A framework for an adaptable and personalised e-learning system based on free web resources

Aeiad, E 2017, A framework for an adaptable and personalised e-learning system based on free web resources , PhD thesis, University of Salford.

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An adaptable and personalised E-learning system (APELS) architecture is developed to provide a framework for the development of comprehensive learning environments for learners who cannot follow a conventional programme of study. The system extracts information from freely available resources on the Web taking into consideration the learners' background and requirements to design modules and a planner system to organise the extracted learning material to facilitate the learning process. The process is supported by the development of an ontology to optimise and support the information extraction process. Additionally, natural language processing techniques are utilised to evaluate a topic's content against a set of learning outcomes as defined by standard curricula. An application in the computer science field is used to illustrate the working mechanisms of the proposed framework and its evaluation based on the ACM/IEEE Computing Curriculum. A variety of models are developed and techniques used to support the adaptability and personalisation features of APELS. First, a learner’s model was designed by incorporating students’ details, students’ requirements and the domain they wish to study into the system. In addition, learning style theories were adopted as a way of identifying and categorising the individuals, to improve their on-line learning experience and applying it to the learner’s model. Secondly, the knowledge extraction model is responsible for the extraction of the learning resources from the Web that would satisfy the learners’ needs and learning outcomes. To support this process, an ontology was developed to retrieve the relevant information as per users’ needs. In addition, it transforms HTML documents to XHTML to provide the information in an accessible format and easier for extraction and comparison purposes. Moreover, a matching process was implemented to compute the similarity measure between the ontology concepts that are used in the ACM/IEEE Computer Science Curriculum and those extracted from the websites. The website with the highest similarity score is selected as the best matching website that satisfies the learners’ request.

A further step is required to evaluate whether the content extracted by the system is the appropriate learning material of the subject. For this purpose, the learning outcome validation process is added to ensure that the content of the selected websites will enable the appropriate learning based to the learning outcomes set by standard curricula. Finally, the information extracted by the system will be passed to a Planner model that will structure the content into lectures, tutorials and workshops based on some predefined learning constraints.

The APELS system provides a novel addition to the field of adaptive E-learning systems by providing more personalized learning material to each user in a time-efficient way saving his/her time looking for the right course from the hugely available resources on the Web or going through the large number of websites and links returned by traditional search engines. The APELS system will adapt better to the learner’s style based on feedback and assessment once the learning process is initiated by the learner. The APELS system is expected to develop over time with more users.

Item Type: Thesis (PhD)
Schools: Schools > School of Computing, Science and Engineering
Depositing User: E Aeiad
Date Deposited: 16 Jan 2018 08:48
Last Modified: 27 Aug 2021 23:34
URI: https://usir.salford.ac.uk/id/eprint/42531

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