Conclusions 163

This section will summarize the findings regarding the fulfilment of the research aim and objectives, (see Section 1.4), in addition to answering the research questions (see Section 1.3), about the research work presented in this thesis.

Research Question 1: How can we bridge the gap between Learning Management Systems (LMS) and adaptive e-learning systems?

This question can be divided into two research sub-questions, which are:

Research Question 1a:How can we provide interoperability between adaptive e-learning systems and Learning Management Systems (LMS)?

In Chapter 3, we analysed this question and presented a novel set of converters from MOT 1.0, My Online Teacher, to Sakai, a popular learning management system, using e- learning standards such as IMS QTI and IMS CP. The design and implementation of these converters were evaluated through a case study. A set of findings from the case study can be summarized as follows:

It is possible to bridge the gap between Learning Management Systems and adaptive e- learning systems, especially at the level of content conversion. For instance, mapping onto the XML format simplifies such a process, as many LMS use such a format. More importantly, mapping onto e-learning standards is essential, as LMS support these standards.

E-learning standards are the key factor in this interoperability issue because they allow for conversion due to the following features:

A). Independency: all LMS use e-learning standards such as IMS CP and IMS QTI, therefore the result of conversions can be easily imported to or exported from any platform that supports these standards.

B).Consistency: Standards are usually agreed upon by much larger communities than the adaptive e-learning community. Their changes are rare, and these standards are well documented. Thus, it is important to use them where possible.

The conversion to e-learning standards is useful for adaptive authoring systems, such as MOT: e-learning standards are vital in LMS. For example, the IMS QTI is used for tests and quizzes, and IMS CP is used for learning contents. Moreover, the converters proved to be easy to learn and easy to use.

The interoperability format issue can be solved using XML, as most learning management system support e-learning standards which are XML-based. This is simplified by the fact that many adaptive systems also use an XML-based data format.

Thus, the answer to the question “How can we provide interoperability between adaptive e-learning systems and Learning Management Systems (LMS)?” is “by using e-learning standards”. We have shown this by implementing the CAF to IMS QTI converter, and the CAF to IMS CP converter. It was also shown how these converters can be used to bridge the gap between learning management systems and adaptive e-learning systems.

Research Question 1b: How can we augment e-learning standards with adaptation in order to provide novel learning experiences?

In Chapter 4 we analysed this question and have shown how to accomplish this task using a set of converters from IMS CP and IMS QTI to MOT, in order to make it possible to use content defined via e-learning standards with an adaptive engine (such as AHA! that can furthermore run in Sakai).

The answer was created within the Adaptive Learning Spaces (ALS) EU Project, by converting an online course on the topic of the Unified Modelling Language (UML), which was used by a group of students that collaborated on a group-based project. The content was transformed from SCORM packages into AHA! lessons, based on the research work presented in Chapter 4, and the developed converters. The tools developed as part of ALS have also shown the great potential for adaptation to improve collaborative learning. Therefore, the answer to research question 1b is “by converting learning materials from IMS QTI and IMS CP into MOT 1.0, the process allows adding adaptive metadata and thus applying adaptation to content defined via e-learning standards, and hence adding adaptation to the LMS which use these standards.”

Research Question 2:How can we harness the Web 2.0 power and its characteristics to improve adaptive and personalized e-learning?

This question also can be divided into two further research sub-questions, which are:

Research Question 2a:Can the current adaptive e-learning frameworks benefit from the strengths of the Web 2.0?

This question examines whether the current adaptive e-learning frameworks are able to model the Web 2.0 features (i.e., tagging, voting, commenting, and user-generated content). We have shown in Section 2.4.6 that these frameworks, with their current architectures, cannot directly model Web 2.0 interactions or benefit from the Web 2.0 advantages. Thus, the answer to this question is “No”.

This further led to the development of a new framework, SLAOS, which extended a “pure” Adaptive Hypermedia framework, with the layers necessary to model social interaction. This framework and the system built based on it, were further used to answer the following research questions.

Research Question 2b: Are collaborative authoring and social annotations useful for authors and learners?

This question has been answered in Chapter 5 and Chapter 6, and the answer to this question is “Yes”. The pre-design case study revealed the importance of collaborative authoring and social annotations for both the authoring and the learning process, and for both learners and authors/teachers. We distinguished between two components of collaboration: collaborative authoring (which modifies the actual web resource, i.e., concept in the domain model by multiple authors) andsocial annotation(which allows the users - Adaptive Hypermedia authors as well as students – to add/edit information without modifying the actual resource).

Moreover, the SLAOS framework and the MOT 2.0 prototype have proven the usefulness of collaborative authoring and social annotations for both authors and learners. The SLAOS framework blended the authoring and delivering phases (i.e., removing the barrier between tutors, learners and authors, all of whom become authors with different sets of privileges).

Research Question 2c: Can the learning content recommendations and the expert peer recommendations of the learners enhance learning?

This question has been answered in Chapter 7 and Chapter 8, and the answer to this question is “Yes”. The second and third prototypes of MOT 2.0 have revealed the importance of the learning content recommendations and expert learners’ recommendation. The granularity of the recommendation in the second prototype was on the whole current module; whereas, the granularity of the recommendation in the second prototype was on the current item.