A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

Year 2020, Volume: 5 Issue: 4, 353 - 372, 01.10.2020

Ilias Papadogıannıs Vassilis Poulopoulos Manolis Wallace

https://doi.org/10.24331/ijere.755047

Cited By: 10

Abstract

This article provides a thorough review of educational data mining (EDM) in the period 2015-2019. Going beyond earlier review works, in this article we examine previous research from a variety of aspects, including the examined data, the algorithms used, the type of conclusions drawn, the educational level/setting of application and the actual exploitation of the results in the educational setting. Our findings indicate that tertiary education dominates the EDM domain, while minimal focus has been given to secondary education and almost none to primary education. Our finding, and suggestion, is that by focusing EDM on earlier education level the field can have a more profound impact on education and on society as a whole

Keywords

Educational data mining, student performance, secondary education

References

• Abdulazeez, Y., & Abdulwahab, L. (2019). Application of classification models to predict students' academic performance using classifiers ensemble and synthetic minority over sampling techniques. Bayero Journal of Pure and Applied Sciences, 11(2), 142-148. doi: 10.4314/bajopas.v11i2.17 Al-Noshan, A. A., Al-Hagery, M. A., Al-Hodathi, H. A., & Al-Quraishi, M. S. (2018). Performance evaluation and comparison of classification algorithms for students at Qassim University. International Journal of Science and Research 8(11), ‎1277-1282.‎ doi: 10.21275/ART20202907 Adekitan, A. I., & Salau, O. (2019). The impact of engineering students' performance in the first three years on their graduation result using educational data mining. Heliyon, 5 (2019), e01250,. doi: 10.1016/j.heliyon.2019.e01250 Afeni, B. O., Oloyede, I. A., & Okurinboye, D. (2019). Students' performance prediction using classsification algorithms. Journal of Advances in Mathematics and Computer Science, 30, 1–9. doi: 10.9734/jamcs/2019/45438 Agaoglu, M. (2016). Predicting ınstructor performance using data mining techniques in higher education. IEEE Access, 4, 2379–2387. doi: 10.1109/ACCESS.2016.2568756 Agasisti, T., & Bowers, A. J. (2017). Data analytics and decision making in education: towards the educational data scientist as a key actor in schools and higher education institutions. In Johnes, G., Johnes, J., Agasisti, T., López-Torres, L. (Eds) Handbook of Contemporary Education Economics. (pp.184-210). Cheltenham, UK: Edward Elgar Publishing. Ahadi, A., Lister, R., Haapala, H., & Vihavainen, A. (2015). Exploring machine learning methods to automatically identify students in need of assistance. In Proceedings of the 2015 ACM Conference on International Computing Education Research. (pp. 121 – 130). doi: 10.1145/2787622.2787717 Ahmad, F., Ismail, N. H., & Aziz, A. A. (2015). The prediction of students' academic performance using classification data mining techniques. Applied Mathematical Sciences, 9(129), 6415–6426. doi: 10.12988/ams.2015.53289 Al-Barrak, M. A., & Al-Razgan, M. (2016). Predicting students final gpa using decision trees: A case study. International Journal of Information and Education Technology, 6(7), 528–533. doi: 10.7763/ijiet.2016.v6.745 Alharbi, Z., Cornford, J., Dolder, L., & De La Iglesia, B. (2016). Using data mining techniques to predict students at risk of poor performance. In Proceedings of 2016 SAI Computing Conference, SAI 2016, (pp. 523–531). doi: 10.1109/SAI.2016.7556030 Almasri, A., Celebi, E., & Alkhawaldeh, R. (2019). MISDataset: Management information systems dataset for predicting undergraduate students' performance. In Proceedings of the 4th International Conference on Computational Intelligence and Applications, ICCIA 2019, (pp. 54–57). Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/ICCIA.2019.00017 Almutairi, S., Shaiba, H., & Bezbradica, M. (2019). Predicting students' academic performance and main behavioral features using data mining techniques. In A. Alfaries, H. Mengash, A. Yasar, & E. Shakshuki (Eds), Advances in Data Science, Cyber Security and IT Applications. (pp. 245–259). Cham: Springer International Publishing. Al-Saleem, M., Al-Kathiry, N., Al-Osimi, S., & Badr, G. (2015). Mining educational data to predict students' academic performance. In: Perner P. (Eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2015. Lecture Notes in Computer Science, vol 9166. (pp. 403–414). Springer, Cham. Al-Shehri, H., Al-Qarni, A., Al-Saati, L., Batoaq, A., Badukhen, H., Alrashed, S., ... & Olatunji, S. O. (2017). Student performance prediction using Support Vector Machine and K-Nearest Neighbor. 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).(pp.1-4). IEEE. doi: 10.1109/ccece.2017.7946847 Altaf, S., Soomro, W., & Rawi, M. I. M. (2019). Student performance prediction using multi-layers artificial neural networks .ACM Press. doi: 10.1145/3325917.3325919 Altujjar, Y., Altamimi, W., Al-Turaiki, I., & Al-Razgan, M (2016). Predicting critical courses affecting students performance: A case study. Procedia Computer Science, 82, 65–71. doi: 10.1016/j.procs.2016.04.010 Aman, F., Rauf, A., Ali, R., Iqbal, F., & Khattak, A. M (2019). A predictive model for predicting students academic performance. In Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA), (pp. 1-4). doi: 10.1109/IISA.2019.8900760 Amani, M., & Salama, S. (2019). Discovering performance evaluation features of faculty members using data mining techniques to support decision making. International Journal of Computer Applications, 178(49), 25–29. doi: 10.5120/ijca2019919417 Amaya, Y., Heredia, D & Barrientos, E. (2015). Student dropout predictive model using data mining techniques. Ieee Latin America Transactions, 13(9), 3127–3134. Amirhajlou, L., Sohrabi, Z., Alebouyeh, M. R., Tavakoli, N., Haghighi, R. Z., Hashemi, A., & Asoodeh, A. (2019). Application of data mining techniques for predicting residents' performance on pre-board examinations: A case study. Journal of Education and Health Promotion, 8, (108). Retrieved 11 February 2020 from: https://www.ncbi. nlm.nih.gov/pmc/articles/PMC6615122/# Amornsinlaphachai, P. (2016). Efficiency of data mining models to predict academic performance and a cooperative learning model. In Proceedings of the 8th International Conference on Knowledge and Smart Technology, KST 2016, (pp. 66–71). doi: 10.1109/KST.2016.7440483 Amrieh, E. A., Hamtini, T., & Aljarah, I. (2015). Preprocessing and analyzing educational data set using X-API for improving student's performance. In Proceedings of the 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies, AEECT 2015. (pp. 1-5). doi: 10.1109/AEECT.2015.7360581 Angiani, G., Ferrari, A., Fornacciari, P., Mordonini, M., & Tomaiuolo, M. (2019). Real marks analysis for predicting students' performance. In: Di Mascio T. et al. (Eds) Methodologies and Intelligent Systems for Technology Enhanced Learning, 8th International Conference. MIS4TEL 2018, (pp. 37– 44). ChamSpringer International Publishing. doi: 10.1007/978-3-319-98872-6_5 Anuradha, C., & Velmurugan, T. (2016). Performance evaluation of feature selection algorithms in educational data mining. International Journal of Data Mining Techniques and Applications, 5(2), 131–139. doi: 10.20894/IJDMTA.102.005.002.007 Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students performance using educational data mining. Computers & Education, 113, 177–194. doi: 10.1016/j.compedu.2017.05.007 Athani, S. S., Kodli, S. A., Banavasi, M. N., & Hiremath, P. S. (2017). Student academic performance and social behavior predictor using data mining techniques. In Proceedings of the International Conference on Computing, Communication and Automation 2017 (ICCCA). (pp. 170-174). IEEE. doi: 10.1109/ccaa.2017.8229794 Baker, R. (2013). Learning, schooling, and data analytics. In: Marilyn M., Redding S., J.Twyman (Eds), Handbook on innovations in learning, (pp. 179–190). Center on Innovations in Learning, Temple University, Philadelphia. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In: J. A. Larusson, & B. White (Eds), Learning Analytics: From Research to Practice (pp. 61–75). New, York, NY: Springer New York. doi: 10.1007/978-1-4614-3305-7_4 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. Bergin, S., Mooney, A., Ghent, J., & Quille, K. (2015). Using machine learning techniques to predict ıntroductory programming performance. International Journal of Computer Science and Software Engineering, 4(12), 323–328. Bhegade, K. B., & Shinde, S. V. (2016). Student performance prediction system with educational data mining. International Journal of Computer Applications, 146(5), 32-35. doi: 10.5120/ijca2016910704 Bowers, A. J. (2017). Quantitative research methods training in education leadership and administration preparation programs as disciplined inquiry for building school improvement capacity. Journal of Research on Leadership Education, 12(1), 72–96. doi: /10.7916/D8K93H16 Brinton, C. G., & Chiang, M. (2015). MOOC performance prediction via clickstream data and social learning networks. In Proceedings of the IEEE Conference on Computer Communications INFOCOM, 26, (pp. 2299–2307). doi: 10.1109/INFOCOM.2015.7218617 Buenaño-Fernández, D., Gil, D., & Luján-Mora, S. (2019). Application of machine learning in predicting performance for computer engineering students: A case study. Sustainability, 11, 2833. doi: 10.3390/su11102833 Buniyamin, N., bin Mat, U., & Arshad, P. M. (2016). Educational data mining for prediction and classification of engineering students achievement. In Proceedings of the 7th International Conference on Engineering Education, ICEED 2015, (pp. 49–53). doi: 10.1109/ICEED.2015.7451491 Burgos, C., Campanario, M. L., de la Peña, D., Lara, J. A., Lizcano, D., & Martínez, M. A. (2018). Data mining for modeling students' performance: A tutoring action plan to prevent academic dropout. Computers & Electrical Engineering, 66, 541–556. doi: 10.1016/J.COMPELECENG.2017.03.005 Bydžovská, H. (2015). Are collaborative filtering methods suitable for student performance prediction? In Proceedings of the Portuguese Conference on Artificial Intelligence. (pp. 425–430). doi: 10.1007/978-3-319-23485-4_42 Bydžovská, H. (2016). A comparative analysis of techniques for predicting student performance. In Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, 2, (pp. 306–311). Canagareddy, D., Subarayadu, K., & Hurbungs, V. (2019). A machine learning model to predict the performance of university students. Smart and Sustainable Engineering for Next Generation Applications (pp. 313–322). Springer International Publishing. doi: 10.1007/978-3-030-18240-3_29 Castro-Wunsch, K., Ahadi, A., & Petersen, A. (2017). Evaluating neural networks as a method for identifying students in need of assistance. In Proceedings of the Conference on Integrating Technology into Computer Science Education, ITiCSE, (pp. 111–116). doi: 10.1145/3017680.3017792 Chango, W., Cerezo, R., & Romero, C. (2019). Predicting academic performance of university students from multi-sources data in blended learning. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems - DATA '19, (pp. 1–5). New York, New York, USA: ACM Press. doi: 10.1145/3368691.3368694 Chanlekha, H., & Niramitranon, J. (2018). Student performance prediction model for early-identification of at-risk students in traditional classroom settings. In Proceedings of the 10th International Conference on Management of Digital EcoSystems - MEDES '18, (pp. 239–245). doi: 10.1145/3281375.3281403 Comendador, B. E. V., Rabago, L. W., & Tanguilig, B. T. (2016). An educational model based on Knowledge Discovery in Databases (KDD) to predict learner behavior using classification techniques. In Proceedings of the IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). (pp. 1-6). IEEE. doi: 10.1109/icspcc.2016.7753623 Corrigan, O., Smeaton, A. F., Glynn, M., & Smyth, S. (2015). Using educational analytics to ımprove test performance. In: Conole G., Klobučar T., Rensing C., Konert J., Lavoué E. (Eds) Design for Teaching and Learning in a Networked World. Lecture Notes in Computer Science, vol 9307. (pp 42–55). Springer, Cham. Costa, E. B., Fonseca, B., Santana, M. A., de Araújo, F. F., & Rego, J. (2017). Evaluating the effectiveness of educational data mining techniques for early prediction of students academic failure in introductory programming courses. Computers in Human Behavior, 73, 247–256. doi: 10.1016/j.chb.2017.01.047 Daud, A., Aljohani, N. R., Abbasi, R. A., Lytras, M. D., Abbas, F., & Alowibdi, J. S. (2017). Predicting student performance using advanced learning analytics. In Proceedings of the 26th International Conference on World Wide Web Companion. (pp. 415-421).ACM Press. doi: 10.1145/3041021.3054164 De Sousa, A., Oliveira, C., & Borges, J. (2018). Using academic performance to predict college students dropout: A case study. Educação e Pesquisa, 44, 1–17. doi: 10.1590/s1678-4634201844180590 Del Río, C., & Pineda Insuasti, J. (2016). Predicting academic performance in traditional environments at higher-education institutions using data mining: A review. Ecos de la Academia(4). 185-201. Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Van Erven, G. (2019). Educational data mining: Predictive analysis of academic performance of public school students in the capital of Brazil. Journal of Business Research, 94, 335–343. doi: 10.1016/j.jbusres.2018.02.012 Francis, B. K., & Babu, S. S. (2019). Predicting academic performance of students using a hybrid data mining approach. Journal of Medical Systems, 43(6), 162. doi: 10.1007/s10916-019-1295-4 Gampa, R., & Baynes, A. (2018). Visual analytic workflow to understand students’ performance in computer science courses. In Proceedings of the 2018 IEEE Frontiers in Education Conference (FIE), (pp. 1–5). doi: 10.1109/FIE.2018.8658790 García-Vélez, R. A., López-Nores, M., González-Fernández, G., Robles-Bykbaev, V. E., Wallace, M., Pazos-Arias, J. J., & Gil-Solla, (2019). On Data protection regulations, big data and sledgehammers in higher education. Applied Sciences, 9(15), 3084. doi: 10.3390/app9153084 González-Marcos, A., Olarte-Valentın, R., Ordieres-Meré, J., & Alba-Elıas, F. (2019). Predicting students' performance in a virtual experience for project management learning. In Proceedings of the 11th International Conference on Computer Supported Education, (pp. 665–673). doi: 10.5220/0007843506650673 Govindasamy, K., & Velmurugan, T. (2017). A study on classification and clustering data mining algorithms based on students academic performance prediction. International Journal Of Control Theory And Applications, 10(23), 147–160. Guo, B., Zhang, R., Xu, G., Shi, C., & Yang, L. (2015), Predicting students performance in educational data mining. In 2015 International Symposium on Educational Technology (ISET), (pp. 125-128). IEEE. doi: 10.1109/ISET.2015.33 Hamsa, H., Indiradevi, S., & Kizhakkethottam, J. (2016). Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technology, 25, 326–332. doi: 10.1016/j.protcy.2016.08.114 Hasheminejad, S. M., & Sarvmili, M. (2018). S3PSO: Students' performance prediction based on particle swarm optimization. Journal of AI and Data Mining 7(1), 77-96. doi: 10.22044/jadm.2018.5506.1662 Hassan, H., Anuar, S., & Ahmad, N. B. (2019). Students' performance prediction model using meta-classifier approach. In Proceedings of the International Conference on Engineering Applications of Neural Networks. (pp. 221-231). Springer, Cham. doi: 10.1007/978-3-030-20257-6_19 He, J., Bailey, J., Rubinstein, B. I., & Zhang, R. (2015, February). Identifying at-risk students in massive open online courses. In Twenty-Ninth AAAI Conference on Artificial Intelligence. Retrieved 5 Febrary 2020 from: https://www.aaai.org/ocs/index.php/AAAI/AAAI15/ paper/view/9696/9460 Huang, A. Y., Lu, O. H., Huang, J. C., Yin, C. J., & Yang, S. J. (2019). Predicting students’ academic performance by using educational big data and learning analytics: evaluation of classification methods and learning logs. Interactive Learning Environments, 28(2), 206-230. doi: 10.1080/10494820.2019.1636086 Hussain, M., Hussain, S., Zhang, W., Zhu, W., Theodorou, P., & Abidi, S. M. R.. (2018). Mining moodle data to detect the ınactive and low-performance students during the moodle course. Presented at the 2nd International Conference on Big Data Research - ICBDR 2018 (pp. 133-140). ACM Press. doi: 10.1145/3291801.3291828 Hussain, S., Dahan, N. A., Ba-Alwib, F. M., & Ribata, N. (2018). Educational data mining and analysis of students' academic performance using WEKA. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 447–459. doi: 10.11591/ ijeecs.v9.i2.pp447-459 Hussain, S., Muhsion, Z. F., Salal, Y. K., Theodoru, P., KurtoÄŸlu, F., & Hazarika, G. C. (2019). Prediction model on student performance based on ınternal assessment using deep learning. International Journal of Emerging Technologies in Learning (iJET), 14(8), 4-22. doi: 10.3991/ijet.v14i08.10001 Iam-On, N., & Boongoen, T. (2015). Improved student dropout prediction in Thai University using ensemble of mixed-type data clusterings. International Journal of Machine Learning and Cybernetics, 8, 497–510. doi: 10.1007/s13042-015-0341-x Inyang, U. G., Umoh, U. A., Nnaemeka, I. C., & Robinson, S. A. (2019). Unsupervised characterization and visualization of students' academic performance features. Computer and Information Science, 12(2), 103-116. doi: 10.5539/cis.v12n2p103 Hridoy, S. A. A., Ekram, M. T., Islam, M. S., Ahmed, F., & Rahman, R. M. (2015). Improving accuracy of students' final grade prediction model using optimal equal width binning and synthetic minority over-sampling technique. Decision Analytics, 2(1), 1–25. doi: 10.1186/s40165-014-0010-2 Kamal, P., & Ahuja, S. (2019). An ensemble-based model for prediction of academic performance of students in undergrad professional course. Journal of Engineering, Design and Technology, 17(4), 769–781. doi: 10.1108/JEDT-11-2018-0204 Kasthuriarachchi, K. T., & Liyanage, S. R. (2019). Predicting students' academic performance using utility based educational data mining. In: Hung J., Yen N., Hui L. (Eds) Frontier Computing. FC 2018. Lecture Notes in Electrical Engineering, vol 542. (pp. 29–39) Springer, Singapore. doi: 10.1007/978-981-13-3648-5_4 Kaur, G., & Singh, W. (2016). Prediction of student performance using weka tool. International Journal of Engineering Sciences, 17, 8–16. Retrieved 10 February 2020 from: http://ijoes.vidyapublications.com/paper/Vol17/02-Vol17.pdf Ketui, N., Wisomka, W., & Homjun, K. (2019). Using classification data mining techniques for students performance prediction. Presented at the Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON) 2019. IEEE. (pp. 359-363). doi: 10.1109/ecti-ncon.2019.8692227 Kitchenham, B., Brereton, O. P., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2010). Systematic literature reviews in software engineering–a tertiary study. Information and software technology, 52(6), 792–805. doi: 10.1016/j.infsof.2010.03.006 Kiu, C. (2018). Data mining analysis on student's academic performance through exploration of student's background and social activities. Presented at the 4th International Conference on Advances in Computing, Communication and Automation, ICACCA 2018, (pp. 1–5). doi: 10.1109/ICACCAF.2018.8776809 Klusener, M., & Fortenbacher, A. (2015). Predicting students' success based on forum activities in MOOCs. Presented at the IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2015, 2, (pp. 925–928). doi: 10.1109/IDAACS.2015.7341439 Knowles, J. (2015). Building an accurate statewide dropout early warning system in Wisconsin. Journal of Educational Data Mining, 7(3), 18–67. Koprinska, I., Stretton, J., & Yacef, K. (2015, June). Predicting student performance from multiple data sources. In International Conference on Artificial Intelligence in Education (pp. 678-681). Springer, Cham. Koprinska, I., Stretton, J., & Yacef, K. (2015). Students at risk: Detection and remediation. Presented at the 8th International Conference on Educational Data Mining. (pp. 512 – 515). Kostopoulos, G., Kotsiantis, S., & Pintelas, P. (2015). Predicting student performance in distance higher education using semi-supervised techniques. In: Bellatreche L., Manolopoulos Y. (Eds) Model and Data Engineering. Lecture Notes in Computer Science, vol 9344. (pp. 259–270). Springer, Cham doi: 10.1007/978-3-319-23781-7_21 Liang, J., Li, C., & Zheng, L. (2016). Machine learning application in MOOCs: Dropout prediction. Presented at the 11th International Conference on Computer Science & Education (ICCSE) 2016, IEEE. (pp. 52–57). doi: 10.1109/ICCSE.2016.7581554 Livieris, I., Mikropoulos, T., & Pintelas, P. (2016). A decision support system for predicting students’ performance. Themes in Science and Technology Education, 9(1), 43–57. Livieris, I. E., Drakopoulou, K., Mikropoulos, T. A., Tampakas, V., & Pintelas, P. (2018). An ensemble-based semi-supervised approach for predicting students' performance. In: Mikropoulos T. (Eds) Research on e-Learning and ICT in Education. (pp. 25-42). Springer Cham. doi: 10.1007/978-3-319-95059-4 Livieris, I. E., Drakopoulou, K., Tampakas, V. T., Mikropoulos, T. A., & Pintelas, P (2019). Predicting secondary school students' performance utilizing a semi-supervised learning approach. Journal of Educational Computing Research, 57(2), 448–470. doi: 10.1177/0735633117752614 Livieris, I. E., Kotsilieris, T., Tampakas, V., & Pintelas, P. (2019). Improving the evaluation process of students' performance utilizing a decision support software. Neural Computing and Applications, 31, 1683–1694. doi: 10.1007/s00521-018-3756-y Livieris, I. E., Tampakas, V., Kiriakidou, N., Mikropoulos, T., & Pintelas, P. (2019). Forecasting students' performance using an ensemble ssl algorithm. Communications in Computer and Information Science (pp. 566–581). Springer. doi: 10.1007/978-3-030-20954-4_43 Guarín, C. E. L., Guzmán, E. L., & González, F. A.. (2015). A model to predict low academic performance at a specific enrollment using data mining. Revista Iberoamericana de Tecnologias del Aprendizaje, 10(3), 119–125. doi: 10.1109/RITA.2015.2452632 Le Mai, T., Do, P. T., Chung, M. T., & Thoai, N. (2019). An apache spark-based platform for predicting the performance of undergraduate students. In 2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE (pp. 191-199).. doi: 10.1109/hpcc/smartcity /dss.2019.00041 Lu, O. H., Huang, A. Y., Huang, J. C., Lin, A. J., Ogata, H., & Yang, S. J. (2018). Applying learning analytics for the early prediction of students' academic performance in blended learning. Educational Technology and Society, 21(2), 220–232. Luo, J., Sorour, S. E., Goda, K., & Mine, T. (2015). Predicting student grade based on free-style comments using word2vec and ann by considering prediction results obtained in consecutive lessons. International Educational Data Mining Society. (pp. 396–399). Mahboob, T., Irfan, S., & Karamat, A.. (2017). A machine learning approach for student assessment in E-learning using Quinlan's C4.5, Naive Bayes and Random Forest algorithms. Presented at the 19th International Multi-Topic Conference, INMIC 2016. (pp. 1 -8). doi: 10.1109/INMIC.2016.7840094 Martins, M. P., Migueis, V. L., & Fonseca, D. S. B. (2018, June). Educational data mining: A literature review. In 2018 13th Iberian Conference on Information Systems and Technologies (CISTI), IEEE. (pp. 1-6). doi: 10.23919/CISTI.2018.8399281 Meedech, P., Iam-On, N., & Boongoen, T (2016). Prediction of student dropout using personal profile and data mining approach. New York. In: Lavangnananda K., Phon-Amnuaisuk S., Engchuan W., Chan J. (Eds) Intelligent and Evolutionary Systems. Proceedings in Adaptation, Learning and Optimization, vol 5. Springer, Cham (pp. 143-155). doi: 10.1007/978-3-319-27000-5_12 Mehboob, B., Liaqat, R., Abbas, N. (2017). Student performance prediction and risk analysis by using data mining approach. Journal of Intelligent Computing, 14(7), 69–76. Merchan Rubiano, S. M., & Duarte Garcia, J. A. (2016). Analysis of data mining techniques for constructing a predictive model for academic performance. IEEE Latin America Transactions, 14 (6), 2783–2788. doi: 10.1109/TLA.2016.7555255 Mhetre, V., & Nagar, M. (2017). Classification based data mining algorithms to predict slow, average and fast learners in educational system using WEKA. Presented at the International Conference on Computing Methodologies and Communication (ICCMC). IEEE. ( pp. 475-479). doi: 10.1109/iccmc.2017.8282735 Miguéis, V. L., Freitas, A., Garcia, P. J., & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51. doi: 10.1016/j.dss.2018.09.001 Moscoso-Zea, O., Saa, P., & Luján-Mora, S. (2019). Evaluation of algorithms to predict graduation rate in higher education institutions by applying educational data mining. Australasian Journal of Engineering Education, 24(1), 4–13. doi: 10.1080/22054952.2019.1601063 Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and predicting students' academic performance using data mining techniques. International Journal of Modern Education and Computer Science, 8(11), (pp. 36–42). doi: 10.5815/ijmecs.2016.11.05 Mundada, O. (2016). Mining educational data from students’ management system. International Journal of Advanced Research in Computer Science, 7(3), 244–249. Namomsa, G., & Sharma, D. P. (2018). Comparative analytics and predictive modeling of student performance through data mining techniques. SSRN Electronic Journal. doi: 10.2139/ssrn.3395126 Naser, S. S., Zaqout, I. S., Abu Ghosh, M., Atallah, R. R., & Alajrami, E. (2015). Predicting student performance using artificial neural network: in the faculty of engineering and ınformation technology. International Journal of Hybrid Information Technology, 8(2), 221–228. doi: 10.14257/ijhit.2015.8.2.20 Navamani, J. M., & Kannammal, A. (2015). Predicting performance of schools by applying data mining techniques on public examination results. Research Journal of Applied Sciences, Engineering and Technology, 9(4), 262–271. doi: 10.19026/rjaset.9.1403 Nemomsa, G., & Mulugeta, A. (2018). Comparative analytics and predictive modeling of student performance through data mining techniques. SSRN. doi: 10.2139/ssrn.3382511 Nuñez, H., Maldonado, G., & Astudillo, C. A. (2018). Semi-supervised regression based on tree SOMs for predicting students performance. Presented at the 9th International Conference on Pattern Recognition Systems (ICPRS 2018). Institution of Engineering and Technology. (pp. 12-19). doi: 10.1049/cp.2018.1288 Nust, C. E. (2017). Student performance prediction and risk analysis by using data mining approach. Journal of Intelligent Computing Volume, 8(2), 49-57. Okubo, F., Yamashita, T., Shimada, A., & Ogata, H. (2017). A neural network approach for students' performance prediction. Presented at the the Seventh International Learning Analytics & Knowledge Conference on - LAK ’17, (pp. 598–599). doi: 10.1145/3027385.3029479 Pai, M., McCulloch, M., Gorman, J. D., Pai, N., Enanoria, W., Kennedy, G., ... & Colford, J. J. (2004). Systematic reviews and meta-analyses: an illustrated, step-by-step guide. The National medical journal of India, 17 (2), 86–95. Pandey, M., & Taruna, S. (2016). Towards the integration of multiple classifier pertaining to the Student's performance prediction. Perspectives in Science, 8, 364–366. doi: 10.1016/j.pisc.2016.04.076 Papamitsiou, Z., & Economides, A. (2014). Learning analytics and educational data mining in practice: A systematic literature review of empirical evidence. Educational Technology & Society, 17(4), 49-64. Parmar, K., Vaghela, D., & Sharma, P. (2015). Performance prediction of students using distributed data mining. Presented at the: International Conference on Innovations in Information, Embedded and Communication Systems 2015 (ICIIECS), (pp. 1–5). doi: 10.1109/ICIIECS.2015.7192860 Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 4(1), 1432–1462. doi: 10.1016/j.eswa.2013.08.042 Polyzou, A., & Karypis, G. (2018). Feature extraction for classifying students based on their academic performance. IEEE Transactions on Learning Technologies, 12(2), 237-248. doi: 10.1109/TLT.2019.2913358 Pradeep, A., Das, S., & Kizhekkethottam, J. J. (2015). Students dropout factor prediction using EDM techniques. Presented at the IEEE International Conference on Soft-Computing and Network Security, ICSNS 2015. (pp. 1-7). doi: 10.1109/ICSNS.2015.7292372 Pristyanto, Y., Pratama, I., & Nugraha, A. F. (2018). Data level approach for imbalanced class handling on educational data mining multiclass classification. Presented at the International Conference on Information and Communications Technology, ICOIACT 2018, (pp. 310–314). doi: 10.1109/ICOIACT.2018.8350792 Rivas, A., Fraile, J. M., Chamoso, P., González-Briones, A., Rodríguez, S., & Corchado, J. M. (2019). Students performance analysis based on machine learning techniques. In: Uden L., Liberona D., Sanchez G., Rodríguez-González S. (Eds) Learning Technology for Education Challenges. LTEC 2019. Communications in Computer and Information Science. (pp. 428–438). Springer International Publishing. doi: 10.1007/978-3-030-20798-4_37 Rodrigues, R. L., Ramos, J. L. C., Silva, J. C. S., Dourado, R. A., & Gomes, A. S. (2019). Forecasting students\textquotesingle performance through self-regulated learning behavioral analysis. International Journal of Distance Education Technologies, 17, 52–74. doi: 10.4018/ijdet.2019070104. Rodriguez, L. G., Rodriguez, C. A., Niguidula, J. D., & Calibo, D. I. (2019). Analysis of students performance: Input to program enhancement of students in computing. International Journal of Information and Education Technology, 9(6), 429–432. doi: 10.18178/ijiet.2019.9.6.1240 Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33 (1), 135–146. doi: 10.1016/j.eswa.2006.04.005 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. doi: 10.1109/TSMCC.2010.2053532 Ruby, J., & David, K. (2015). Analysis of ınfluencing factors in predicting students performance using MLP -A comparative study. International Journal of Innovative Research in Computer and Communication Engineering (An ISO Certified Organization), 3(2), 1085–1092. doi: 10.15680/ijircce.2015.0302070 S. Abu-Oda, G., & M. El-Halees, A. (2015). Data mining in higher education : University Student dropout case study. International Journal of Data Mining & Knowledge Management Process, 5(1), 15–27. doi: 10.5121/ijdkp.2015.5102 Saa, A. A., Al-Emran, M., & Shaalan, K. (2019). Mining student information system records to predict students’ academic performance. Presented at the International conference on advanced machine learning technologies and applications. (pp. 229–239). Springer Cham. doi: 10.1007/978-3-030-14118-9_23 Kalyani, B. S., Harisha, D., RamyaKrishna, V., & Manne, S.. (2019). Evaluation of students performance using neural networks. In: Pandian, A. P., Ntalianis, K., & Palanisamy, R. (Eds). Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing. (pp. 499 - 505). Springer. doi: 10.1007/978-3-030-30465-2_55 Santoso, L. W., & Yulia. (2019). The analysis of student performance using data mining. In: Bhatia S., Tiwari S., Mishra K., Trivedi M. (Eds) Advances in Computer Communication and Computational Sciences (pp. 559-573). Springer. doi: 10.1007/978-981-13-6861-5_48 Sara, N. B., Halland, R., Igel, C., & Alstrup, S. (2015). High-school dropout prediction using machine learning High-School Dropout Prediction Using Machine Learning : A Danish Large-scale Study. Presented at the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, (pp. 319–324). Bruges. Saravanan, M., & Jyothi, V. L. (2019). A novel approach for performance and estımatıon of students by usıng hybrıd model. IIOAB Journal, 10(2), 9–12. Satyanarayana, A., & Nuckowski, M. (2016). Data mining using ensemble classifiers for ımproved prediction of student academic performance data mining using ensemble classifiers for ımproved prediction of student academic performance. City University of New York (CUNY), New York. Retrieved 10 February 2020 from: https://academicworks.cuny. edu/cgi/viewcontent.cgi?article =1078&context=ny_pubs Shrivas, A. K., & Tiwari, P. (2017). Comparative analysis of models for student performance with data mining tools. International Journal of Computer Trends and Technology, 46(1), 42–46. doi: 10.14445/22312803/ijctt-v46p109 Singh, J., & Kaur, E. H. (2018). Implementation of classification based algorithms to prediction of students performance using educational system. International Journal of Engineering Science and Computing, 8, 19233–19238. Sivakumar, S., & Selvaraj, R. (2018). Predictive modeling of students performance through the enhanced decision tree. Advances in Electronics, Communication and Computing, 21-36. doi: 10.1007/978-981-10-4765-7_3 Sivakumar, S., Venkataraman, S., & Selvaraj, R. (2016). Predictive modeling of student dropout ındicators in educational data mining using ımproved decision tree. Indian Journal of Science and Technology,9(4), 1-5. doi: 10.17485/ijst/2016/v9i4/87032 Son, L. H., & Fujita, H. (2019). Neural-fuzzy with representative sets for prediction of student performance. Applied Intelligence, 49(1), 172–187. doi: 10.1007/s10489-018-1262-7 Sorour, S. E., Goda, K., & Mine, T. (2015). Estimation of Student performance by considering consecutive lessons. Presented at the 4th International Congress on Advanced Applied Informatics, Okayama, 2015, (pp. 121-126), doi: 10.1109/IIAI-AAI.2015.170 Strecht, P., Cruz, L., Soares, C., & Mendes-Moreira, J. (2015). A comparative study of classification and regression algorithms for modelling students' academic performance. In Proceedings of the 8th International Conference on Educational Data Mining, 392–395. Retrieved 9 February 2020 from: http://www.educationaldatamining.org /EDM2015/proceedings/short392-395.pdf. Sukhija, K., Jindal, M., & Aggarwal, N. (2015). The recent state of educational data mining: A survey and future visions. Presented at the 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE), (pp. 354–359). doi: 10.1109/MITE.2015.7375344 Sumitha, R., Vinothkumar, E. S., & Scholar, P. G. (2016). Prediction of students outcome using data mining techniques. International Journal of Scientific Engineering and Applied Science (IJSEAS), 2(6), 132–139. Tang, J. K., Xie, H., & Wong, T. L. (2015). A big data framework for early ıdentification of dropout students In: J. Lam, K. K. Ng, S. K. Cheung, T. L. Wong, K. C. Li, & F. L. Wang (Eds) , Communications in Computer and Information Science, (pp. 127–132). Springer Berlin Heidelberg. doi: 10.1007/978-3-662-48978-9_12 Tasnim, N., Paul, M. K., & Sattar, A. S. (2019). Performance analysis of different decision tree based methods for ıdentifying drop out students. Presented at the 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), (1–6). IEEE. doi: 10.1109/ICASERT.2019.8934518 Tegegne, A. K., & Alemu, T. A. (2018). Educational data mining for students' academic performance analysis in selected Ethiopian universities. Information Impact: Journal of Information and Knowledge Management, 9(2), 1-15. doi: 10.4314/iijikm.v9i2.1 Thakar, P., Anil Mehta, A. (2015). Performance analysis and prediction in educational data mining: A research travelogue. International Journal of Computer Applications, 110(15). 60-68. Timaran-Pereira, R., Hidalgo, A., Caicedo, J., & Benavides, J. (2018). Discovering factors associated with academic performance of high school students. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education. (pp. 188-197). Association for the Advancement of Computing in Education (AACE). Ulloa-Cazarez, R. L., López-Martín, C., Abran, A., & Yáñez-Márquez, C. (2018). Prediction of online students performance by means of genetic programming. Applied Artificial Intelligence, 32(9), 858–881. doi: 10.1080/08839514.2018.1508839 Widyaningsih, Y., Fitriani, N., & Sarwinda, D. (2019). A semi-supervised learning approach for predicting student ' s performance : First-year. Presented at the 12th International Conference on Information & Communication Technology and System (ICTS), (pp. 291–295) Yehuala, M. A. (2015). Application of data mining techniques for student success and failure prediction the case of Debre Markos University. International Journal of Scientific & Technology Research,4 (4), 91–94. Zhang, X., Sun, G., Pan, Y., Sun, H., He, Y., & Tan, J. (2018). Students performance modeling based on behavior pattern. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1659–1670. doi: 10.1007/s12652-018-0864-6 Zhou, Q., Zheng, Y., & Mou,. (2015). Predicting students' performance of an offline course from their online behaviors. In 2015 Fifth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), (pp. 70–73). doi: 10.1109/DICTAP.2015.7113173