文章基本信息

标题：Distance Learners’ Attributes in Optimizing Learning Achievement Using Learning Analytics
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作者：Noraizan Amran ; Farrah Diana Saiful Bahry ; Halida Yu 等
期刊名称：International Journal of Academic Research in Business and Social Sciences
电子版ISSN：2222-6990
出版年度：2021
卷号：11
期号：1
页码：441-453
DOI：10.6007/IJARBSS/v11-i1/8157
语种：English
出版社：Human Resource Management Academic Research Society
摘要：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.