Brief Review of Educational Applications Using Data Mining and Machine Learning
DOI:
https://doi.org/10.24320/redie.2017.19.4.1305Palavras-chave:
Education, Data mining, Machine learningResumo
The large amounts of data used nowadays have motivated research and development in different disciplines in order to extract useful information with a view to analyzing it to solve difficult problems. Data mining and machine learning are two computing disciplines that enable analysis of huge data sets in an automated manner. In this paper, we give an overview of several applications using these disciplines in education, particularly those that use some of the most successful methods in the machine learning community, such as artificial neural networks, decision trees, Bayesian learning and instance-based methods. Although these two areas of artificial intelligence have been applied in many real-world problems in different fields, such as astronomy, medicine, and robotics, their application in education is relatively new. The search was performed mainly on databases such as EBSCO, Elsevier, Google Scholar, IEEEXplore and ACM. We hope to provide a useful resource for the education community by presenting this review of approaches.Downloads
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Referências
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Russell, P., & Norving, S. (2010). Artificial Intelligence: a modern approach (3rd. ed.). Prentice Hall.
Schiaffino, S., García, P., & Amandi, A. (2008). E-teacher: providing personalized assistance to e-learning students. Computers & Education, 51, 1744-1754.
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Witten, I., Frank, E., & Hall M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd. ed.). New Zealand: Morgan Kaufmann.
Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and eLearning, 17(1), 118-133.
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Anupama, S., & Vijayalakshmi, M.N. (2011). Efficiency of decision trees in predicting student’s academic performance. Computer Science and Information Technology, 2, 335-343.
Arnaut, A., & Giourguli, S. (2010). Los grandes problemas de México: VII educación [The main issues in Mexico: VII Education]. Mexico City: El Colegio de México.
Ayinde, A. Q., Adetunji, A. B., Bello, M., & Odeniyi, O. A. (2013). Performance evaluation of naive bayes and decision algorithms in mining students’ Educational Data. International Journal of Computer Science Issues, 10(4), 147-151.
Baylari, A., & Montazer, G. A. (2008). Design a personalized e-learning system based on item response theory and artificial neural network approach. Expert Systems with Applications, 39, 8013-8021.
Bishop, C. M. (2007). Pattern recognition and Machine Learning. Singapore: Springer.
Cheng, S.-C., Huang, Y.-M., Chen, J.-N., & Lin, Y.-T. (2005). Automatic Leveling System for E-Learning Examination Pool Using Entropy-Based Decision Tree. Advances in Web-Based Learning-ICWL 2005: 4th International Conference, Hong Kong, China, July 31st - August 3rd.
De Ibarrola, M. (2012). Los grandes problemas del sistema educativo mexicano [The main issues in the Mexican education system]. Perfiles educativos, XXXIV (especial), 16-28.
Demsar, J., Curk, T., Erjavec, A., Gorup, C., Hocevar, T., Milutinovic, M. et al. (2013). Orange: data mining toolbox in python. Journal of Machine Learning Research 14, 2349-2353.
García, P., Amandi, A., Schiaffino, S., & Campo, M. (2005). Using bayesian networks to detect students’ learning styles in a web-based education system. 7o. Simposio Argentino de Inteligencia Artificial-ASAI2005, 115-126. Rosario, Argentina: Universidad del Centro de la Provincia de Buenos Aires.
García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks' precision for detecting students' learning styles. Computers & Education, 49, 794-808.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explorations, 11(1).
Han, J., Kamber, M., & Pei, J. (2011). Data mining: concepts and techniques. Amsterdam: Morgan Kaufmann.
Hofer, H., & KNIME. (2016). Oper for innovation. Retrieved from http://www.knime.org
jWork.ORG, & Chekanov, S. (2016). Data melt: computation and visualization enviroment. Retrieved from http://jwork.org/dmelt
Kakavand, S., Mokfi, T., & Jafar, M. (2014). Prediction the loyal student using decision tree algorithms. International Journal of Information and Communication Technology Research, 4(1), 32-37.
Karamouzis, S. T., & Vrettos, A. (2008). An artificial neural network for predicting student graduation outcomes. Proceedings of the World Congress on Engineering and Computer Science. WCECS 2008, 1-4. San Francisco.: International Association of Engineers.
Karypis, G. (2015). Karypis Lab. Retrieved from http://glaros.dtc.umn.edu/gkhome/cluto/cluto/overview
Kotsiantis, S., Pierrakeas, C., & Pintelas, P. (2004). Predicting Students' performance in distance learning using machine learnings teachniques. Applied Artificial Intelligence, 18, 411-426.
Kriesel, D. (2013). A brief introduction to neural networks. Retrieved from http://www.dkriesel.com/en/blog/2009/1011_a_brief_introduction_to_neural_networks_published_in _epsilon_version
Kumar, B., & Pal, S. (2011). Mining educational data to analyze students' performance. International Journal of Advanced Computer Science and Applications, 2(6), 63-60.
Márquez, J. M., Ortega, J. A., Gonzalez-Abril, L., & Velasco, F. (2008). Creating adaptive learning paths using ant colony optimization and bayesian networks. Neural Networks, 2008. IEEE World Congress on Computational Intelligence, 3834-3839. Hong Kong, China.
Minaei-Bidgoli, B., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003). Predicting student performance: an application of data mining methods with the educactional we-based system LON-CAPA. 33rd ASEE/IEEE Frontiers in Education Conference, 1-6. Boulder, CO.
Mitchell, T. M. (1997). Machine learning. Singapore: McGrawHill.
OCDE. (2010). Mejorar las escuelas: estrategias para la acción en México. Resumen ejecutivo [Improving schools: Strategies for action in Mexico. Executive summary]. Autor.
Oladokun, V. O., Adebanjo, A. T., & Charles-Owaba, O. E. (2008). Predicting students’ academic performance using artificial neural network: a case study of an engineering course. The Pacific Journal of Science and Technology, 9(1), 72-79.
Ranjan, J., & Khalil, S. (2008). Conceptual framework of data mining process in management education in India: an institutional perspective. Information Technology Journal, 7(1), 16-23.
RapidMiner. (2016). RapidMiner. Retrieved from http://rapidminer.com
Russell, P., & Norving, S. (2010). Artificial Intelligence: a modern approach (3rd. ed.). Prentice Hall.
Schiaffino, S., García, P., & Amandi, A. (2008). E-teacher: providing personalized assistance to e-learning students. Computers & Education, 51, 1744-1754.
The Apache Software Foundation. (2014). Mahout. Retrieved from http://mahout.apache.org
Thomas, E. H., & Galambos, N. (2004). What satisfies students? Mining student-opinion data with regression and decision tree analysis. Research in Higher Education, 45(3), 251-269.
Toware. (2014). Data mining resources. Retrieved from http://datamining.togaware.com
Vialardi, C., Bravo, J., Shafti, L., & Ortigosa, Á. (2009). Recommendation in Higher Education using data mining techniques. Second International Conference on Educational Data Mining (pp. 190-199). Cordoba, Spain: International Working Group on Educational Data Mining.
Witten, I., Frank, E., & Hall M. A. (2011). Data mining: Practical machine learning tools and techniques (3rd. ed.). New Zealand: Morgan Kaufmann.
Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and eLearning, 17(1), 118-133.
Zorrilla, M., & Barba, B. (2008). Reforma Educativa en México: Descentralización y nuevos actores. Sinéctica, 30, 1-32. Retrieved from https://sinectica.iteso.mx/index.php/SINECTICA/article/view/189
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2017-10-25
