**3. Research problem and hypotheses**

This study is a crucial step of our main research on supporting policy based management of 3D Multi User Learning Environments. In this phase, we are specific to identify the major factors that contribute for successful policy considerations for 3D MULE management. Student behaviour and control through system administration have been widely considered as major aspects for effective learning environment design and management. Based on our previous studies on 3D MUVE use cases and role analysis (Perera *et al.*, 2011a) and the 3D MUVE system function behaviours, we hypothesised these two parameters to be the most influential factors for successful policy considerations for 3D MULE management. As a result, in this study we examine the three factors: student behaviour, system (environment) management and student engagement with the learning environment as the research variables.

Therefore, we first defined the following two research hypotheses to examine the supposed two variables based on our previous observations.

**H1** – Student behaviour with self-regulation is a major factor of the successful 3D Multi User Learning Environment management

3D Multi User Learning Environment Management –

is given in the Table 1.

1 CS3102

2 CS5021

communication.

Module Associated

Data

communication and Networks

Advanced Networks and Distributed Systems

Experiment setup

An Exploratory Study on Student Engagement with the Learning Environment 113

Networks) and the other from the postgraduate (taught MSc program) curriculum (CS5021 – Advanced Networks and Distributed Systems). Information about the experiment samples

> Number of students

Brief Description of the Learning Task

simulations with different settings. Able to explain basic conditions and problems related with Wireless

simulations with different settings. Able to explain basic and advanced scenarios and problems related with

10 31 Interact with the Wireless Island and

communication.

11 28 Interact with the Wireless Island and

observe the Wireless traffic

observe the Wireless traffic

Wireless communication

SCQF level

Table 1. Details about the experiment samples and associated course modules

sample consisting of 59 students for the data analysis.

It is important to mention that these two course modules have different learning objectives and tasks while representing different levels in Scottish Credits and Qualification Framework (SCQF) (SCQF, 2007). Therefore, the subject content in each level had dissimilar objectives, and the assessment tasks were slightly diverse, although students used the same learning tool. However, for this study, we are focused on student engagement with the environment and the impact on that from the two aspects, their view of self-regulation and the system management. In that regard, we can conclude that, although students had a different level of the same learning task on the same learning aid, both samples had the same characteristics with respect to our measure. Therefore, the experiment has not been affected by the learning tasks given in the modules; hence can be considered as a single

The learning environment, Wireless Island (Sturgeon *et al.*, 2009), is a dedicated region for facilitating learning and teaching wireless communication. It was developed as a research project which provides interactive simulations with various configuration settings for students to explore and learn. It also includes supplementary learning content such as lecture notes, lecture media stream and a museum to depict the history of wireless communication development. Figure 2 shows the island layout (left-side image) with different interactive content and places for student learning. The right-side image of the Figure 2 shows the interactive simulation on teaching *Exposed Node Problem* in wireless

To facilitate small group learning with a less competitive environment interaction, we decided to have 6 students per region. Therefore, five regions were created in the OpenSim environment and loaded the Wireless Island on each. This resulted in an

**H2** – Appropriate system environment management is a major factor of the success of 3D Multi User Learning Environment management

For the second objective of this study, we investigated the impact of the above said two variables on the student engagement with the 3D MULE. Importantly, the engagement with the 3D MULE may not necessarily represent the student engagement with the learning, although there can be a positive correlation if the learning tasks are constructively aligned (Biggs, 1996). However, the opposite of the above condition is trivial; if the student engagement with the 3D MULE is low, then their engagement with the learning tasks that depend on 3D MULE, tend to be low as well, for obvious reasons. Nevertheless, since many researchers on 3D MUVE supported learning have indicated, as mentioned above, there are unique advantages of using 3D MUVE for teaching and learner support, which we may not be able to obtain through the other methods. If that is true, then deductively, we can presume that if students do not highly engage with 3D MUVE that host learning task, there is a high tendency on them having less engagement with the learning tasks as well.

Furthermore, as we are expected to formulate policy considerations for 3D MULE management, those policy considerations should not negatively affect the student engagement with the environment. Therefore, to examine the influence on student selfregulation and system environment management on student engagement with 3D MULE, we defined the following two hypotheses for the analysis.

**H3** – Students' self-regulatory behaviour has a positive and significant effect on student engagement with 3D Multi User Learning Environments

**H4** – System environment management has a positive and significant effect on student engagement with 3D Multi User Learning Environments
