Öz
E-öğrenme sistemlerinde veri ambarı (DW) kullanımı, öğrencilerin çeşitli açılardan değerlendirilmesine yardımcı olur. En iyi sonuçları elde etmek için, e-öğrenme platformunun içeriğini veya tasarımını kullanıcı davranışına göre değiştirebiliriz. Bu çalışmanın amacı, üniversitenin uzaktan eğitim programının ana bilgi sistemi olarak hizmet verecek bir veri ambarı altyapısı oluşturmaktır. Veri ambarı altyapısı sayesinde öğrencilerin öğrenim yönetim sistemindeki (ÖYS: Anadolum e-Kampüs) tüm etkinlikleri rutin olarak kayıt altına alınmaktadır. Öğrencilerin çalışma stilleri, tercih edilen e-öğrenme kaynakları ve sistemde harcanan zamanla ilgili çeşitli analitik raporlar almak mümkün olacaktır. Veri ambarı sistemi ayrıca açıköğretim sistemi ile ilgili öğrenci bilgi sistemi gibi diğer bilgi sistemlerinden de düzenli olarak veri çekmektedir. Veri ambarı, veritabanı gibi anlık operasyonel işlemleri yapmak yerine birden çok kaynaktan gelen verileri, tek bir birleşik görünümünde birleştirmek için tasarlanmıştır. Genel olarak, veri ambarları, işlem işleme yerine analitik işleme için kullanılır. Geçmiş verileri ve eğilimleri analiz etmenin yanı sıra iş kararlarını bilgilendirmeye yardımcı olabilecek modelleri ve görüşleri belirlemek için kullanılırlar. Veri ambarı kullanılarak analiz ve raporlama işlemleri farklı bir ortamda yapıldığında, günlük operasyonel işlemler ile meşgul olan veri tabanı üzerine daha fazla yük getirilmemiş olunur. Veri ambarı, günlük, aylık ve yıllık dahil olmak üzere çeşitli zaman dilimleri için öğrenci analitiği elde etmeyi kolaylaştıracaktır. Bu altyapı üzerinden sağlanan raporlar ÖYS sisteminin kalitesini artırmak amacıyla yapılan çalışmalarda kullanılmaktadır.
Anahtar Kelimeler
Veri analitiği Veri madenciliği Veri ambarı Açık ve uzaktan eğitim sistemi
Destekleyen Kurum
Anadolu Üniversitesi
Proje Numarası
Anadolu üniversitesi bilimsel araştırma projesi no: 1808E291
Kaynakça
- Araque, F., Salguero, A., Martínez, L., Navarro, E., & Calero, M. (2007). Data warehousing for improving web-based learning sites. International Journal of Emerging Technologies in Learning, 2(4). https://doi.org/10.3991/ijet.v2i4.81
- Ballard, C., Herreman, D., Schau, D., Bell, R., Kim, E., & Valencic, A. (1998). Data modeling techniques for data warehousing (p. 25). San Jose: IBM Corporation International Technical Support Organization.
- Cantabella, M., Martínez-España, R., Ayuso, B., Yáñez, J. A., & Muñoz, A. (2019). Analysis of student behavior in learning management systems through a Big Data framework. Future Generation Computer Systems, 90, 262-272. https://doi.org/10.1016/j.future.2018.08.003
- Gladić, D., & Petrovački, J. (2021, March). Using a data warehouse system to monitor and analyze student achievement in teaching process: student paper. In 2021 20th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-6). IEEE. https://doi.org/10.1109/INFOTEH51037.2021.9400685
- Golfarelli, M., Maio, D., & Rizzi, S. (1998, January). Conceptual design of data warehouses from E/R schemes. In Proceedings of the Thirty-First Hawaii International Conference on System Sciences (pp. 334-343). IEEE.
- Goyal, M., & Vohra, R. (2012). Applications of data mining in higher education. International Journal of Computer Science Issues (IJCSI), 9(2), 113. Janati, S. E., Maach, A., & El Ghanami, D. (2019). Learning analytics framework for adaptive E-learning system to monitor the learner’s activities. International Journal of Advanced Computer Science and Applications, 10(8).
- Kimball, R., & Ross, M. (2010). The Kimball group reader: relentlessly practical tools for data warehousing and business intelligence. John Wiley & Sons.
- Kurniawan, Y., & Halim, E. (2013). Use data warehouse and data mining to predict student academic performance in schools: A case study (perspective application and benefits). In Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 98-103). IEEE. https://doi.org/10.1109/TALE.2013.6654408 Lane, P., & Schupmann, V. (2007). Oracle Database Data Warehousing Guide. 11g Release 2 (11.2) E16579-01.
- Li, Q., Duffy, P., & Zhang, Z. (2022). A novel multi-dimensional analysis approach to teaching and learning analytics in higher education. Systems, 10(4), 96. https://doi.org/10.3390/systems10040096
- Li, Q., Li, Z., Han, J., & Ma, H. (2022). Quality assurance for performing arts education: A multi-dimensional analysis approach. Applied Sciences, 12(10), 4813. https://doi.org/10.3390/app12104813
- Nebić, Z., & Mahnič, V. (2010). Data warehouse for an e-learning platform. Latest Trends on Computers, 2, 415-420.
- Seker, S. E. (2015). Büyük veri ve büyük veri yaşam döngüleri. Ybs Ansiklopedi, 2(3), 10-17.
- Simitsis, A., Vassiliadis, P., & Sellis, T. (2005). Optimizing ETL processes in data warehouses. In 21st International Conference on Data Engineering (ICDE'05) (pp. 564-575). IEEE. https://doi.org/10.1109/ICDE.2005.103
- Solodovnikova, D., & Niedrite, L. (2005). Using data warehouse resources for assessment of e-learning influence on university processes. In: Proc. of the 9th East-European Conf. on Advances in Databases and Information Systems (ADBIS), (pp. 233. – 248), Tallinn.
- Zhang, Y., Oussena, S., Clark, T., & Kim, H. (2010). Use data mining to improve student retention in higher education. In Proceeding of the 12th International Conference on Enterprise Information System (ICEIS 2010), (pp. 8-12), Funchal.
- Zorrilla, M.E. (2009). Data warehouse technology for E-learning. In: D., Zakrzewska, E., Menasalvas, & L., Byczkowska-Lipinska (Eds.), Methods and Supporting Technologies for Data Analysis (ss. 1-20). Springer. https://doi.org/10.1007/978-3-642-02196-1_1
Öz
The use of a data warehouse (DW) in e-learning applications helps us evaluate students from different perspectives. Depending on user behavior, we may change the content or appearance of the e-learning platform to achieve the best results. The aim of this study is to build a data warehouse infrastructure, which is a central information system for Anadolu University's distance education system. Thanks to the data warehouse project, all learners’ activities in the Learning Management System (ÖYS: Anadolum e-Kampüs) will be recorded regularly. Various analytical reports were obtained about students' study styles, preferred e-learning resources, and the time spent in the system. Additionally, the data warehouse system regularly pulls information from other information systems, such as the student information system related to the open education system. Data warehousing is designed to combine data from multiple sources into a single unified view, rather than performing operational operations like a database. Generally, data warehouses are used for analytical processing as opposed to transaction processing. They are used to analyze historical data and trends, as well as to identify patterns and insights that can aid in informing business decisions. When analysis and reporting is done in a different environment using data warehousing, there is no additional load on the database, which is already busy with daily operational processes. The data warehouse has made it easy to obtain student analytics for various periods, including daily, monthly, and yearly. The reports provided through this infrastructure are used in studies to increase the quality of the LMS system.
Anahtar Kelimeler
Data analytics Data mining Data warehouse Open and distance learning
Proje Numarası
Anadolu üniversitesi bilimsel araştırma projesi no: 1808E291
Kaynakça
- Araque, F., Salguero, A., Martínez, L., Navarro, E., & Calero, M. (2007). Data warehousing for improving web-based learning sites. International Journal of Emerging Technologies in Learning, 2(4). https://doi.org/10.3991/ijet.v2i4.81
- Ballard, C., Herreman, D., Schau, D., Bell, R., Kim, E., & Valencic, A. (1998). Data modeling techniques for data warehousing (p. 25). San Jose: IBM Corporation International Technical Support Organization.
- Cantabella, M., Martínez-España, R., Ayuso, B., Yáñez, J. A., & Muñoz, A. (2019). Analysis of student behavior in learning management systems through a Big Data framework. Future Generation Computer Systems, 90, 262-272. https://doi.org/10.1016/j.future.2018.08.003
- Gladić, D., & Petrovački, J. (2021, March). Using a data warehouse system to monitor and analyze student achievement in teaching process: student paper. In 2021 20th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-6). IEEE. https://doi.org/10.1109/INFOTEH51037.2021.9400685
- Golfarelli, M., Maio, D., & Rizzi, S. (1998, January). Conceptual design of data warehouses from E/R schemes. In Proceedings of the Thirty-First Hawaii International Conference on System Sciences (pp. 334-343). IEEE.
- Goyal, M., & Vohra, R. (2012). Applications of data mining in higher education. International Journal of Computer Science Issues (IJCSI), 9(2), 113. Janati, S. E., Maach, A., & El Ghanami, D. (2019). Learning analytics framework for adaptive E-learning system to monitor the learner’s activities. International Journal of Advanced Computer Science and Applications, 10(8).
- Kimball, R., & Ross, M. (2010). The Kimball group reader: relentlessly practical tools for data warehousing and business intelligence. John Wiley & Sons.
- Kurniawan, Y., & Halim, E. (2013). Use data warehouse and data mining to predict student academic performance in schools: A case study (perspective application and benefits). In Proceedings of 2013 IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE) (pp. 98-103). IEEE. https://doi.org/10.1109/TALE.2013.6654408 Lane, P., & Schupmann, V. (2007). Oracle Database Data Warehousing Guide. 11g Release 2 (11.2) E16579-01.
- Li, Q., Duffy, P., & Zhang, Z. (2022). A novel multi-dimensional analysis approach to teaching and learning analytics in higher education. Systems, 10(4), 96. https://doi.org/10.3390/systems10040096
- Li, Q., Li, Z., Han, J., & Ma, H. (2022). Quality assurance for performing arts education: A multi-dimensional analysis approach. Applied Sciences, 12(10), 4813. https://doi.org/10.3390/app12104813
- Nebić, Z., & Mahnič, V. (2010). Data warehouse for an e-learning platform. Latest Trends on Computers, 2, 415-420.
- Seker, S. E. (2015). Büyük veri ve büyük veri yaşam döngüleri. Ybs Ansiklopedi, 2(3), 10-17.
- Simitsis, A., Vassiliadis, P., & Sellis, T. (2005). Optimizing ETL processes in data warehouses. In 21st International Conference on Data Engineering (ICDE'05) (pp. 564-575). IEEE. https://doi.org/10.1109/ICDE.2005.103
- Solodovnikova, D., & Niedrite, L. (2005). Using data warehouse resources for assessment of e-learning influence on university processes. In: Proc. of the 9th East-European Conf. on Advances in Databases and Information Systems (ADBIS), (pp. 233. – 248), Tallinn.
- Zhang, Y., Oussena, S., Clark, T., & Kim, H. (2010). Use data mining to improve student retention in higher education. In Proceeding of the 12th International Conference on Enterprise Information System (ICEIS 2010), (pp. 8-12), Funchal.
- Zorrilla, M.E. (2009). Data warehouse technology for E-learning. In: D., Zakrzewska, E., Menasalvas, & L., Byczkowska-Lipinska (Eds.), Methods and Supporting Technologies for Data Analysis (ss. 1-20). Springer. https://doi.org/10.1007/978-3-642-02196-1_1
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | Anadolu üniversitesi bilimsel araştırma projesi no: 1808E291 |
| Yayımlanma Tarihi | 15 Temmuz 2023 |
| Gönderilme Tarihi | 19 Ocak 2023 |
| Kabul Tarihi | 23 Haziran 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 13 Sayı: 3 |
