Review of the Research Objectives

This section introduces the research objectives and reviews the means of achieving them.

A review of the existing work on automatic knowledge extraction from the Web, E- learning platforms and E-learning styles

The main objective of this study is to develop the E-learning system that can provide

personalised adaptable learning material to leaners; the system can be utilised by the

educational institutes with great flexibility. First, it is essential to review the existing work in

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the available NLP tools and resources discussed in Chapters 2 and 3, an ontology was selected

as the main tool to be utilised to extract the relevant learning resources from the Web. It will

take in account the learner`s pedagogical needs, background and learning styles before

commencing any search. Among the available learning styles, VARK was chosen due to its

ease of use and its free availability in order to identify the preferred learning styles of the learner

(Chapter 3), which will be considered in designing a course. Commonly used similarity

measures were reviewed in order to select the appropriate one to be employed for matching the

extracted material with the ontology concept as applied to a specific domain. The Dice

coefficient was chosen in the present study because of its ease of use and its superiority to

others in finding the best fit as a result of the intersection between the ontology domain

concepts and the entities on the Web.

Designing the architecture of the Adaptable and Personalised E-learning System (APELS)

After reviewing the literature for the most appropriate tools to be used, APELS was designed

as discussed in Chapter 4. APELS consists of three main models: the Learner model,

Information extraction model and delivery model. Each model is represented as a separate

entity in the architecture. The learner model includes information about the learner’s

background, pedagogical needs, learning styles and content preferences that help the system

determine the appropriate teaching strategies. The information extraction model includes the

relevance phase and the ranking phase. The relevance phase aims to extract the most relevant

websites from the freely available resources rapidly and cost-efficiently. This is performed first

by fetching a list of websites that deal with specific areas according to the learner’s request and

transforming them from HTML to XHTML to be more structured in order to facilitate the

matching and knowledge extraction processes. Thereafter, the elements in the XHTML files

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ontology of the specific domain of knowledge was constructed using the protégé tool to obtain

the OWL file forming the second vector. The two vectors were then matched by the dice

coefficient to find out the best similarity between them in order to extract only the relevant

websites.

In the ranking phase, a linguistic analysis of the extracted content was performed using the

Stanford CoreNLP tool to semantically annotate the target words. A novel learning outcome

validation approach is proposed in this research; it utilises the linguistic feature of NLP to

extract significant key phrases and keywords related to the pre-defined learning outcome,

which is sub-classified into Familiarity, Usage and Assessment as defined by Bloom’s

taxonomy. To perform this, eight linguistic rules and keyword based rule were developed in

this study to extract key phrases and keywords, which meet the learning outcomes, based on

defining the patterns of the parts of speech of the lexical items and their dependency relations.

To address the adaptability aspect of the system, a third model was added to APELS, which is

the delivery model. This model has a planner that structures the produced content into the

module title, a summary of the programme and the intended learning outcomes. Interestingly,

the planner is also able to update the content according to the learner’s feedback and learning style to ensure the adaptability of the system. The learner model, knowledge extraction model

and delivery model of APELS were assessed separately and then they were integrated to

formulate a novel E-learning system APELS that was then implemented using a specific field

of learning with a well-defined curriculum content, making use of computer science.

Implementing the proposed tools using a specific field of learning with well-defined curriculum content and integrating them into APELS to develop a computer based APELS system

The core of the thesis is to design the appropriate tools for APELS and to assess their

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individually through the performance of the necessary tasks (Chapter 4). It is the researcher`s

conviction that separating the three models, in particular the knowledge extraction model,

enhances the system’s flexibility and extensibility and allows for the reusability of all of its components in any educational domain. After designing the APELS architecture, it is important

to assess the functionality of this novel system as a whole by an experimental implementation

using the computer science domain (Chapter 5). First, the knowledge domain was structured

by organising the topics of the ACM/IEEE Computer Science Curriculum and establishing the

semantic relations between domain topics using ontology. The produced OWL files were

implemented in the matching process to extract the relevant learning resources. The results of

the matching process were presented in Chapter 5, where the system consulted a list of websites

for the specific module, “Algorithms and Data Structure”, with the best match is 53% using the dice coefficient. The websites with the least ranking were excluded from further analysis

as they were considered to be irrelevant to the learner`s request. The content of the relevant

websites was then validated against the pre-defined learning outcomes. A proposed novel

learning outcome validation approach was also applied; it utilised the linguistic features of NLP

to extract the significant key phrases and keywords related to the pre-defined learning outcome,

which is sub-classified into Familiarity, Usage and Assessment. For example, the highest

Familiarity score in the “Fundamental Programming Concepts” module was obtained by the (www.cplusplus.com/doc/ tutorial/variables) website, which reflects the higher frequency of

the familiarity-related key phrases and keywords in the content; therefore, this website is

considered to be the best for a learner who endeavours to gain a basic understanding of a topic

containing lots of definitions and illustrations of the fundamental programming concept. To

ensure that the system could deliver the right content that satisfy the pre-defined learning

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Evaluating the proposed system using experts from the field of education

The system evaluation process was described in Chapter 6; it was centred on performing

predictive evaluation of the learning material, which was produced by the system to meet the

pre-defined learning outcomes drawn by experienced reviewers. The evaluation also included

assessing the system usability and the unstructured interviews. Ten experts (university

academic staff from various disciplines, i.e. computing, mathematics and education) were

invited to perform this task. Overall, the feedback with regard to matching the content to the

learning outcomes was positive. 80% of the experts agree that the provided material was of

good quality and that it could be used for preparing and delivering a lecture in order to

familiarise the students with a given topic, and even more promising, 90% of them think that

the content provided by the system in the experiment was so high in quality that it could be

used as teaching material to achieve the Usage task. They agree that the content was

informative and comprehensive and that it clearly reflects the success of the novel learning

outcome validation approach and the NLP tool used to perform this function as well the

ontology tools used for information extraction from the Web. These results were promising

because extracting suitable learning materials is situated at the heart of APELS. Moreover, one

of the key goals of this system was achieved; it was using freely available resources that added

the cost-efficiency advantage to the system. Although the main goal of the developed system

was achieved successfully, it was also important to assess how convenient and satisfied the

user would be while navigating throughout the system. For this aspect, most of the experts

except one were satisfied with the system interface; however, few comments were raised by

the expert such as the lack of proper instruction while navigating the page, and the small font

size of some information items on the page, which should be addressed in the future. The

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requirements and queries; besides, it could update itself based on the learner feedback, which

ensures its adaptability.