Group or team formation has been a well-studied field in numerous contexts, such as business teams, project teams, and educational teams. There has, however, been little consideration of how groups or particularly project teams might have to be reorganized during a lecture in order to yield an optimal learning outcome. Empirical studies show that the outcome of groups will be affected by a number of factors, including the individual behavior of each group member, their skills and inclinations. In this research, the authors aim at finding correlations between the individual characteristics and learning outcomes during a running lecture so that teaching and learning can be improved. A study has been carried out that involved software engineering students over the past three years. The results show an improvement in the learning outcomes for groups that were systematically formed, which, for future settings, could enable educators as well as project leaders to systematically form groups and improve the outcomes of these groups in various domains.

Online laboratories have gained motion during the last decade, mainly due to the increase of online education programs. Online labs provide pedagogical values in some circumstances and fill in some gaps left open by traditional hands-on experiments. It is however a challenge to develop and deploy such laboratories for lecturing staff, as special software development skills are needed for such a task. In this research, we propose a novel approach for the deployment of new experimentation equipment. This system is not tailored to any specific type of laboratory. It can rather be understood as general purpose service, or lab as a service (LaaS). It is different from the approaches to deliver lab as a service implemented to date because the intended consumers of our service are lab owners, and not lab users (students, teachers and lab instructors). It shifts the complexity of a lab server to a central location (cloud) and ensures that the consumer (lab owner) has only to implement a tiny piece of software to deploy an online laboratory.

Online laboratories have been effective in bridging the gaps between theory and practice particularly in situations not covered by traditional hands on laboratories. Together with secondary school students we aim to develop 3 laboratory setups including a novel approach to seamlessly plug these setups to the cloud and make them available to other peers. Students will strongly be involved in the research which might illuminate new insights into the requirements of the young learners and teachers using and developing these laboratories. This paper proposes a novel approach to develop software components that might ease the provision of laboratory equipment in online portals. The anticipated results of this research include better understanding of students' preferences for the design of online laboratories. Based on these preferences, novel plug-play-share software components will be developed including a set of instruction how to use those software components on a large scale.

This work in progress paper describes the implementation of software components that aim at facilitating the development of online laboratories. We propose the creation of an additional level of abstraction in the development process of online laboratories that would allow teaching staff to plug existing equipment into a Remote Laboratory Management System (RLMS) and share it with peers and students. Online laboratories provide pedagogical values in some circumstances and many researchers encourage their use. It is however a challenge to develop and deploy such laboratories for lecturing staff, as special software development skills are needed for such a task. In this research, we propose a novel approach for the deployment of new experimentation equipment. To ease this deployment process we developed a software prototype that can be used across a broad range of domains that provides an easy way to connect laboratory equipment to the World Wide Web. Preliminary results show potential for this prototype to be used on large scale.

Skills and knowledge that can be gained by groups of individuals will be affected by the characteristics of those groups. Systematic formation of the groups could therefore potentially lead to significantly improved learning outcomes. This research explores a framework for group formation that continuously adapts rules used for the grouping process in order to optimize the selected performance criteria of the group. We demonstrate an implementation of this approach within the context of groups of students undertaking remote laboratory experiments. The implementation uses multiple linear regression analysis to adaptively update the rules used for creating the groups. In order to address specific learning outcomes, certain behaviors of the group might be desired to achieve this learning outcome. We can show that by using a set of individual/group characteristics and group behavior we can dynamically create rules and hence optimize the selected performance criteria. The selected performance is in reality the group behaviour, which might lead to improved learning outcomes.

Background: There is substantial literature that shows the benefits of collaborative work, though these benefits vary enormously with circumstances. Irrespective of their structure and composition, groups usually exist for a particular reason and implicitly or explicitly target one or more outcomes. The achievements of group outcomes depend on many factors, including the individual behaviour of each group member. These behaviours are, in turn, affected by the individual characteristics, the context and the group composition. Constructing groups in a way that maximises the achievement of a specific outcome is complex with the optimal group composition depending on the attributes of the group members. Previous work has in most cases considered group formation based on one particular attribute, such as learning style, gender, personality, etc. Less common are instances of group formation rules being adjusted systematically to accommodate changes in an individual's attributes or disposition. Purpose: This paper considers how the multi-factorial nature of group performance and the variations in desired behaviour across different circumstances can be addressed within a consistent framework. Design/Method: The methodology consisted of two main stages. In the first stage, a simulation was encoded in MatLab to assess the conceptual approach of progressively updating rules for group formation. The method uses an unsupervised learning algorithm and correlation factors between quantifiable group characteristics (average age, degree of motivation, etc.) and resultant behaviours of the groups that are actually formed (level of dialogue, interface interactions, etc.) to update the rules used for group formation, and hence progressively construct groups that are more likely to behave in desired ways. The second stage involved an evaluation of this approach in a real world scenario using remotely accessible laboratories where engineering students voluntarily participated in a study in April 2012. Results: The simulation results show that under certain conditions the desired behaviour chosen with the intention of improving specific learning outcomes can be optimized and that groups can be constructed that are more likely to exhibit desired behaviour. The paper also reports preliminary evidence that shows the feasibility of this approach in selecting group participants in an engineering class to promote a desired outcome in this case independent learning. Conclusions: This study demonstrates the feasibility of using a set of individual characteristics of group members to form groups that are more likely to have desired group behaviours and that these characteristics can be monitored and updated to dynamically alter group formation to account for changes in any individual's characteristics. This has potential to allow groups formation decisions to be made dynamically to achieve a desired outcome, for example promote collaborative learning.

Group/Team formation has been a well studied field in numerous contexts, (i.e. business teams, project teams, educational teams etc.) but have barely been considered within the scope of remote laboratories. Formation of educational groups in traditional labs/classes often occurs in an ad-hoc fashion where students are assigned to groups mostly without any particular constraints or regard to the group composition that is most likely to lead to optimal educational outcomes. This same ad hoc approach has typified the formation of groups within current remote laboratory environments that involve collaborative groups in remote laboratory settings. There is typically no arbitration for allocating group members to a specific group to perform a particular experiment. In this paper, we consider an approach to automated group formation that continuously analyses group performance and uses this to build rules regarding optimal group composition. These rules can be subsequently used to allocate students to groups that are more likely to have higher performance. Index Terms—group formation, remote laboratories, group allocation, team performance.