Personalised e-Learning

This thesis proposes to add value to the traditional e-learning systems by personalising the content being presented. The personalisation process was brought together through the amalgamation of crowdsourcing techniques, explicit with learners’ interests, and learner profiling technologies. A prototype called iPLE, intelligent personal learning environment, was developed and tested within an empirical study where participants experienced and compared the proposed iPLE with a static e-learning environment and a standard face-to-face delivery. A number of data collection instruments have been integrated within the empirical study to accumulate participants’ feedback. The results were fully documented and analysed using a combination of quantitative and qualitative data analysis tools that generated essential assessment information. An indicative improvement was reported following the data analysis and evaluation of results that led to the conclusion that even though there is plenty of room for further development and research, the combination of the proposed techniques does help and assist in rendering e-learning more effective.