Component Implementation

Since the LAMS system has been developed as a web application, the iGLS add-on was also implemented as web-based. A range of web technology was adopted for developing the add-on including Apache Struts, JSP, Java Servlet, and XML.

The Apache Struts web framework [12] enables the developed add-on to use a Model-View-Controller (MVC) architecture. This means that the code of the developed add-on was separated in three parts. The Model part represents the business (i.e. how to calculate students‘ learning styles and how to formulate collaborative groups based on the proposed grouping algorithm) or database (i.e. how to store and retrieve the obtained learning style scores and grouping results) code. The View part corresponds to the page design code (e.g. the web page that represents the learning style questionnaire). Moreover, the Controller part stands for the navigational code (e.g. forwarding a submission of the learning style questionnaire to the backend score calculation module).

JSP technology is responsible for generating dynamic web pages in terms of the presentation of the learning style scores and the grouping results to individual students. JSP technology is also in charge of creating static web pages

58 with regard to the presentation of the LSQ and the configuration of the grouping parameters.

In addition, Java Servlet technology is responsible for handling the requests from a client and dispatching relevant responses to the client. Furthermore, XML technology is used to represent the Struts configuration for the whole application.

Next, the implementations for each component of the iGLS add-on are described. As the focus of this subsection is the implementation procedure that it embodies, concrete implementation constructs such as JSP pages, Servlet classes, and Java data access classes (for storing and retrieving data from the database) are avoided.

Learning Styles Modelling

Figure 3.5 illustrates the main modules that were developed for the learning styles modelling component. ‗Collecting questionnaire‘ is a module which handles the delivery of the LSQ to individual students and collects responses to the questionnaire for further processing. The module ‗calculating scores‘ calculates the learning style scores based on the method discussed in Section 3.2 and forwards the results to the module of ‗display results‘ for showing the learning style scores to individual students. Additionally, the ‗storing learning style scores‘ module can store the calculated scores to the LAMS database. These scores are stored in a table named ‗lams_user_score‘ that was created for the iGLS add-on.

59 Figure 3.5. Implementation of learning styles modelling

Grouping parameter Identification

As mentioned in Section 3.5.3, the grouping parameter identification component was built on top of the LAMS core module ‗Author‘ since it is the module that enables defining the parameters such as the number of groups to be created for the LAMS own grouping component. A new grouping type ‗iGLS-grouping‘ was created which sets the number of students per group as a property. When a course manager or a teacher creates a grouping design for a learning process, the ‗Author‘ module which incorporates the defined grouping type ‗iGLS-grouping‘ allows them to decide the value of the parameter. This configuration can then be adopted by the grouping algorithm implementation component when a group formation process starts.

Grouping Algorithm Implementation

Figure 3.6 demonstrates the main modules created for implementing the iGLS grouping algorithm. The ‗iGLS grouper‘ is the module that implements the Algorithm 3-1, which consists of several parts as shown in Figure 3.6. The middle layer of the figure shows the modules for retrieving learning style scores (namely ‗learning style scores querier‘) and storing the grouping results (namely ‗grouping

Collecting Questionnaire Display Results Calculating Scores Storing Learning Style Scores LAMS Database Table lams_user_score

60 results querier‘) with the LAMS database. After retrieving students‘ learning style scores from the LAMS database, the ‗learning style scores querier‘ module can send the learning style scores to the module ‗iGLS grouper‘ for sorting the students. After generating the grouping results, the ‗iGLS grouper‘ can provide the results to the ‗grouping results querier‘ to store them into the LAMS database. As shown at the bottom of Figure 3.6, a table named as ‗lams_iGLS_groups‘ was created to store the grouping results. The table ‗lams_user_score‘ can provide the required learning style scores for the ‗iGLS grouper‘ module.

Figure 3.6. Implementation of the iGLS grouping algorithm

Supporting Table Creation

Two tables namely ‗lams_user_score‘ and ‗lams_iGLS_group‘ were created for the iGLS add-on. Since the LAMS database was created with a MySQL system, these two tables were also established in the MySQL system. As mentioned above, the table ‗lams_user_score‘ is used to store students‘ learning style scores that are extracted via the learning styles modelling component. The table ‗lams_iGLS_group‘ is used to store the grouping results which are produced by the

Learning Style Scores Querier

Grouping Results Querier Remove Learners

Randomly Create Groups

Sort Learners for Segment Insert Learners to Segment Assign Students to Groups Handle orphan students Select Group iGLS Grouper LAMS Database Table lams_user_score lams_iGLS_groups

61 grouping algorithm implementation component. Since the schemas for the two tables are simple, they are not presented in this subsection.