Overview

From the review of existing case studies (as discussed in Section 2.2.3, i.e. ―impact of learning styles on group collaboration‖), mixed learning style groups tend to obtain better learning outcomes than other types of groups. Hence, the aim of this chapter is to propose a solution for group formation in a CLE which is able to formulate diverse learning style groups.

For achieving the aim of this chapter, the proposed grouping approach should address the following research questions. First, the approach should address the question of how to model students‘ learning styles. By the notion of ‗model‘, the process of acquiring learning style scores from individual students is referred to. Second, it needs to identify other elements besides learning styles that should be considered for the problem of group formation together with a method to define them. Furthermore, the approach should include a method to create diverse groups of students based on their learning style scores and the identified elements that affect the group formation.

Considering these research questions, the proposed iGLS approach is composed of the following components:

 a learning styles modelling component;

 a grouping parameter identification component; and

 a grouping algorithm.

The learning styles modelling component is responsible for acquiring learning style scores from individual students. The grouping parameter

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identification component attempts to determine the method for defining the values of parameters to be used in the process of group formation. The grouping algorithm is the method for assigning students into heterogeneous learning style groups (i.e. students with different levels of learning style).

The overall process of applying iGLS for completing a group formation task is illustrated in Figure 3.1. This process includes extracting students‘ learning style scores through the learning styles modelling component, defining the values of the parameters via the grouping parameter identification component and subsequently assigning students into diverse learning style groups by the grouping algorithm. The grouping algorithm can take the students‘ learning style scores and the grouping parameters as input and generate the desired grouping results.

Figure 3.1. The overall process of iGLS

As the group collaboration process is assumed to be carried out with a CLE, the components of the iGLS approach are desired to fit into current CLEs. Before describing how the components of iGLS fit a CLE, the modules that constitute current CLEs for supporting teaching and learning activities are discussed below.

Learning Styles Modelling Grouping Parameter Identification Grouping Algorithm Grouping Results (Collaborative groups) Learning style scores Grouping parameters

38 As mentioned in Section 2.1, the functionalities that a CLE provide are diverse, and can vary from educational administration to content management. The CLE block as shown in Figure 3.2 illustrates the functionalities that current CLEs (e.g. Moodle [128], LAMS [102] and Blackboard [26]) provide. These include administration, collaborative workplace, tools for collaborative activities and content management.

Figure 3.2. iGLS and collaborative learning environment

Each of the mentioned functionalities is supported by several modules of a CLE, which are described as follows:

 Administration:

- user management

- course management

- system settings

Administration

Tools for Collaborative Activities

Collaborative Workplace Content Management Learning Styles Modelling Grouping Parameter Identification Grouping Algorithm

CLE: Collaborative Learning Environment

iGLS: Intelligent Grouping based on Learning Styles

Activity Arrangement

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 Collaborative workplace:

- activity performing

 Tools for collaborative activities:

- tools for learning activities such as chats, forums, and bulletinboards

- tools for assessment activities such as questions, submit files, and multiple choices

 Content management:

- learning resources management

- collaborative activity arrangement

Figure 3.2 also illustrates how the three components of iGLS (the iGLS block) fit into a CLE for the process of group formation. The learning styles modelling component can be built on top of the user management module which supports the administration functionality of the underlying collaborative learning environment. The grouping parameter identification component can be integrated in the activity arrangement module which underpins the content management

functionality of the CLE. Moreover, the grouping algorithm component can be incorporated into the activity module that supports the functionality of

collaborative workplace. Details of the interactions between the iGLS components and a CLE are discussed later in Section 3.5.3.

In the remaining sections of this chapter, the three components of the iGLS approach, the iGLS add-on for LAMS and a scenario with the developed iGLS add-on are presented. Section 3.2 presents the categorization of learning styles that is adopted for describing students‘ learning styles, the reasons for choosing it and

40 how it is applied in the learning styles modelling component. Section 3.3 identifies other elements that should be considered for the process of group formation and how the parameters representing these elements can be determined in the component for grouping parameter identification. Following that, the details of the proposed grouping algorithm are presented in Section 3.4. Furthermore, Section 3.5 discusses how the iGLS add-on for LAMS was created including a brief description of the LAMS system, the architecture of the iGLS add-on, the essential implementation issues that were decided and a concise description of the components of the developed add-on. Subsequently, a real world scenario in which the developed iGLS add-on is used for supporting the process of group formation in a LAMS system is described in Section 3.6. Finally, Section 3.7 presents a summary of this chapter.