EJEL Volume 6 Issue 3
October 2008

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

Liezl van Dyk
Centre for Teaching and Learning, Stellenbosch University, South Africa

An abundance of research, by educational researchers and scholars of teaching and learning alike, can be found on the use of ICT to plan design and deliver learning activities and assessment activities. The first steps of the instructional design process are covered quite thoroughly by this. However, the use of ICT and quantitative methods to close the instructional design cycle by supporting sustainable decision-making with respect to the evaluation of the effectiveness of teaching processes hold much unleashed potential. In this paper a business intelligence approach is followed in an attempt to take advantage of ICT to enable the evaluation of the effectiveness of the process of facilitating learning. The focus is on micro-level decision support based on data drawn from the Learning Management System (LMS). Three quantifiable measures of online behaviour (number of hits, total online time and hits consistency) and three quantifiable measures of teaching effectiveness (performance, learning styles and student satisfaction) are identified from the literature to arrive at a 3x3 matrix according to which nine measures of e-teaching effectiveness can be derived by means of pair-wise correlation. The value and significance of information are increased within the context of other information. In this paper it is shown how the value of LMS tracking data increases within the context of data from other modules or other years and that useful information is created when this tracking data is correlated with measures of teaching effectives such as results, learning styles and student satisfaction. This information context can only be created when a deliberate business intelligence approach is followed. In this paper a data warehouse model is proposed to accomplish exactly this.

Keywords: learning management system; data warehouse; student tracking, decision support; student feedback; learning styles

Download FULL PAPER

Back to Contents