp. 8-19, https://doi.org/10.5815/ijeme.2017.02.0.
[7] S. Natek, M. Zwilling, Expert Systems with Applications Student Data Mining Solution Knowledge Management System Related to Higher Education Institutions, Expert Systems with Applications, Vol. 41, 2014, pp. 6400-6407, https://doi.org/10.1016/j.eswa.2014.04.02.
[8] A. M. Shahiri, W. Husain, N. A. Rashid, A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, Vol. 72, 2015, pp. 414-422, https://doi.org/10.1016/j. procs.2015.12.157.
[9] R. Sathya, A. Abraham, Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification, International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 2, 2013, pp. 34-38,
https://doi.org/10.14569/IJARAI.2013.020206.
[10] D. Yang, M. Piergallini, I. Howley, C. Rose, Forum Thread Recommendation for Massive Open Online Courses, Proceedings of the 7th International Conference on Educational Data Mining (EDM), 2014, pp. 257-260.
[11] A. Elbadrawy, A. Polyzou, Z. Ren, M. Sweeney, G. Karypis, H. Rangwala, Okay Predicting Student Performance Using Personalized Analytics. Computer, Vol. 49, No. 4, pp. 61-69, https://doi.org/10.1109/MC.2016.119. 12.
[12] M. Fei, D. Y. Yeung, Temporal Models for Predicting Student Dropout in Massive Open Online Courses, IEEE International Conference on Data Mining Workshop (ICDMW), Vol. 2, No. 15, 2015, pp. 256-263, https://doi.org/10.1109/ICDMW.2015.174.
[13] I. O. Natthakan, T. Boongoen, Generating Descriptive Model for Student Dropout: A Review of Clustering Approach, Human-centric Computing and Information Sciences, Vol. 7, No. 1, 2017, pp. 1-24, https://doi.org/10.1186/ s13673-016-0083-0.
[14] L. Deng, D. Yu, Deep Learning: Methods and Applications. Foundations and Trends® in Signal Processing, Vol. 7, No. 3-4, 2014, pp. 197-387.
[15] V. Ramachandra, K. Way, Deep Learning for Causal Inference, 2018.
[16] M. Fei, D. Y. Yeung, Temporal Models for Predicting Student Dropout in Massive Open Online Courses, 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015, pp. 256-263, https://doi.org/10.1109/ICDMW.2015.174.
[17] A. Rakesh, M. Christoforaki, S. Gollapudi, A. Kannan, K. Kenthapad, A. Swaminathan, Mining Videos from the Web for Electronic Textbooks, Microsoff Research, Yudelson, MV, Koedinger, KR and Gordon, GJ, Individualized Bayesian Knowledge Tracing Models, 2014.
[18] H. P. Beck, W. D. Davidson, Establishing an Early Warning System: Predicting Low Grades in College Students from Survey of Academic Orientations Research in Higher Education, 2016.
[19] S. Nunn, J. T. Avella, T. Kanai, M. Kebritchi, Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review, Online Learning, Vol. 20, No. 2, 2016, pp. 13-29, https://doi.org/10.24059/olj.v20i2.790.
[20] N. T. Nghe, T. Q. Dinh, University Admissions Support System, Science Magazine Can Tho University, 2015, pp 152-159,
https://sj.ctu.edu.vn/ql/docgia/tacgia711/baibao31511.html (inVietanmese).
[21] N. T. Uyen, N. M. Tam, Predicting Student Learning Outcomes by Data Mining Techniques, Scientific Journal - Vinh University, No. 48, 2019, pp. 68-73 (in Vietnamese).
[22] L. H. Sang, T. T. Dien, N. T. Nghe, N. T. Hai, Predicting Learning Outcomes by Deep Learning Techniques with Multilayer Neural Networks, Can Tho University Journal of Science, Vol. 56, No. 3, 2020, pp. 20-28,
https://doi.org/10.22144/ctu.jvn.2020.049 (in Vietnames).
[2] S. B. Kotsiantis, Use of Machine Learning Techniques for Educational Proposes: A Decision Support System for Forecasting Students’ Grades, Artificial Intelligence Review, Vol. 37, No. 4, 2012, pp. 331-344, https://doi.org/10.1007/s10462-011-9234-x.
[3] G. Erik, Introduction to Supervised Learning, Data Mining and Knowledge Discovery Handbook, 2014, pp. 149-164, https://doi.org/10.1007/0-387-25465-X8.
[4] P. M. Arsad, N. Buniyamin, J. A. Manan, A Neural Network Students’ Performance Prediction Model (NNSPPM), IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA), 2013, pp. 1-5.
[5] G. Gray, C. McGuinness, P. Owende, An Application of Classification Models to Predict Learner Progression in Tertiary Education, IEEE International Advance Computing Conference (IACC), 2014, pp. 549-554,
https://doi.org/10.1109/IAdCC.2014.6779384.
[6] M. Kumar, A. J. Singh, D. Handa, Literature Survey on Educational Dropout Prediction, J. Education and Management Engineering, Vol. 2, 2017, pp. 8-19, https://doi.org/10.5815/ijeme.2017.02.0.
[7] S. Natek, M. Zwilling, Expert Systems with Applications Student Data Mining Solution Knowledge Management System Related to Higher Education Institutions, Expert Systems with Applications, Vol. 41, 2014, pp. 6400-6407, https://doi.org/10.1016/j.eswa.2014.04.02.
[8] A. M. Shahiri, W. Husain, N. A. Rashid, A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, Vol. 72, 2015, pp. 414-422, https://doi.org/10.1016/j. procs.2015.12.157.
[9] R. Sathya, A. Abraham, Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification, International Journal of Advanced Research in Artificial Intelligence, Vol. 2, No. 2, 2013, pp. 34-38,
https://doi.org/10.14569/IJARAI.2013.020206.
[10] D. Yang, M. Piergallini, I. Howley, C. Rose, Forum Thread Recommendation for Massive Open Online Courses, Proceedings of the 7th International Conference on Educational Data Mining (EDM), 2014, pp. 257-260.
[11] A. Elbadrawy, A. Polyzou, Z. Ren, M. Sweeney, G. Karypis, H. Rangwala, Okay Predicting Student Performance Using Personalized Analytics. Computer, Vol. 49, No. 4, pp. 61-69, https://doi.org/10.1109/MC.2016.119. 12.
[12] M. Fei, D. Y. Yeung, Temporal Models for Predicting Student Dropout in Massive Open Online Courses, IEEE International Conference on Data Mining Workshop (ICDMW), Vol. 2, No. 15, 2015, pp. 256-263, https://doi.org/10.1109/ICDMW.2015.174.
[13] I. O. Natthakan, T. Boongoen, Generating Descriptive Model for Student Dropout: A Review of Clustering Approach, Human-centric Computing and Information Sciences, Vol. 7, No. 1, 2017, pp. 1-24, https://doi.org/10.1186/ s13673-016-0083-0.
[14] L. Deng, D. Yu, Deep Learning: Methods and Applications. Foundations and Trends® in Signal Processing, Vol. 7, No. 3-4, 2014, pp. 197-387.
[15] V. Ramachandra, K. Way, Deep Learning for Causal Inference, 2018.
[16] M. Fei, D. Y. Yeung, Temporal Models for Predicting Student Dropout in Massive Open Online Courses, 2015 IEEE International Conference on Data Mining Workshop (ICDMW), 2015, pp. 256-263, https://doi.org/10.1109/ICDMW.2015.174.
[17] A. Rakesh, M. Christoforaki, S. Gollapudi, A. Kannan, K. Kenthapad, A. Swaminathan, Mining Videos from the Web for Electronic Textbooks, Microsoff Research, Yudelson, MV, Koedinger, KR and Gordon, GJ, Individualized Bayesian Knowledge Tracing Models, 2014.
[18] H. P. Beck, W. D. Davidson, Establishing an Early Warning System: Predicting Low Grades in College Students from Survey of Academic Orientations Research in Higher Education, 2016.
[19] S. Nunn, J. T. Avella, T. Kanai, M. Kebritchi, Learning Analytics Methods, Benefits, and Challenges in Higher Education: A Systematic Literature Review, Online Learning, Vol. 20, No. 2, 2016, pp. 13-29, https://doi.org/10.24059/olj.v20i2.790.
[20] N. T. Nghe, T. Q. Dinh, University Admissions Support System, Science Magazine Can Tho University, 2015, pp 152-159,
https://sj.ctu.edu.vn/ql/docgia/tacgia711/baibao31511.html (inVietanmese).
[21] N. T. Uyen, N. M. Tam, Predicting Student Learning Outcomes by Data Mining Techniques, Scientific Journal - Vinh University, No. 48, 2019, pp. 68-73 (in Vietnamese).
[22] L. H. Sang, T. T. Dien, N. T. Nghe, N. T. Hai, Predicting Learning Outcomes by Deep Learning Techniques with Multilayer Neural Networks, Can Tho University Journal of Science, Vol. 56, No. 3, 2020, pp. 20-28,
https://doi.org/10.22144/ctu.jvn.2020.049 (in Vietnames).
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