Distance learning and the empowerment of students: applied statistical analysis for students of the Built Environment.

Abstract

Built Environment students (including construction management, quantity surveying and so forth) generally exhibit limited understanding of mathematics and statistics, both from a theoretical and practical perspective (cf. Johnson, 1998; Llewellyn, 1999; Mtenga and Spainhour, 2000). This statement is supported by the fact that over half of the first year students (2001/2 intake) who completed an Individual Learner Profile (ILP) admitted to exhibiting poor mathematical skill. In addition, fewer than one in forty students have gained a mathematical qualification higher than a GCSE. Hence, undergraduate students are faced with a huge task when initially conceptualising the analytical component of a dissertation. Consequently, students elect to avoid robust and rigorous analysis in preference for elementary and somewhat naïve statistical methods to interpret any gathered data. This problem is further exacerbated by the reference to many ‘introductory’ statistical texts that are written for persons who have an ‘above average’ mathematical knowledge. Due to their background, Built Environment students struggle in transferring their data into a format that can be analysed and interpreted by statistical software. To do so requires time and commitment of staff combined with student initiative and drive. The problem here is that over 50% of students in the School of Engineering and the Built Environment (SEBE) attend University on a part time basis. Hence, physical restrictions limit these students’ ability to access the library and search for an appropriate textbook. Therefore, an easily accessible (internet) reference tool would provide an ideal opportunity with which to overcome this potential stumbling block. The aim of the proposed project was to develop an internet-based tool to assist undergraduate students learn ‘applied’ statistical analysis of data (relevant to typical construction problems) not just statistics per se. Such a tool would facilitate students, who actively seek to enhance their general mathematical and statistical knowledge as well as gain an insight into using commercially available statistical software simulation packages.