Data Mining Studies in Education: Literature Review For The Years 2014-2020

Yıl 2022, , 342 - 376, 31.03.2022

Zehra Bilici , Durmuş Özdemir

https://doi.org/10.35675/befdergi.849973

Cited By: 5

Öz

Data mining is one of the important and beneficial technological developments in education and its usage area is becoming widespread day by day as it includes applications that contribute positively to teaching activities. It is possible to make teaching activities more effective and efficient by transforming the raw data in the field of education into meaningful using data mining techniques. Studies carried out in the field of education between 2014-2020 with data mining methods were scanned from the "Science Direct" database. It was determined that 60 articles from the scanning studies were directly related to data mining in education. The studies include issues such as the development of e-learning systems, pedagogical support, clustering of educational data, and student performance predictions. These selected articles were analyzed in terms of purpose, application area, method, and contribution to the literature. The aim of the study is to group the work carried out in the field of education under specific headings using the data mining process, to evaluate its methods and objectives, and to direct the individuals who will work in this field.

Anahtar Kelimeler

Data mining in education, Data mining Technologies, Technologies for teaching, Trends in education

Eğitimde Veri Madenciliği Çalışmaları: 2014-2020 Yılları Literatür Taraması

Öz

Veri madenciliği eğitimde önemli ve faydalı teknolojik gelişmelerden biridir ve öğretim faaliyetlerine olumlu yönde katkı sağlayan uygulamaları içerdiği için kullanım alanı gün geçtikçe yaygınlaşmaktadır. Veri madenciliği teknikleri kullanılarak eğitim alanındaki ham verilerin anlamlı hale getirilmesi ile öğretim etkinliklerinin daha etkin ve verimli hale getirilmesi mümkündür. Veri madenciliği yöntemleriyle 2014-2020 yılları arasında eğitim alanında yapılan çalışmalar "Science Direct" veri tabanından tarandı. Tarama çalışmalarından 60 makalenin eğitimde veri madenciliği ile doğrudan ilişkili olduğu tespit edilmiştir. Bu çalışmalar e-öğrenme sistemlerinin geliştirmesi, pedagojik destek, eğitim verilerinin kümelenmesi, öğrenci performans tahminleri gibi konuları içermektedir. Bu çalışmada eğitim alanında veri madenciliği yöntemi kullanılarak 2014 ile 2020 yılları arasında yapılmış araştırmalar “Science Direct” platformu üzerinden taranmıştır. Seçilen bu 60 adet makale, makalenin amacı, uygulama alanı ve örneklem, metot ve yöntemi, literatüre katkısı şeklinde tasnif edilerek sunulmuştur. Araştırmada; veri madenciliği yöntemi kullanılarak eğitim alanında yapılan çalışmaları belirli başlıklar altında gruplamak, yöntemlerini ve amaçlarını belirlemek ve bu alanda çalışacak olan kişilere yön göstermek amaçlanmıştır.

Anahtar Kelimeler

Eğitimde veri madenciliği, Veri madenciliği teknolojileri, Öğretim teknolojileri, Eğitimde yeni yaklaşımlar

Kaynakça

• 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(2), e01250.
• Agarwal, S., Pandey, G. N., & Tiwari, M. D. (2012). Data mining in education: data classification and decision tree approach. International Journal of E-Education, e-Business, e-Management and e-Learning, 2(2), 140.
• Ahmed, A. M., Rizaner, A., & Ulusoy, A. H. (2016). Using data mining to predict instructor performance. Procedia Computer Science, 102, 137–142.
• Aldowah, H., Al-Samarraie, H., & Fauzy, W. M. (2019). Educational data mining and learning analytics for 21st century higher education: A review and synthesis. Telematics and Informatics, 37, 13–49.
• Alfiani, A. P., & Wulandari, F. A. (2015). Mapping student’s performance based on data mining approach (a case study). Agriculture and Agricultural Science Procedia, 3, 173–177.
• Aljobouri, H. K., Jaber, H. A., Kocak, O. M., Algin, O., & Cankaya, I. (2018). Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining. Journal of Neuroscience Methods, 299, 45–54.
• Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in Marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1–7.
• Amornsinlaphachai, P. (2015). The design of a framework for cooperative learning through web utilizing data mining technique to group learners. Procedia-Social and Behavioral Sciences, 174, 27–33.
• Angeli, C., Howard, S. K., Ma, J., Yang, J., & Kirschner, P. A. (2017). Data mining in educational technology classroom research: Can it make a contribution? Computers & Education, 113, 226–242.
• Anoopkumar, M., & Rahman, A. M. J. M. Z. (2016). A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration. 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), 122–133.
• Asif, R., Merceron, A., Ali, S. A., & Haider, N. G. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers & Education, 113, 177–194.
• Aydoğdu, Ş. (2020). Educational Data Mining Studies in Turkey: A Systematic Review. Turkish Online Journal of Distance Education, 21(3), 170–185.
• Badr, G., Algobail, A., Almutairi, H., & Almutery, M. (2016). Predicting students’ performance in university courses: a case study and tool in KSU mathematics department. Procedia Computer Science, 82, 80–89.
• Bajaj, R., & Sharma, V. (2018). Smart Education with artificial intelligence based determination of learning styles. Procedia Computer Science, 132, 834–842.
• Baker, R. (2010). Data mining for education. International Encyclopedia of Education, 7(3), 112–118.
• Balaman, S. (2020). A Study on the Impacts of Digital Storytelling on EFL Learners’ Self-Efficacy and Attitudes toward Education Technologies. International Online Journal of Education and Teaching, 7(1), 289–311.
• Bhullar, M. S., & Kaur, A. (2012). Use of data mining in education sector. Proceedings of the World Congress on Engineering and Computer Science, 1, 24–26.
• 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.
• Cabada, R. Z., Estrada, M. L. B., & Bustillos, R. O. (2018). Mining of educational opinions with deep learning. Journal of Universal Computer Science, 24(11), 1604–1626.
• Campagni, R., Merlini, D., Sprugnoli, R., & Verri, M. C. (2015). Data mining models for student careers. Expert Systems with Applications, 42(13), 5508–5521.
• Chakraborty, B., Chakma, K., & Mukherjee, A. (2016). A density-based clustering algorithm and experiments on student dataset with noises using Rough set theory. 2016 IEEE International Conference on Engineering and Technology (ICETECH), 431–436.
• Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., & Tsolakidis, A. (2014). Improving quality of educational processes providing new knowledge using data mining techniques. Procedia-Social and Behavioral Sciences, 147, 390–397.
• Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16–24.
• Chen, J., Wei, W., Guo, C., Tang, L., & Sun, L. (2017). Textual analysis and visualization of research trends in data mining for electronic health records. Health Policy and Technology, 6(4), 389–400.
• Converse, G., Curi, M., & Oliveira, S. (2019). Autoencoders for educational assessment. International Conference on Artificial Intelligence in Education, 41–45.
• 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.
• Drayton-Brooks, S. M., Gray, P. A., Turner, N. P., & Newland, J. A. (2020). The use of big data and data mining in nurse practitioner clinical education. Journal of Professional Nursing.
• 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.
• Fok, W. W. T., He, Y. S., Yeung, H. H. A., Law, K. Y., Cheung, K. H., Ai, Y. Y., & Ho, P. (2018). Prediction model for students’ future development by deep learning and tensorflow artificial intelligence engine. 2018 4th International Conference on Information Management (ICIM), 103–106.
• Fotache, M., & Strimbei, C. (2015). SQL and data analysis. Some implications for data analysits and higher education. Procedia Economics and Finance, 20, 243–251.
• Gobert, J. D., Kim, Y. J., Sao Pedro, M. A., Kennedy, M., & Betts, C. G. (2015). Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills and Creativity, 18, 81–90.
• Guan, X., Fan, Y., Qin, Q., Deng, K., & Yang, G. (2020). Construction of science and technology achievement transfer and transformation platform based on deep learning and data mining technology. Journal of Intelligent & Fuzzy Systems, Preprint, 1–12.
• Guo, B., Zhang, R., Xu, G., Shi, C., & Yang, L. (2015). Predicting students performance in educational data mining. 2015 International Symposium on Educational Technology (ISET), 125–128.
• Guo, J., Gu, C., Yang, J., Zhang, Y., & Yang, H. (2020). Data mining and application of ship impact spectrum acceleration based on PNN neural network. Ocean Engineering, 203, 107193.
• Gupta, P., Mehrotra, D., & Sharma, T. K. (2015). Identifying knowledge indicators in higher education organization. Procedia Computer Science, 46, 449–456.
• Hassan, S. M., & Al-Razgan, M. S. (2016). Pre-university exams effect on students GPA: a case study in IT department. Procedia Computer Science, 82, 127–131.
• Hernández-Blanco, A., Herrera-Flores, B., Tomás, D., & Navarro-Colorado, B. (2019). A systematic review of deep learning approaches to educational data mining. Complexity, 2019.
• Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.-X., & Xu, C. (2018). Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Science of the Total Environment, 630, 1044–1056.
• Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2020). Systematic ensemble model selection approach for educational data mining. Knowledge-Based Systems, 105992.
• Irfan, M. T., & Gudivada, V. N. (2016). Cognitive Computing Applications in Education and Learning. In Handbook of Statistics (Vol. 35, pp. 283–300). Elsevier.
• Jiang, Y., & Li, X. (2020). Intelligent online education system based on speech recognition with specialized analysis on quality of service. International Journal of Speech Technology, 1–9.
• Juhaňák, L., Zounek, J., & Rohlíková, L. (2019). Using process mining to analyze students’ quiz-taking behavior patterns in a learning management system. Computers in Human Behavior, 92, 496–506.
• Karal, H., Nabiyev, V., Erümit, A. K., Arslan, S., & Çebi, A. (2014). Students’ opinions on artificial intelligence based distance education system (Artimat). Procedia-Social and Behavioral Sciences, 136, 549–553.
• Kaur, P., Singh, M., & Josan, G. S. (2015). Classification and prediction based data mining algorithms to predict slow learners in education sector. Procedia Computer Science, 57, 500–508.
• Kim, D., Park, Y., Yoon, M., & Jo, I.-H. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. The Internet and Higher Education, 30, 30–43.
• Kim, D., Yoon, M., Jo, I.-H., & Branch, R. M. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women’s university in South Korea. Computers & Education, 127, 233–251.
• Klimek, J., & Klimek, J. A. (2020). IT and Data Mining in Decision-Making in the Organization. Education Management in the Culture of Late Modernity. Procedia Computer Science, 176, 1990–1999.
• Koedinger, K. R., D’Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rose, C. P. (2015). Data mining and education. Wiley Interdisciplinary Reviews: Cognitive Science, 6(4), 333–353.
• Krau, S. D. (2015). The influence of technology in nursing education. Nursing Clinics, 50(2), 379–387.
• Lang, S., Bravo-Marquez, F., Beckham, C., Hall, M., & Frank, E. (2019). Wekadeeplearning4j: A deep learning package for weka based on deeplearning4j. Knowledge-Based Systems, 178, 48–50.
• Lara, J. A., Lizcano, D., Martínez, M. A., Pazos, J., & Riera, T. (2014). A system for knowledge discovery in e-learning environments within the European Higher Education Area–Application to student data from Open University of Madrid, UDIMA. Computers & Education, 72, 23–36.
• Li, X., & Wu, X. (2015). Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4520–4524.
• Li, Y., & Zhai, X. (2018). Review and prospect of modern education using big data. Procedia Computer Science, 129, 341–347. Liao, S. (2003). Knowledge management technologies and applications—literature review from 1995 to 2002. Expert Systems with Applications, 25(2), 155–164.
• Martínez-Abad, F., Gamazo, A., & Rodríguez-Conde, M.-J. (2020). Educational Data Mining: Identification of factors associated with school effectiveness in PISA assessment. Studies in Educational Evaluation, 66, 100875.
• Mayilvaganan, M., & Kalpanadevi, D. (2015). Cognitive skill analysis for students through problem solving based on data mining techniques. Procedia Computer Science, 47, 62–75.
• Miguéis, V. L., Freitas, A., Garcia, P. J. V, & Silva, A. (2018). Early segmentation of students according to their academic performance: A predictive modelling approach. Decision Support Systems, 115, 36–51.
• Natek, S., & Zwilling, M. (2014). Student data mining solution–knowledge management system related to higher education institutions. Expert Systems with Applications, 41(14), 6400–6407.
• Notten, T. (2014). Public management and leadership. Lasagne model in training for educational management. Journal of Social Intervention: Theory and Practice, 23(1), 144–148.
• Oeda, S., & Hashimoto, G. (2017). Log-Data Clustering Analysis for Dropout Prediction in Beginner Programming Classes. Procedia Computer Science, 112, 614–621.
• Ognjanovic, I., Gasevic, D., & Dawson, S. (2016). Using institutional data to predict student course selections in higher education. The Internet and Higher Education, 29, 49–62.
• Okubo, F., Yamashita, T., Shimada, A., & Ogata, H. (2017). A neural network approach for students’ performance prediction. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 598–599.
• Özden, C., & Atasoy, R. (2019). Determination of Educational Needs of Technology and Design Courses in Secondary School Students. International Online Journal of Education and Teaching, 6(3), 511–523.
• Pandey, U. K., & Bhardwaj, B. K. (2012). Data Mining as a Torch Bearer in Education Sector. ArXiv Preprint ArXiv:1201.5182.
• Park, Y., Yu, J. H., & Jo, I.-H. (2016). Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute. The Internet and Higher Education, 29, 1–11.
• Patil, S., & Kulkarni, S. (2018). Mining social media data for understanding students’ learning experiences using memetic algorithm. Materials Today: Proceedings, 5(1), 693–699.
• Popoola, S. I., Atayero, A. A., Badejo, J. A., John, T. M., Odukoya, J. A., & Omole, D. O. (2018). Learning analytics for smart campus: data on academic performances of engineering undergraduates in Nigerian private university. Data in Brief, 17, 76–94.
• Priyambada, S. A., Mahendrawathi, E. R., & Yahya, B. N. (2017). Curriculum Assessment of Higher Educational Institution Using Aggregate Profile Clustering. Procedia Computer Science, 124, 264–273.
• Rattanamethawong, N., Sinthupinyo, S., & Chandrachai, A. (2018). An innovation model of alumni relationship management: Alumni segmentation analysis. Kasetsart Journal of Social Sciences, 39(1), 150–160.
• Reamer, A. C., Ivy, J. S., Vila-Parrish, A. R., & Young, R. E. (2015). Understanding the evolution of mathematics performance in primary education and the implications for STEM learning: A Markovian approach. Computers in Human Behavior, 47, 4–17.
• Rodrigues, M. W., Isotani, S., & Zárate, L. E. (2018). Educational Data Mining: A review of evaluation process in the e-learning. Telematics and Informatics, 35(6), 1701–1717.
• Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146.
• Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27.
• Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
• Saleti, S., & Subramanyam, R. B. V. (2019). A MapReduce solution for incremental mining of sequential patterns from big data. Expert Systems With Applications, 133, 109–125.
• Sandoval, A., Gonzalez, C., Alarcon, R., Pichara, K., & Montenegro, M. (2018). Centralized student performance prediction in large courses based on low-cost variables in an institutional context. The Internet and Higher Education, 37, 76–89.
• Santoso, L. W. (2017). Data warehouse with big data technology for higher education. Procedia Computer Science, 124, 93–99.
• Sen, B., & Ucar, E. (2012). Evaluating the achievements of computer engineering department of distance education students with data mining methods. Procedia Technology, 1, 262–267.
• Shabtay, L., Fournier-Viger, P., Yaari, R., & Dattner, I. (2020). A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data. Information Sciences.
• Shahiri, A. M., & Husain, W. (2015). A review on predicting student’s performance using data mining techniques. Procedia Computer Science, 72, 414–422.
• Shukor, N. B. A., Tasir, Z., & van der Meijden, H. A. T. (2015). An examination of online learning effectiveness using data mining.
• Srinivas, S., & Rajendran, S. (2019). Topic-based knowledge mining of online student reviews for strategic planning in universities. Computers & Industrial Engineering, 128, 974–984.
• Stahovich, T. F., & Lin, H. (2016). Enabling data mining of handwritten coursework. Computers & Graphics, 57, 31–45.
• Taub, M., Azevedo, R., Bradbury, A. E., Millar, G. C., & Lester, J. (2018). Using sequence mining to reveal the efficiency in scientific reasoning during STEM learning with a game-based learning environment. Learning and Instruction, 54, 93–103.
• Tayefi, M., Tajfard, M., Saffar, S., Hanachi, P., Amirabadizadeh, A. R., Esmaeily, H., Taghipour, A., Ferns, G. A., Moohebati, M., & Ghayour-Mobarhan, M. (2017). hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm. Computer Methods and Programs in Biomedicine, 141, 105–109.
• Thuraisingham, B. (2003). Web data mining and applications in business intelligence and counter-terrorism. CRC Press. Ueda, H., & Nakamura, M. (2017). Data analysis for evaluation on course design and improvement of “cyberethics” moodle online courses. Procedia Computer Science, 112, 2345–2353.
• Valls, F., Redondo, E., Fonseca, D., Torres-Kompen, R., Villagrasa, S., & Martí, N. (2018). Urban data and urban design: A data mining approach to architecture education. Telematics and Informatics, 35(4), 1039–1052.
• Vieira, C., Parsons, P., & Byrd, V. (2018). Visual learning analytics of educational data: A systematic literature review and research agenda. Computers & Education, 122, 119–135.
• Viloria, A., López, J. R., Leyva, D. M. G., Vargas-Mercado, C., Hernández-Palma, H., Llinas, N. O., David, M. A., & Rodriguez, J. V. (2019). Data Mining Techniques and Multivariate Analysis to Discover Patterns in University Final Researches. Procedia Computer Science, 155, 581–586.
• Viloria, A., López, J. R., Payares, K., Vargas-Mercado, C., Duran, S. E., Hernández-Palma, H., & David, M. A. (2019). Determinating Student Interactions in a Virtual Learning Environment Using Data Mining. Procedia Computer Science, 155, 587–592.
• Vranic, M., Pintar, D., & Skocir, Z. (2007). The use of data mining in education environment. 2007 9th International Conference on Telecommunications, 243–250.
• Wang, H., Tan, X., Huang, Z., Pan, B., & Tian, J. (2020). Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks. Artificial Intelligence in Medicine, 101859.
• Wang, L., Sy, A., Liu, L., & Piech, C. (2017). Learning to Represent Student Knowledge on Programming Exercises Using Deep Learning. International Educational Data Mining Society.
• Wassan, J. T. (2015). Discovering big data modelling for educational world. Procedia-Social and Behavioral Sciences, 176, 642–649.
• Xing, W., Guo, R., Petakovic, E., & Goggins, S. (2015). Participation-based student final performance prediction model through interpretable Genetic Programming: Integrating learning analytics, educational data mining and theory. Computers in Human Behavior, 47, 168–181.
• Xu, X., Yin, X., & Chen, X. (2019). A large-group emergency risk decision method based on data mining of public attribute preferences. Knowledge-Based Systems, 163, 495–509.
• Yang, F., & Li, F. W. B. (2018). Study on student performance estimation, student progress analysis, and student potential prediction based on data mining. Computers & Education, 123, 97–108.
• Yucel, M., Aslan, Z., & Burunkaya, M. (2020). Classification of the temperature-dependent gain of an erbium-doped fiber amplifier by using data mining methods. Optik, 164515.
• Zhang, H., Huang, T., Liu, S., Yin, H., Li, J., Yang, H., & Xia, Y. (2020). A learning style classification approach based on deep belief network for large-scale online education. Journal of Cloud Computing, 9, 1–17.
• Zhao, S., Su, Z., & Miao, G. (2020). Application of English education information management system based on convolution neural network classification algorithm. The International Journal of Electrical Engineering & Education, 0020720920940614.
• Zhuhadar, L., Daday, J., Marklin, S., Kessler, B., & Helbig, T. (2019). Using survival analysis to discovering pathways to success in mathematics. Computers in Human Behavior, 92, 487–495.