Behavioural and System Usability Aspects for Learning

For the success of any information system, it has to be usable and accepted by its users. Learning solutions based on virtual environments are also governed by this norm. This section includes related literature on system usability aspects, and user behaviours with specific considerations for the thesis of this work.

2.7.1 Learning Environment Usability

Usability of a software system is a well-accepted norm, which may even be considered as the most important attribute for certain application types. Nielsen developed usability heuristics [140] to define the main aspects of usability evaluation. There have been studies applying these generic usability concepts to develop educational environments expecting to achieve high usability. However, Jones et al., [141] argue that by directly following usability features without considering the educational and learning context can introduce challenges for teaching and learning. For example, highly usable user interface designed for a specific domain can still be used for educational purposes, yet can become overly complex when a particular learning and assessment session is designed within. This is particularly true and was evident in many studies of 3D game based learning; the rigid interface options and game plots introduced additional challenges unnecessarily when used for learning. The usability evaluation of an educational environment or tool should incorporate three measures with respect to the learning activity and the users involved: i.e., context, interactions and attitudes and outcomes [141, 142]. Suggestions for usability on teaching with 3D games in [142] indicate that the classification of usability characteristics has to take place in relation to the 3D virtual environment; complexities can be found with respect to UI, the available operations, and the strategies of the game.

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Three factors were evaluated and empirically confirmed in [142]: User Interface Acquaintance that indicates the student’s knowhow of the user interface, Navigational Effort that indicates how well the user could navigate through the virtual world, and Environment Distraction that indicates disturbance to learning taking away the student attention disengaging them from the educational task are the main factors of usability for learning with 3D virtual environments. These suggestions were also observed in our teaching and learning support projects in OpenSim (SL) as part of this research. The view that usability aspects of an educational environment in MUVEs should include the convenient facility to engage in learning activities in-world and the ability to manage the environment to get the maximum benefits for the learning has been followed. User training for managing MUVE learning include the above three factors and substantially help to increase the usability of MULEs.

One of the widely used generic and efficient usability evaluation measures is System Usability Scale (SUS) proposed by Brooke [143]. It consists of 10 questions: alternatively arranged 5 supporting (odd numbered questions with positive wordings) and 5 opposing statements (even numbered questions with negative wordings) aimed at the examined system usability. It evaluates the responses through 5-point Likert scale from Strongly Disagree (1) to Strongly Agree (5). For positively-worded items (1, 3, 5, 7 and 9), the score contribution is the scale position minus 1; i.e., {0, 1, 2, 3, 4}. For negatively-worded items (2, 4, 6, 8 and 10), it is 5 minus the scale position {4, 3, 2, 1, 0}. To get the overall SUS score, first add all the score values and then multiply the sum by 2.5. SUS scores range from 0 to 100. The statements are:

1. I think that I would like to use this system frequently. 2. I found the system unnecessarily complex.

3. I thought the system was easy to use.

4. I think that I would need the support of a technical person to be able to use this system. 5. I found the various functions in this system were well integrated.

6. I thought there was too much inconsistency in this system.

7. I would imagine that most people would learn to use this system very quickly. 8. I found the system very cumbersome to use.

9. I felt very confident using the system.

10. I needed to learn a lot of things before I could get going with this system.

SUS based usability analysis has been confirmed for its validity and sensitivity [144, 145]. Also SUS has found to be reliable even at low sample size of 12 participants, while providing rapid convergence to the accurate conclusion [144]. On a comparative study with 5 widely used usability measuring questionnaires, SUS yielded among the most reliable results across sample sizes [146]. Because of these advantages, SUS has been widely used in e-learning and other forms of TEL to examine the usability of those learning environments [147-149]. The flexibility and the generic nature of the SUS statements that could be well fitted into most of the learning contexts have been commended in these studies. SUS

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questionnaire has been used in MUVE supported educational projects as well. It was used for several educational islands developed in Second Life and OpenSim providing sufficient measures to evaluate the usability of those virtual regions through user studies [88, 92].

In overview, one of the major challenges of learning and teaching in existing MUVEs (OpenSim and SL in particular) is the usability of the environments; although the environment is attractive and engaging, the interface and the complex tasks associated with avatar and environment interactions can impede the motivation of students for learning as well as the academics for using those in their teaching, as highlighted by several previous studies. Moreover, the domain specific terminology used in viewer applications and the complex UI menu options further increase the difficulties for users. This is a significant challenge affecting the perceived usability of a MULE. The work presented in this thesis is pivotal in enhancing the usability of these environments for managed learning. Further details on usability evaluations are discussed in later chapters.

2.7.2 Self-regulatory Learning

Self-regulation is a widely discussed behavioural trait, which means individuals behave with self- control of their actions according to the context and environmental factors of their work. This concept of students behaving with a restrained approach imposed at their desire towards achieving learning objectives has been developed as a learning paradigm and widely researched in traditional and TEL environments.

Zimmerman and Schunk described self-regulated learning as a collection of self-generated thoughts, feelings, and actions, which are systematically oriented toward attainment of student goals [150]. Pintrich, defines self-regulation as “an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behaviour, guided and constrained by their goals and the learning environment contextual features” [151]. This is important when promoting self-regulatory learning in virtual environments. The considerations for environment context and its attributes are quite significant for engaging and supportive learning in MUVEs.

Schunk [152] has suggested that there is a need for more research aimed at improving students' self- regulatory skills as they are engaged in learning and to examine how learning environment contexts affect the amount and type of self-regulation displayed. In a study with immersive learning activities authors reported distractions to learning when students behaved as if they had forgotten what their ultimate learning goal was; they repeated actions without any particular meaning[142]. The reason can be because the students have been absent–minded about their learning task [142]. Virtual environments provide better learning experience when students self-regulate as the activities are learner centric and subject to learner behaviour [153]. Students with higher self-regulatory skills tend to be more academically motivated and display better learning [154]; this is relevant to the MUVE support learning as well since the students often have to follow exploratory and collaborative forms of learning.

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Usually, students expect the teacher to tell them what to do, how and when to do it, and when to stop doing it, yet too much external regulation (forced) can introduce negative results in their learning [155]. This was seen as a crucial factor for MULEs since the advantages of MUVEs that facilitate a range of student supportive modern pedagogies also require adequate levels of guidance and environment management and allow students to regulate their learning. Too much external regulation in the form of environment management can inhibit the advantage of MUVEs while too little can be prone to fail in achieving the learning goals. The research focus is pitched on this invaluable and yet to be explored area of study in the quest to support academics and students to have better and managed learning in MULEs.

A modified self-regulated learning model has been developed for augmented environments supporting adult learning [156]. The authors highlight the need of tailored self-regulatory approaches to fit with the domain of learning environment. Wan and Reddy [157], have examined self-regulated learning practices combining the idea of community of learning in 3D virtual environments (Second Life). Their views indicate the practice of self-regulatory learning within a community of avatars of the students in MUVE based learning. Moreover, they have argued that the conventional self-regulatory learning model that is based on the individual student should be extended for MUVE learning since student avatars are part of the community. Students are not only responsible for their individual learning goals but also for the community that they are part of [157]. This indeed supports the validity of our research focus where self-regulation should be part of the MULE management practices, not only in the form of learning but also for the environment interactions and avatar behaviours in-world. Further discussion on self-regulation is in Chapter 4.