Identifying group processes and affect in learners : A holistic approach to assessment in virtual worlds in higher education

In the last five years, the analytical techniques for identifying the processes of online learning have developed to the point where applications for the assessment of learning can be discussed. This would be most appropriate for twenty-first century skills-such as collaboration, decision-making, and teamwork skills-which are the core learning outcomes in immersive learning environments. The state of the art in this field is still at the stage of discovering patterns of the processes of learning, identifying stages, and suggesting their meaning. However, already it is important to consider what technologies can offer and what information teachers need in order to evaluate students' situated performance and to provide useful feedback. This chapter describes an imagined virtual world, one that affords the range of twentyfirst century skills, in order to illustrate types of analyes that could be conducted on learning process data. Such analytical methods could provide both descriptive information about the performance of learners and depict structures and patterns of their learning processes. The future assessment of learning in immersive virtual worlds may draw on data about deep embodied processes and multiple senses that usually underpin professional skills, such as affect, visual perception, and movement. This type of assessment could also provide deeper insights into many psychological processes in collaborative learning, decision-making, and problem-solving in virtual worlds, such as motivation, self-efficacy, and engagement. Overall, the view of the assessment presented in this chapter extends beyond the formal learning outcomes that are usually required by tertiary education quality and standards agencies and assessed in traditional courses in higher education to include a range of new capacities that may not be required but are essential for successful performance in contemporary workplaces.