ISSN: 2222-6990
Open access
The distance learning operations and activities from Learning Management System (LMS) and various information system provide huge educational data. Data mining can be used as a tool to extract the huge educational raw data and turn them into useful information. Educational Data Mining (EDM) is the utilization of data mining systems on educational data, to analyse insight information and to determine educational strategic direction. This paper aims (1) to propose distance learners’ attributes in optimizing learning achievement using learning analytics and, (2) to identify the application areas of the attributes for Educational Data Mining and Learning Analytics. Data pre-processing is the first step in any data mining process and, Waikato Environment for Knowledge Analysis (WEKA) is one of the machine learning algorithms for educational data mining undertakings. It allows the transformation of raw educational data into a suitable format, ready to be used by a data mining algorithm for performing a specific educational data set. Therefore, a precise description of distance learners’ attributes views was developed and gathered. A framework was designed to demonstrate the proposal of distance learners’ attributes from distance learners’ profile, distance learners’ learning activities and distance learners’ learning behaviour; and it will shape the improvement of Distance Learners’ Learning Achievement. Hence, the specified educational dataset in the proposal can be adapted and visualize using any learning analytics tools for strategizing distance learning new strategies.
Aher, S. B., & Lobo, L. M. R. J. (2011, March). Data mining in educational system using weka. In International Conference on Emerging Technology Trends (ICETT) (Vol. 3, pp. 20-25).
Ahmadi, F., & Ahmad, M. S. (2013). Data mining in teacher evaluation system using WEKA. International Journal of Computer Applications, 63(10).
Amran, N., Bahry, F. D. S., Anwar, N., & Yu, H. (2016). Educational Data Utilization by the School Administrators to Improve the Students’ Academic Performance. International Journal of Academic Research in Business and Social Sciences, 6(11), 474-485.
Amran, N., Mohamed, H., & Bahry, F. D. S. (2018). Developing Human Resource Training Management (HRTM) Conceptual Model Using Entity Relationship Diagram (ERD). INTERNATIONAL JOURNAL OF ACADEMIC RESEARCH IN BUSINESS AND SOCIAL SCIENCES, 8(12).
Ayyoob, M. P. (2015). Tools Pros and Cons of Clustering Algorithms using Weka Tools. In International Journal of Computer Applications: National Conference on Advances in Computing Communication and Application, pp-13-16.
Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1, 1-57.
Burns, M. (2011). Distance education for teacher training: Modes, models and methods. Burns.–Washington: Education Development Center Inc.
Coronel, C., & Morris, S. (2019). 13rd Ed. Database Systems. Design, Implementation and Management. Cengage Learning
Educational Data Mining. (2020). Retrieved at https://educationaldatamining.org
Encyclopedia Britannica. (2020). Retrieved from https://www.britannica.com/
Fauzi, M., & Iga, S. (2020, February). Enhance the Institution Reputation with Open Learning System-Based Distance Learning Development Prototype in Globalization Era. In 2nd International Conference on Islam, Science and Technology (ICONIST 2019) (pp. 231-239). Atlantis Press.
Joshi, A. P., & Panchal, R. K. (2014). Data mining for staff recruitment in education system using WEKA. Int. J. Res. Comput. Sci. Manag, 2(1).
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.
Keegan, D. J. (1980). On defining distance education. Distance education, 1(1), 13-36.
Lewis, D., Madison-Harris, R., Muoneke, A., & Times, C. (2010). Using data to guide instruction and improve student learning. SEDL Letter, 22(2), 10-12.
Mining, T. E. D. (2012, October). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. In Proceedings of conference on advanced technology for education (pp. 1-64).
Prabha, S. L. (2015). Dr. AR Mohamed Shanavas. Application of Educational Data mining techniques in e-Learning A Case Study. International Journal of Computer Science and Information Technologies, 6(5), 4440-4443.
Romero, C., Romero, J. R., & Ventura, S. (2014). A survey on pre-processing educational data. In Educational data mining (pp. 29-64). Springer, Cham.
Saini, P. (2014). Building a Classification Model for Enrollment In Higher Educational Courses using Data Mining Techniques. arXiv preprint arXiv:1405.3729.
Thakur, R., & Mahajan, A. R. (2015). Preprocessing and Classification of Data Analysis in Institutional System using Weka. International Journal of Computer Applications, 112(6).
In-Text Citation: (Amran et al., 2021)
To Cite this Article: Amran, N., Bahry, F. D. S., & Yu, H. (2021). Distance Learners’ Attributes in Optimizing Learning Achievement Using Learning Analytics, 11(1), 441–453.
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