Tailoring the morphology of WO₃films with substitutional cation doping: Effect on the photoelectrochemical properties

With the aim of improving the performance and extending the range of applications of mesoporous WO₃films, which were initially developed for the photoelectrochemical oxidation of water, we investigated the effect of a number of dopants (lithium, silicon, ruthenium, molybdenum and tin) upon the transparency, crystallinity, porosity and conductivity of the modified films. Tin, molybdenum and silicon were shown to improve the electrochromic behaviour of the layers whereas ruthenium enhanced considerably the electronic conductivity of the WO₃films. Interestingly, most of the dopants also affected the film morphology and the size of WO₃nanocrystals. X-ray photoelectron spectra revealed absence of significant segregation of doping elements within the film. Raman analyses confirmed that the monoclinic structure of WO₃films does not change upon substitutional cation doping; thus, the crystallinity of WO₃films is maintained.

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.

Semantic web and e-learning

With emergence of "Semantic Web" there has been much discussion about the impact of technologies such as XML and RDF on the way we use the Web for developing e-learning applications and perhaps more importantly on how we can personalise these applications. Personalisation of e-learning is viewed by many authors (see amongst others Eklund & Brusilovsky, 1998; Kurzel, Slay, & Hagenus, 2003; Martinez, 2000; Sampson, Karagiannidis, & Kinshuk, 2002; Voigt & Swatman, 2003) as the key challenge for the learning technologists. According to Kurzel (2004) the tailoring of e-learning applications can have an impact on content and how it's accesses; the media forms used; method of instruction employed and the learning styles supported. This paper will report on a research project currently underway at the eCentre in University of Greenwich which is exploring different approaches and methodologies 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.