Towards a Generalised Pedagogical Framework for Creating Mixed-Mode Role-Play in a 3D Virtual Environment

Role-play has proved itself to be an effective teaching method, and role-play within a virtual environment has been found to be more especially so. Thus, there have been many studies concerned with role-play and computer simulation used together; however, up to this point, limitations may still be found with respect to the work which has been done in this area. Some of the major outstanding problems associated with creating virtual environments for learning are: finding the simplest way to model and represent abstract concepts as 3D objects; and implementing the students’ interactions - with each other, with their instructor, and with the represented objects. Also, many projects have focused on only one pedagogical topic. My vision is to introduce a generalized method that facilitates the construction of learning scenarios and renders them as message-passing role-play activities. Then, these activities could be deployed in a virtual environment (VE) in order to help students to become more immersed in the learning process. Each such activity is to be constructed by humanizing a ‘non-human’ object, whereby the students embody and imitate an (often abstract) object which is part of a technological system and which occurs in a virtual world. This can lead to many benefits, such as being able to better support the students’ ability to imagine and visualize such objects, making them more engaged with their learning, enhancing their conceptual understanding, strengthening their reasoning when solving problems related to the topic area, and reinvigorating their interest in learning. This research presents an evaluation of an approach for the creation of a role-play simulation in a role-play supporting virtual environment, which harnesses the advantages of 3D virtual environments in an effective way - in order to benefit the students’ learning in terms of improving their understanding of abstract concepts. Moreover, this approach is generalized and thus extends the previous studies by offering a system that can be applied to a wide range of topics - that involve message-passing role-play scenarios. The approach is presented within a conceptual pedagogical framework that is supported by an analysis of the findings and results from experiments that were conducted in order to validate the framework from both the learning and technical perspectives

Modelling Group Dynamics with SYMLOG and Snowdrift for Intelligent Classroom Environment

The aim of this thesis is to provide assistance to human teachers focusing on supporting group work within a classroom environment. This is achieved by incorporating theories from Psychology and Game Theory in order to provide a better method of modelling and predicting group interactions. This research proposes a framework that extends the pre-existing Intelligent Tutorial System (ITS) beyond the individual and into one that encompasses one or more groups of learners within a learning space. This framework transforms a traditional school classroom into a group interface as part of the communication module of an ITS and enhances the role of a human teacher. This is achieved by automating class management tasks and providing an immersive learning experience. Moreover, the proposed framework monitors emotional well-being and feeds back, to the teacher, emotional profiles of individuals and groups. This new ITS system is named Intelligent Classroom Tutoring System (ICTS). 6 experiments were conducted to support the ICTS. 2 experiments were set up to compare experimental frameworks for SYMLOG allowing the researchers to test a new mod-SYMLOG which was found to be an effective tool for modelling groups interactions. 1 experiment was centred around a longitudinal study of group work, and the final 3 composing of both AI and human studies, examining applying a new mod-Snowdift game to produce a predictive mechanism for group interaction

An Observation Framework for Recognising Learning Evidence in 3D Collaborative Virtual Environments

Immersive environments such as 3D virtual spaces enable collaborative learning and facilitate better connections between students, virtually. Learners do acquire new knowledge or skills while practising collaborative activities in such spaces. However, recognising evidence of learning to assess students is a critical issue which must be considered when organising learning activities in virtual environments. Although there is extensive coverage in the empirical literature regarding assessing learning in real-world classrooms, there is a lack of research focused on identifying learning evidence and assessing students who are performing educational activities within virtual worlds. This thesis aims to fill this research gap, exploit the affordances of immersive environments, and investigate appropriate methods for identifying users’ performance within these. This research proposes a computational framework, and a number of virtual observation models, for classifying learning evidence in immersive environments – and then maps all these elements to an appropriate learning design. In order to implement the computational framework required, the research includes the construction of a proof-of-concept prototype. The prototype employs virtual observation components and applies fuzzy logic and multi-agents approaches in order to assess students’ performance in real-time; this is from a number of different perspectives and based on multiple pedagogical frameworks. The present study also goes on to evaluate the research framework proposed by putting together a large number of educational sessions which are then carried out in a virtual world. These evaluation sessions involve both student and expert participants collaborating together to validate the model used. Subsequently, the thesis discusses the findings from the experimental sessions and their broader significance for the research area. Overall, the results strongly supported the effectiveness and usefulness of using the proposed virtual observation method when assessing collaborative students performing within immersive environments