A Data Warehouse Model for Micro-Level Decision Making in Higher Education

In its broadest sense, e-learning can be defined as the facilitation of any type of learning by means of any type of information and communication technology (ICT). The process of facilitating learning (teaching process) is a cyclical process that typically consists of the following: Analyze the situation, define the outcomes, design and deliver of learning activities and assessment activities, then the effectiveness of the teaching process is evaluated, which lead again into a situational analysis with respect to the next teaching cycle. The use of ICT and quantitative methods to support decision making with respect to the evaluation of the effectiveness of teaching processes is far from reaching its full potential. In this paper a business intelligence approach is followed in attempt to exploit ICT to enable the evaluation of the effectiveness of the teaching process. Each time a lecturer or student logs into a Learning Management System (LMS), participates in an online discussion, completes an electronic quiz or reads an electronic document, an electronic transaction is performed. With each transaction performed, data are captured by the LMS. As a result loads of data are created, which are most often only archived for record keeping purposes and not used to support decision making. The purpose of the paper is to propose a data warehouse module for micro-level decision making that draws upon electronic student tracking data captured by LMSs and other information sources used by Higher Education Institutions (HEIs). For purposes of this paper e-learning is restricted to learning facilitated by an LMS. Within the scope of the paper, the LMS tracking data of most undergraduate Industrial Engineering modules of the University of Pretoria for 2005 and 2006 are used to learn about the methodological quality of data. To accomplish this, the student tracking data are quantified in terms of hits frequency, hits consistency and average time per hit. These indicators are correlated with performance per student per module as well as learning style index (Felder ILS).