Abstract
The educational recommendation system to provide support for academic guidance and adaptive learning has always been an important issue of research for smart education. A bad guidance can give rise to difficulties in further studies and can be extended to school dropout. This paper explores the potential of Educational Data Mining for academic guidance recommendation by predicting students’ performance which involves analyzing data of students’ records, socio-economic data and of course the student’s motivation. The proposed model was analyzed and tested using student’s data collected from the preparatory classes for “Grandes Ecoles” Reda Slaoui (CPGE) - Morocco. More specifically, it proposes the use of three models that were applied on real data: Decision tree, Naive Bayes, and Neural networks. The data include the classes period (2012–2014 and 2013–2015) of 330 students in specialty the grade Mathematical Physics (MP) and Engineering Sciences (MPSI). The performance results indicate that our framework can make more accurate predictions of students’ performance.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: a review and future visions. JEDM| Journal of Educational Data Mining, 1(1), 3–17.
Bakhshinategh, B., Zaiane, O. R., ElAtia, S., & Ipperciel, D. (2018). Educational data mining applications and tasks: a survey of the last 10 years. Education and Information Technologies, 23(1), 537–553.
Baradwaj, B. K. & Pal, S. (2012). Mining educational data to analyze students' performance. arXiv preprint arXiv:1201.3417.
Belghiti, K., EL Kirat El Allame, Y., & Chana, M. (2017). Critical thinking development: the case of the english course in the CPGE Classes in Meknes, Fes and Kenitra.
Belskaya, E., Moldovanova, E., Rozhkova, S., Tsvetkova, O., & Chervach, M. (2016). University smart guidance counselling. In Smart Education and e-Learning 2016 (pp. 39–49): Springer.
Bhardwaj, B. K. & Pal, S. (2012). Data mining: a prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418.
Brijain, M., Patel, R., Kushik, M., & Rana, K. (2014). A survey on decision tree algorithm for classification.
Cakir, M., Xhafa, F., Zhou, N. & Stahl, G. (2005). Thread-based analysis of patterns of collaborative interaction in chat. AIED.
Calvo-Flores, M. D., Galindo, E. G., Jiménez, M. P., & Pineiro, O. P. (2006). Predicting students’ marks from Moodle logs using neural network models. Current Developments in Technology-Assisted Education, 1(2), 586–590.
Castellano, E. J., Martinez, L., & Sánchez, P. J. (2008). OrieB, a linguistic CRS for supporting decision making in academic orientation. World Scientific Proceedings Series on Computer Engineering and Information Science Computational Intelligence in Decision and Control, Chapter 2. 841–846. https://doi.org/10.1142/9789812799470_0138. Consulted 15/08/2018.
Castro, F., Vellido, A., Nebot, À., & Mugica, F. (2007). Applying data mining techniques to e-learning problems. Evolution of Teaching and Learning Paradigms in Intelligent Environment, 183–221.
CNIPE -MENFPES (Centre National de l'Innovation Pédagogique et de l'Expérimentation Ministère de l'Education Nationale, de la Formation Professionnelle, de l'Enseignement Supérieur et de la Recherche Scientifique) (2018). Computational simulation of CPGE's selection formula. http://www.cpge.ac.ma/Cand/Simulation.aspx. Accessed 23 October 2018.
Es-Saady, Y. (2012). Contribution au développement d'approches de reconnaissance automatique de caractères imprimés et manuscrits, de textes et de documents Amazighes. Thèse de Doctorat,Université Ibnou Zohr, Faculté des Sciences, Agadir (Maroc). Consulted 15/08/2018.
Goyal, M., & Vohra, R. (2012). Applications of data mining in higher education. International Journal of Computer Science, 9(2), 113.
Guichard, J., Huteau, M., & Huteau, M. (2005). L'orientation scolaire et professionnelle. Paris: Dunod.
Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design (Vol. 20). Boston: Pws Pub.
Hien, N. T. N. & Haddawy, P. (2007). A decision support system for evaluating international student applications. Frontiers In Education Conference-Global Engineering: Knowledge Without Borders, Opportunities Without Passports, 2007. FIE'07. 37th Annual, IEEE.
Hung, J.-L., & Zhang, K. (2008). Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching.
Ibrahim, Z. & Rusli, D. (2007). Predicting students’ academic performance: Comparing artificial neural network, decision tree and linear regression. 21st Annual SAS Malaysia Forum, SAS Kuala Lumpur.
Kohavi, R. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai (Vol. 14, pp. 1137–1145, Vol. 2): Montreal, Canada.
Oladokun, V., Adebanjo, A., & Charles-Owaba, O. (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.
Oskouei, R. J., & Askari, M. (2014). Predicting academic performance with applying data mining techniques (generalizing the results of two different case studies). Computer Engineering and Applications Journal, 79e88.
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.
Romero, C., & 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), 601–618.
Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.
Sacin, C. V., Agapito, J. B., Shafti, L., & Ortigosa (2009). A. Recommendation in higher education using data mining techniques. In Educational Data Mining 2009.
Sanjeev, A. P., & Zytkow, J. M. (1995). Discovering enrollment knowledge in university databases. In Proceeding KDD'95 Proceedings of the First International Conference on Knowledge Discovery and Data Mining, 246–251.
Sara, N.-B., Halland, R., Igel, C. & Alstrup, S. (2015). High-school dropout prediction using machine learning: a danish large-scale study. ESANN 2015 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.
Tair, M. M. A., & El-Halees, A. M. (2012). Mining educational data to improve students' performance: a case study. International. Journal of Information, 2(2), 140–146.
Tan, P.-N. (2006). Introduction to data mining. London: Pearson Education India.
Vialardi, C., Bravo, J., Shafti, L., & Ortigosa, A. (2009) Recommendation in higher education using data mining techniques. In Proceedings of Second Educational Data Mining Conference, Córdoba, Spain, pp. 190–199.
Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J. (2016). Data mining: practical machine learning tools and techniques. Morgan Kaufmann.
Wu, X., & Kumar, V. (2009). The top ten algorithm in data mining. International Standard Book, 13, 978–971.
Yann, L. (1987). Modèles connexionnistes de l'apprentissage. Thèse de Doctorat, Université Pierre et Marie Curie (Paris). http://www.sudoc.abes.fr/DB=2.1/SRCH?IKT=12&TRM=043586643 Consulted 15/08/2018.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mimis, M., El Hajji, M., Es-saady, Y. et al. A framework for smart academic guidance using educational data mining. Educ Inf Technol 24, 1379–1393 (2019). https://doi.org/10.1007/s10639-018-9838-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10639-018-9838-8