Using learning styles for dynamic group formation in adaptive collaborative hypermedia systems

Collaborative tools have been used in educational contexts for supporting communication and collaboration among students, discussions about topics, cooperative problem resolution, knowledge sharing and collaborative knowledge construction. A proper use of these tools reduces student isolation in web-based courses and facilitates the development of personal and social skills. At the same time, it is generally assented that learning styles are the preferences of students regarding to how they learn. It is desirable that a web-based instructional system includes information about the student learning style to optimally adapt the whole course to the individual characteristics of the students. Due to the benefits of the use of learning styles in adaptive hypermedia systems and the benefits of collaboration, we propose the use of learning styles to automatically adapt collaborative activities in web-based systems. Learning styles can be taken into account by proposing or discouraging collaborative activities, grouping students and choosing the most suitable statement of the problem and collaborative tools for each group of students.The Spanish Interdepartmental Commission of Science and Technology (CICYT), project number TIC2001-0685-C02-01, has sponsored this work

Blended Learning - What Practitioners Can Learn From MOOCs

The rapid increase in the use of information technologies in third level education is changing the way courses are provided. Online multimedia have helped reduce the difficulties teachers face with a diversity of student profiles and a large number of students in a classroom. Massive Open Online Courses (MOOCs) present an extreme with regard to student groups in relation to size and diversity and, therefore, many techniques and methods of overcoming the difficulties that this can present have been developed. Much of these methods can apply to online courses generally and to blended teaching environments. This study identifies four key areas where practitioners can learn from the large data set research provided by MOOCs. These are: methods of delivering content, dealing with the diversity of the group, providing for different learning preferences and assessment methods. Findings include the need for personalisation of the online environment, improving the use of peer assessment, the creation of more accessible learning content, and tailoring of the online environment to the pedagogical approach

Project team formation support for self-directed learners in social learning networks

Spoelstra, H., Van Rosmalen, P., & Sloep, P. B. (2012). Project team formation support for self-directed learners in social learning networks. In P. Kommers, P. Isaias, & N. Bessis (Eds.), Proceedings of the IADIS International Conference on Web Based Communities and Social Media (ICWBC & SM 2012) (pp. 89-96). July, 21-23, 2012, Lisbon, Portugal.Despite their name, social learning networks often lack explicit support for collaborative learning, even though collaborative learning offers benefits over individual learning. The outcomes of collaborative, project-based learning can be optimized when team formation experts assemble the project teams. This paper addresses the question of how to provide team formation services to individual, self-directed learners in a social learning network so they can make use of and profit from project-based learning opportunities. A model of a team formation process is presented, based on current team formation theory. It is used to design an automated team formation service that can be used by self-directed learners to form teams for project-based learning. Starting from a project description situated in a knowledge domain, the model defines three categories of variables that govern the team formation process: (I) knowledge, (II) personality and (III) preferences. Learner data on these categories are combined in a measure of fit, which calculates the best team for a project. A novelty introduced is that, depending on the desired project outcomes the relative weight of the categories can be altered to optimise the project formation process. The feasibility of the approach is demonstrated in an example in which the proposed algorithm is used to determine the most productive team for a project. Finally, future work and research are indicated

An assistant for group formation in CSCL based on constraint satisfaction

Group formation is a key aspect in computer-supported collaborative learning, since different characteristics of students might influence the group performance. In this article, we present an assistant that models group formation as a weighted constraint satisfaction problem (WCSP), and considers three students’ features, namely: psychological styles, team roles and social networks. Our WCSP formulation is able to combine constraints and preferences for individuals and groups. This assistant can aid teachers to form groups considering factors such as team role balance and distribution of psychological styles. We report on a pilot study to evaluate the proposal in different scenarios.Sociedad Argentina de Informática e Investigación Operativ

Effective team formation in networked learning settings

Professional development can be achieved by interacting with the abundance of learning materials provided by Internet-based services and by collaborating with other learners. However, knowledge sources are scattered across the Internet, while suitable co-learners are hard to find. Learning professionals require strong self-direction powers to fully benefit from these resources. However, these are not readily available in all learners. Based on social-constructivist/connectivist collaborative learning theory and team formation theory, a model is presented for the effective formation of teams engaging in structured collaborative learning. The model describes the creation knowledge domain representations by centralising learning materials from various sources. It allows learners to define structured learning tasks and provides an answer to the question whether a particular learning task can be addressed sufficiently well in the knowledge domain. Based on team formation theory, it provides the means to form teams of mutual learners and peer-teachers based on bridgeable knowledge differences (an interpretation of Vygotsky's "zone of proximal development") and personality aspects. The model also allows recommending suitable learning materials to the teams. A selection of tools is presented to afford an implementation of the model. These consist of an implementation of the method of Latent Semantic Analysis, a validated learning team formation algorithm and the Big Five personality test. The model is subsequently tested. The results of this test indicate that representations of knowledge domains can be successfully created and that the fit of learning tasks to the learning materials in the domain can be assessed. An experiment with learners (n=64) shows that the implementation can successfully assess prior knowledge and that collaborations based on prior knowledge differences do lead to knowledge gains. Furthermore, learners highly appreciate the learning materials suggested. However, the evidence for a level of knowledge difference between learners at which learning becomes most effective is currently limited. The results are discussed, and conclusions and directions for future research are included

An assistant for group formation in CSCL based on constraint satisfaction

Group formation is a key aspect in computer-supported collaborative learning, since different characteristics of students might influence the group performance. In this article, we present an assistant that models group formation as a weighted constraint satisfaction problem (WCSP), and considers three students’ features, namely: psychological styles, team roles and social networks. Our WCSP formulation is able to combine constraints and preferences for individuals and groups. This assistant can aid teachers to form groups considering factors such as team role balance and distribution of psychological styles. We report on a pilot study to evaluate the proposal in different scenarios.Sociedad Argentina de Informática e Investigación Operativ

Adaptive group formation to promote desired behaviours

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 individualâs 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 individualâs characteristics. This has potential to allow groups formation decisions to be made dynamically to achieve a desired outcome, for example promote collaborative learning

A method for group formation using genetic algorithm

Due to the increasing of complexity in software projects, group work is becoming more important in order to ensure quality software products can be delivered on time.Thus, in universities, group work is seen as a good preparation for students to enter industry because by working in group, it can reduce the individual workload, improve the ability to manage a project and enhance the problem solving skills. However, due to lack of programming skills especially in Java programming language, most of the students’ software project cannot be delivered successfully.To solve this problem, systematic group formation is one of the initial factors that should be considered to ensure that every group consists of quality individuals who are good in programming.This paper presents a method for group formation using genetic algorithm, where the members for each group will be generated based on the students’ programming skill

An Automatic Group Formation Method to Foster Innovation in Collaborative Learning at Workplace

Despite group formation in learning environments is commonly and successfully approached, there is a gap in the research literature with respect to its application in corporative learning. Regarding that creativity is as an important factor to increase innovation in companies, in the present research, we propose a group formation method, considering preferred roles and functional diversity, aiming to improve creativity in collaborative learning at workplace. We employed Tabu Search algorithm to automatically form groups based on Nonaka\u27s knowledge creation theory and preferred roles from Belbin’s model. We performed a case study to compare the quality of socio-cognitive interactions duringcollaborative learning in groups formed by the proposed method and randomly formed groups. The results show that groups formed by preferred roles and functional diversity are more creative and present enhanced fluency and more elaborated products in comparison to randomly formed groups