Intelligent system for personalizing students' academic behaviors--a conceptual framework

• Published in: International journal of new computer architectures and their applications Vol. 2; no. 1; pp. 138 - 153
• Main Authors: Aziz, Azwa Abdul, Idris, Wan Mohd Rizhan Wan, Hassan, Hasni, Jusoh, Julaily Aida
• Format: Journal Article
• Language: English
• Published: The Society of Digital Information and Wireless Communications 01.01.2012
• Subjects: Algorithms; Case studies; Data mining; Decision-making; Forecasts and trends; Students
• ISSN: 2220-9085; 2220-9085

Business Intelligence (BI) refers to skills, processes, technologies, applications and practices used to support decision making. BI technologies provide historical, current, and predictive views of business operations which are normally used to analyze business data. Online Analytical Processing (OLAP) is one of the common BI approaches in answering multi-dimensional analytical queries for analytical purpose. In addition, Educational Data Mining (EDM) is an emergent discipline for exploring data, and a method to support learning and teaching processes. In this paper, we proposed Educational Intelligence (EI) Framework by combining BI technologies with various EDM algorithm techniques. Taking UniSZA as our case study, the patterns on students' academic behaviors and performance can be analyzed. A set of data from students' examination results in relational database is extracted into multi-dimensional model to support OLAP query processing. The results are grouped into several subject areas. Then, the analysis to recognize the patterns on students' academic behaviors is conducted using EDM algorithms. From the analysis, the groups of students who have excellent skills or vice versa can be identified. It also optimizes the time to perform current and historical data analysis. The weaknesses and strengths of the student can also be obtained. Finally, students' future potential areas of studies can be predicted using the framework.