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Using Big Data in Education: Curriculum Review with Educational Data Mining

Yıl 2022, Cilt: 4 Sayı: 2, 181 - 195, 31.12.2022
https://doi.org/10.51535/tell.1192930

Öz

Today, most educational institutions have become more interested in big data. Because the importance of extracting useful information from educational data to support decision-making on educational issues has increased day by day. In this context, through educational data mining, this research study aims to reveal the association rules among compulsory courses in the Computer Education and Instructional Technology curriculum within the faculty of education of a state university in Turkey. In this context, the research was conducted with data obtained from 258 preservice teachers who had completed all of their compulsory courses (n = 42) for the Computer Education and Instructional Technology curriculum, having graduated from the Computer Education and Instructional Technology program between 2012 and 2020. According to the experimental results, the academic performance of preservice teachers in some courses could be used as a predictor of their academic performance in other courses. Other findings from the study are discussed in detail, and suggestions put forth for future research.

Destekleyen Kurum

Not applicable

Proje Numarası

Not applicable

Teşekkür

Not applicable

Kaynakça

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Yıl 2022, Cilt: 4 Sayı: 2, 181 - 195, 31.12.2022
https://doi.org/10.51535/tell.1192930

Öz

Proje Numarası

Not applicable

Kaynakça

  • Abinowi, E., & Aminudin. (2020). Analysis of Instagram posting for marketing using apriori method. Palarch’s Journal of Archaeology of Egypt/Egyptology, 17(10), 3094-3101. https://archives.palarch.nl/index.php/jae/article/view/5445
  • Acharya, S., & Madhu, N. (2012). Discovery of students’ academic patterns using data mining techniques. International Journal on Computer Science and Engineering (IJCSE), 4(06), 1054-1062.
  • Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD, 22(2), 207-216. https://doi.org/10.1145/170035.170072
  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In J. B. Bocca, M. Jarke, & C. Zaniolo (Eds.), VLDB ’94: Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499). Kaufmann.
  • Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16, Article 40. https://doi.org/10.1186/s41239-019-0172-z
  • Alkatheri, S., Abbas, S. A., & Siddiqui, M. A. (2019). A comparative study of big data frameworks. International Journal of Computer Science and Information Security (IJCSIS), 17(1), 66-73. https://doi.org/10.5539/mas.v13n7p1
  • Altun, E., & Ateş, A. (2008). The problems and future concerns of computer and instructional technologies preservice teachers. Elementary Education Online, 7(3), 680-692. https://www.ilkogretim-online.org/fulltext/218-1596683177.pdf?1619079787
  • Baaziz, A., & Quoniam, L. (2013). How to use big data technologies to optimize operations in upstream petroleum industry. International Journal of Innovation, 1(1), 30-42. https://doi.org/10.5585/iji.v1i1.4
  • Baker, R. S. J. d. (2007). Is gaming the system state-or-trait? Educational data mining through the multi-contextual application of a validated behavioral model. In Complete On-Line Proceedings of the Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling 2007 (Vol. 2007, pp. 76-80). User Modeling. https://educationaldatamining.org/EDM_ORG/wp-content/uploads/2020/05/DM.UM07_proceedings_full.pdf
  • Baker, R. S. J. d. (2011). Data mining for education. In B. McGaw, P. Peterson, & E. Baker (Eds.), International Encyclopedia of Education (3rd ed., Vol. 7, pp. 112-118.). Elsevier.
  • Baker, R. S. J. d., & de Carvalho, A. M. J. A. (2008). Labeling student behavior faster and more precisely with text replays. Proceedings of the First International Conference on Educational Data Mining, (pp. 38-47).
  • Baker, R. S. J. d., & Yacef, K. (2009). The state of educational data mining in 2009 : A review and future visions. Journal of Educational Data Mining, 1(1), 3-16. https://doi.org/10.5281/zenodo.3554657
  • 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, 537-553. https://doi.org/10.1007/s10639-017-9616-z
  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. U.S. Department of Education, Office of Educational Technology. https://tech.ed.gov/wp-content/uploads/2014/03/edm-la-brief.pdf
  • Bozkurt, A. (2016). Öğrenme analitiği: E-öğrenme, büyük veri ve bireyselleştirilmiş öğrenme [Learning analytics: E-learning, big data and personalized learning]. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 2(4), 55-81. https://dergipark.org.tr/en/pub/auad/issue/34066/377071
  • Calders, T., & Pechenizkiy, M. (2012). Introduction to the special section on educational data mining. ACM SIGKDD Explorations Newsletter, 13(2), 3-6. https://doi.org/10.1145/2207243.2207245
  • Calvet Liñán, L., & Juan Pérez, Á. A. (2015). Educational data mining and learning analytics: Differences, similarities, and time evolution. RUSC. Universities and Knowledge Society Journal, 12(3), 98-112. https://doi.org/10.7238/rusc.v12i3.2515
  • Cil, I. (2012). Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10), 8611-8625. https://doi.org/10.1016/j.eswa.2012.01.192
  • Daniel, B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. https://doi.org/10.1111/bjet.12230
  • Demšar, J., & Zupan, B. (2013). Orange: Data mining fruitful and fun - A historical perspective. Informatica, 37(1), 55-60. http://www.informatica.si/ojs-2.4.3/index.php/informatica/article/viewFile/434/438
  • Dongre, J., Prajapati, G. L., & Tokekar, S. V. (2014). The role of apriori algorithm for finding the association rules in data mining. In A. Sharma, A. Ahlawat, A. Pandey, & V. Sharma (Eds.), International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT) (pp. 657-660). IEEE. https://doi.org/10.1109/ICICICT.2014.6781357
  • Dunham, M. H. (2003). Data mining introductory and advanced topics. Pearson.
  • Dutt, A., Aghabozrgi, S., Ismail, M. A. B., & Mahroeian, H. (2015). Clustering algorithms applied in educational data mining. International Journal of Information and Electronics Engineering, 5(2), 112-116. https://doi.org/10.7763/ijiee.2015.v5.513
  • Education Data. (2021). Online education statistics. https://educationdata.org/online-education-statistics.
  • Educational Data Mining Society. (2021). Educational Data Mining. https://educationaldatamining.org/ Elias, T. (2011). Learning analytics: Definitions, processes and potential. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.456.7092&rep=rep1&type=pdf.
  • Fernandes, E., Holanda, M., Victorino, M., Borges, V., Carvalho, R., & Erven, G. V. (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. https://doi.org/10.1016/j.jbusres.2018.02.012
  • Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130-160. https://doi.org/10.3102/0091732X20903304
  • Han, J., Kamber, M., & Pei, J. (2011). Data mining : Concepts and techniques. Kaufman.
  • 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, 4(4), 426-436. https://jolt.merlot.org/vol4no4/hung_1208.pdf
  • Hussain, S., Atallah, R., Kamsin, A., & Hazarika, J. (2019). Classification, clustering and association rule mining in educational datasets using data mining tools: A case study. Advances in Intelligent Systems and Computing, 765, 196-211. https://doi.org/10.1007/978-3-319-91192-2_21
  • International Telecommunication Union. (2016). ICT facts and figures 2016. https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2016.pdf
  • Jabbar, A., Akhtar, P., & Dani, S. (2020). Real-time big data processing for instantaneous marketing decisions: A problematization approach. Industrial Marketing Management, 90, 558-569. https://doi.org/10.1016/j.indmarman.2019.09.001
  • Jha, J., & Ragha, L. (2013). Educational data mining using improved apriori algorithm. International Journal of Information and Computation Technology, 3(5), 411-418. https://www.ripublication.com/irph/ijict_spl/08_ijictv3n5spl.pdf
  • Jhang, K.-M., Chang, M.-C., Lo, T.-Y., Lin, C.-W., Wang, W.-F., & Wu, H.-H. (2019). Using the apriori algorithm to classify the care needs of patients with different types of dementia. Patient Preference and Adherence, 13, 1899-1912. https://doi.org/10.2147/PPA.S223816
  • Joshi, A., & Sodhi, J. S. (2014). Target advertising via association rule mining. International Journal of Advance Research in Computer Science and Management Studies, 2(5), 256-261. http://www.ijarcsms.com/docs/paper/volume2/issue5/V2I5-0066.pdf
  • Kılınç, Ç. (2015). Üniversite öğrenci başarısı üzerine etki eden faktörlerin veri madenciliği yöntemleri ile incelenmesi [Examining the effects on university student success by data mining techniques] [Master’s Thesis]. Eskişehir Osmangazi University, Turkey. http://hdl.handle.net/11684/1256
  • Ko, C.-Y., & Leu, F.-Y. (2021). Examining successful attributes for undergraduate students by applying machine learning techniques. IEEE Transactions on Education, 64(1), 50-57. https://doi.org/10.1109/TE.2020.3004596
  • Kumar, V., & Chadha, A. (2012). Mining association rules in students assessment data. International Journal of Computer Science Issues, 9(5), 211-216. http://ijcsi.org/articles/Mining-association-rules-in-students-assessment-data.php
  • Lioutas, E. D., & Charatsari, C. (2020). Big data in agriculture: Does the new oil lead to sustainability? Geoforum, 109, 1–3. https://doi.org/10.1016/j.geoforum.2019.12.019
  • Long, P., Siemens, G., Conole, G., & Gašević, D. (2011). Proceedings of the 1st International Conference on Learning Analytics and Knowledge. ACM. https://dl.acm.org/doi/proceedings/10.1145/2090116
  • Mabe, M. (2003). The growth and number of journals. Serials, 16(2), 191-197. https://serials.uksg.org/articles/10.1629/16191/galley/729/download/
  • Moubayed, A., Injadat, M., Shami, A., & Lutfiyya, H. (2018). Relationship between student engagement and performance in e-learning environment using association rules. In IEEE World Engineering Education Conference (EDUNINE). IEEE. https://doi.org/10.1109/EDUNINE.2018.8451005
  • Natalia, D., & Salvatore, L. (2020). Apriori algorithm for association rules mining in aircraft runway excursions. Civil Engineering and Architecture, 8(3), 206-217. https://doi.org/10.13189/cea.2020.080303
  • Ougiaroglou S., & Paschalis G. (2012). Association rules mining from the educational data of ESOG web-based application. In L. Iliadis, I. Maglogiannis, H. Papadopoulos, K. Karatzas, & S. Sioutas (Eds.), Artificial Intelligence Applications and Innovations. AIAI 2012. IFIP Advances in Information and Communication Technology (Vol 382, pp. 105-114). Springer. https://doi.org/10.1007/978-3-642-33412-2_11
  • Parack, S., Zahid, Z., & Merchant, F. (2012). Application of data mining in educational databases for predicting academic trends and patterns. In IEEE International Conference on Technology Enhanced Education (ICTEE) (paper 17). IEEE. https://doi.org/10.1109/ICTEE.2012.6208617
  • Pardo, A., & Teasley, S. (2014). Learning analytics research, theory and practice: Widening the discipline. Journal of Learning Analytics, 1(3), 4-6. https://doi.org/10.18608/jla.2014.13.2
  • Putra, P. B. I. S. P., Suryani, N. P. S. M., & Aryani, S. (2018). Analysis of apriori algorithm on sales transactions to arrange placement of goods on minimarket. International Journal of Engineering and Emerging Technology, 3(1), 13-17. https://ocs.unud.ac.id/index.php/ijeet/article/download/41250/25102
  • Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135-146. https://doi.org/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. https://doi.org/10.1109/TSMCC.2010.2053532
  • Romero, C., & Ventura, S. (2013). Data mining in education. WIREs Data Mining and Knowledge Discovery, 3(1), 12-27. https://doi.org/10.1002/widm.1075
  • Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. WIREs Data Mining and Knowledge Discovery, 10(3), Article e1355. https://doi.org/10.1002/widm.1355
  • Shilo, S., Rossman, H., & Segal, E. (2020). Axes of a revolution: Challenges and promises of big data in healthcare. Nature Medicine, 26, 29-38. https://doi.org/10.1038/s41591-019-0727-5
  • Shweta, M., & Garg, K. (2013). Mining efficient association rules through apriori algorithm using attributes and comparative analysis of various association rule algorithms. International Journal of Advanced Research in Computer Science and Software Engineering, 3(6), 306-312. http://ijarcsse.com/Before_August_2017/docs/papers/Volume_3/6_June2013/V3I6-0192.pdf
  • Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851
  • Siemens, G., & Baker, R. S. J. d. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In S. B. Shum, D. Gasevic, & R. Ferguson (Eds.), Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252-254). ACM. http://dx.doi.org/10.1145/2330601.2330661
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  • Soimart, L., & Mookdarsanit, P. (2016, September 22-23). An admission recommendation of high-school students using apriori algorithm [Conference presentation]. 6th International Conference on Sciences and Social Sciences, Mahasarakham, Thailand.
  • Tan, H. R., Chng, W. H., Chonardo, C., Ng, M. T. T., & Fung, F. M. (2020). How chemists achieve active learning online during the COVID-19 pandemic: Using the community of inquiry (CoI) framework to support remote teaching. Journal of Chemical Education, 97(9), 2512-2518. https://doi.org/10.1021/acs.jchemed.0c00541
  • Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Computers in Human Behavior, 47, 157-167. https://doi.org/10.1016/j.chb.2014.05.038
  • Turkish Council of Higher Education. (2021). Higher Education System in Turkey. https://www.yok.gov.tr/en/institutional/higher-education-system
  • Wu, X., & Zeng, Y. (2019). Using apriori algorithm on students’ performance data for association rules mining. Advances in Social Science, Education and Humanities Research, 322, 403-406. https://dx.doi.org/10.2991/iserss-19.2019.105
  • Yang, Q., & Hu, Y. (2011). Application of improved apriori algorithm on educational information. In J. Watada, P.-C. Chung, J.-M. Lin, C.-S. Shieh, & J.-S. Pan (Eds.), 5th International Conference on Genetic and Evolutionary Computing (pp. 330-332). IEEE. https://doi.org/10.1109/ICGEC.2011.82
  • Yin, S., & Kaynak, O. (2015). Big data for modern industry: Challenges and trends. Proceedings of the IEEE, 103(2), 143-146. https://doi.org/10.1109/JPROC.2015.2388958
  • Yüksekoğretim Kurulu. (2021a). Eğitim Fakültesi Öğretmen Yetiştirme Lisans Programlari [Faculty of Education Teacher Education Undergraduate Programs]. https://www.yok.gov.tr/Documents/Yayinlar/Yayinlarimiz/egitim-fakultesi-ogretmen-yetistirme-lisans-programlari.pdf
  • Yüksekoğretim Kurulu. (2021b). Programların Güncelleme Gerekçeleri, Getirdiği Yenilikler ve Uygulama Esasları [New Teacher Education Undergraduate Programs]. https://www.yok.gov.tr/kurumsal/idari-birimler/egitim-ogretim-dairesi/yeni-ogretmen-yetistirme-lisans-programlari
  • Zaiane, O. R. (2002). Building a recommender agent for e-learning systems. In Proceedings of the International Conference on Computers in Education (pp. 55-59). IEEE. https://doi.org/10.1109/CIE.2002.1185862
Toplam 66 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Alan Eğitimleri
Bölüm Araştırma Makaleleri
Yazarlar

Yusuf Ziya Olpak 0000-0001-5092-252X

Mustafa Yağcı 0000-0003-2911-3909

Proje Numarası Not applicable
Yayımlanma Tarihi 31 Aralık 2022
Kabul Tarihi 11 Kasım 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 4 Sayı: 2

Kaynak Göster

APA Olpak, Y. Z., & Yağcı, M. (2022). Using Big Data in Education: Curriculum Review with Educational Data Mining. Journal of Teacher Education and Lifelong Learning, 4(2), 181-195. https://doi.org/10.51535/tell.1192930

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