Personalised e-learning, semantic web and learning ontologies

Kurzel(2004) points out that researchers in e-learning and educational technologists, in a quest to provide improved Learning Environments (LE) for students are focusing on personalising the experience through a Learning Management System (LMS) that attempts to tailor the LE to the individual (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez,2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman; 2003). According to Kurzel (2004) this tailoring can have an impact on content and how it’s accessed; the media forms used; method of instruction employed and the learning styles supported. This project is aiming to move personalisation forward to the next generation, by tackling the issue of Personalised e-Learning platforms as pre-requisites for building and generating individualised learning solutions. The proposed development is to create an e-learning platform with personalisation built-in. This personalisation is proposed to be set from different levels of within the system starting from being guided by the information that the user inputs into the system down to the lower level of being set using information inferred by the system’s processing engine. This paper will discuss some of our early work and ideas.

Single machine scheduling models with deterioration and learning: handling precedence constraints via priority generation

We consider various single machine scheduling problems in which the processing time of a job depends either on its position in a processing sequence or on its start time. We focus on problems of minimizing the makespan or the sum of (weighted) completion times of the jobs. In many situations we show that the objective function is priority-generating, and therefore the corresponding scheduling problem under series-parallel precedence constraints is polynomially solvable. In other situations we provide counter-examples that show that the objective function is not priority-generating.

The role of Minitab in teaching and learning statistics

This paper describes how the statistical package Minitab is used in teaching statistics in our undergraduate programmes in Mathematics and Statistics to enhance student learning. How the sophisticated recent versions of Minitab can be used to help students understand statistical concepts, develop their statistical thinking and gain valuable skills in performing statistical analysis are discussed.

Some issues on assessment methods and learning in mathematics and statistics

This paper looks at the application of some of the assessment methods in practice with the view to enhance students’ learning in mathematics and statistics. It explores the effective application of assessment methods and highlights the issues or problems, and ways of avoiding them, related to some of the common methods of assessing mathematical and statistical learning. Some observations made by the author on good assessment practice and useful approaches employed at his institution in designing and applying assessment methods are discussed. Successful strategies in implementing assessment methods at different levels are described.

Can effective teaching and learning strategies help student nurses to retain drug calculation skills?

Student nurses need to develop and retain drug calculation skills in order accurately to calculate drug dosages in clinical practice. If student nurses are to qualify and be fit to practise accurate drug calculation skills, then educational strategies need to not only show that the skills of student nurses have improved but that these skills have been retained over a period of time. A quasi-experimental approach was used to test the effectiveness of a range of strategies in improving retention of drug calculation skills. The results from an IV additive drug calculation test were used to compare the drug calculation skills of student nurses between two groups of students who had received different approaches to teaching drug calculation skills. The sample group received specific teaching and learning strategies in relation to drug calculation skills and the second group received only lectures on drug calculation skills. All test results for students were anonymous. The results from the test for both groups were statistically analysed using the Mann Whitney test to ascertain whether the range of strategies improved the results for the IV additive test. The results were further analysed and compared to ascertain the types and numbers of errors made in each of the sample groups. The results showed that there is a highly significant difference between the two samples using a two-tailed test (U=39.5, p<0.001). The strategies implemented therefore did make a difference to the retention of drug calculation skills in the students in the intervention group. Further research is required into the retention of drug calculation skills by students and nurses, but there does appears to be evidence to suggest that sound teaching and learning strategies do result in better retention of drug calculation skills.