Fostering the reduction of assortative mixing or homophily into the class

Human societies from the outset have been associated according to race, beliefs, religion, social level, and the like. These behaviors continue even today in the classroom at primary, middle, and superior levels. However, the growth of ICT offers educational researchers new ways to explore methods of team formation that have been proven to be efficient in the field of serious games through the use of computer networks. The selection process of team members in serious games through the use of computer networks is carried out according to their performance in the area of the game without distinction of social variables. The use of serious games in education has been discussed in multiple research studies which state that its application in teaching and learning processes are changing the way of teaching. This article presents an exploratory analysis of the team formation process based on collaboration through the use of ICT tools of collective intelligence called TBT (The best team). The process and its ICT tool combine the paradigms of creativity in swarming, collective intelligence, serious games, and social computing in order to capture the participants’ emotions and evaluate contributions. Based on the results, we consider that the use of new forms of teaching and learning based on the emerging paradigms is necessary. Therefore, TBT is a tool that could become an effective way to encourage the formation of work groups by evaluating objective variable of performance of its members in collaborative works.Postprint (published version

Investigation of Team Formation in Dynamic Social Networks

Team Formation Problem (TFP) in Social Networks (SN) is to collect the group of individuals who match the requirements of given tasks under some constraints. It has several applications, including academic collaborations, healthcare, and human resource management. These types of problems are highly challenging because each individual has his or her own demands and objectives that might conflict with team objectives. The major contribution of this dissertation is to model a computational framework to discover teams of experts in various applications and predict the potential for collaboration in the future from a given SN. Inspired by an evolutionary search technique using a higher-order cultural evolution, a framework is proposed using Knowledge-Based Cultural Algorithms to identify teams from co-authorship and industrial settings. This model reduces the search domain while guiding the search direction by extracting situational knowledge and updating it in each evolution. Motivated from the above results, this research examines the palliative care multidisciplinary networks to identify and measure the performance of the optimal team of care providers in a highly dynamic and unbalanced SN of volunteer, community, and professional caregivers. Thereafter, a visualization framework is designed to explore and monitor the evolution in the structure of the care networks. It helps to identify isolated patients, imbalanced resource allocation, and uneven service distribution in the network. This contribution is recognized by Hospice and the Windsor Essex Compassion Care Community in partnership with the Faculty of Nursing. In each setting, several cost functions are attempted to measure the performance of the teams. To support this study, the temporal nature of two important evaluation metrics is analyzed in Dynamic Social Networks (DSN): dynamic communication cost and dynamic expertise level. Afterward, a novel generic framework for TFP is designed by incorporating essential cost functions, including the above dynamic cost functions. The Multi-Objective Cultural Algorithms (MOCA) is used for this purpose. In each generation, it keeps track of the best solutions and enhances exploration by driving mutation direction towards unexplored areas. The experimental results reach closest to the exact algorithm and outperform well-known searching methods. Subsequently, this research focuses on predicting suitable members for the teams in the future, which is typically a real-time application of Link Prediction. Learning temporal behavior of each vertex in a given DSN can be used to decide the future connections of the individual with the teams. A probability function is introduced based on the activeness of the individual. To quantify the activeness score, this study examines each vertex as to how actively it interacts with new and existing vertices in DSN. It incorporates two more objective functions: the weighted shortest distance and the weighted common neighbor index. Because it is technically a classification problem, deep learning methods have been observed as the most effective solution. The model is trained and tested with Multilayer Perceptron. The AUC achieves above 93%. Besides this, analyzing common neighbors with any two vertices, which are expected to connect, have a high impact on predicting the links. A new method is introduced that extracts subgraph of common neighbors and examines features of each vertex in the subgraph to predict the future links. The sequence of subgraphs\u27 adjacency matrices of DSN can be ordered temporally and treated as a video. It is tested with Convolutional Neural Networks and Long Short Term Memory Networks for the prediction. The obtained results are compared against heuristic and state-of-the-art methods, where the results reach above 96% of AUC. In conclusion, the knowledge-based evolutionary approach performs well in searching through SN and recommending effective teams of experts to complete given tasks successfully in terms of time and accuracy. However, it does not support the prediction problem. Deep learning methods, however, perform well in predicting the future collaboration of the teams

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

Academic team formation as evolving hypergraphs

This paper quantitatively explores the social and socio-semantic patterns of constitution of academic collaboration teams. To this end, we broadly underline two critical features of social networks of knowledge-based collaboration: first, they essentially consist of group-level interactions which call for team-centered approaches. Formally, this induces the use of hypergraphs and n-adic interactions, rather than traditional dyadic frameworks of interaction such as graphs, binding only pairs of agents. Second, we advocate the joint consideration of structural and semantic features, as collaborations are allegedly constrained by both of them. Considering these provisions, we propose a framework which principally enables us to empirically test a series of hypotheses related to academic team formation patterns. In particular, we exhibit and characterize the influence of an implicit group structure driving recurrent team formation processes. On the whole, innovative production does not appear to be correlated with more original teams, while a polarization appears between groups composed of experts only or non-experts only, altogether corresponding to collectives with a high rate of repeated interactions

A Stochastic Team Formation Approach for Collaborative Mobile Crowdsourcing

Mobile Crowdsourcing (MCS) is the generalized act of outsourcing sensing tasks, traditionally performed by employees or contractors, to a large group of smart-phone users by means of an open call. With the increasing complexity of the crowdsourcing applications, requesters find it essential to harness the power of collaboration among the workers by forming teams of skilled workers satisfying their complex tasks' requirements. This type of MCS is called Collaborative MCS (CMCS). Previous CMCS approaches have mainly focused only on the aspect of team skills maximization. Other team formation studies on social networks (SNs) have only focused on social relationship maximization. In this paper, we present a hybrid approach where requesters are able to hire a team that, not only has the required expertise, but also is socially connected and can accomplish tasks collaboratively. Because team formation in CMCS is proven to be NP-hard, we develop a stochastic algorithm that exploit workers knowledge about their SN neighbors and asks a designated leader to recruit a suitable team. The proposed algorithm is inspired from the optimal stopping strategies and uses the odds-algorithm to compute its output. Experimental results show that, compared to the benchmark exponential optimal solution, the proposed approach reduces computation time and produces reasonable performance results.Comment: This paper is accepted for publication in 2019 31st International Conference on Microelectronics (ICM

The duality of networks and groups: Models to generate two-mode networks from one-mode networks

Focus theory describes how shared memberships, social statuses, beliefs, and places can facilitate the formation of social ties, while two-mode projections provide a method for transforming two-mode data on individuals' memberships in groups into a one-mode network of their possible social ties. In this paper, I explore the opposite process: how social ties can facilitate the formation of groups, and how a two-mode network can be generated from a one-mode network. Drawing on theories of team formation, club joining, and organization recruitment, I propose three models that describe how such groups might emerge from the relationships in a social network. I show that these models can be used to generate two-mode networks that have characteristics commonly observed in empirical two-mode social networks, and that they encode features of the one-mode networks from which they were generated. I conclude by discussing these models' limitations, and future directions for theory and methods concerning group formation

Information Gathering in Networks via Active Exploration

How should we gather information in a network, where each node's visibility is limited to its local neighborhood? This problem arises in numerous real-world applications, such as surveying and task routing in social networks, team formation in collaborative networks and experimental design with dependency constraints. Often the informativeness of a set of nodes can be quantified via a submodular utility function. Existing approaches for submodular optimization, however, require that the set of all nodes that can be selected is known ahead of time, which is often unrealistic. In contrast, we propose a novel model where we start our exploration from an initial node, and new nodes become visible and available for selection only once one of their neighbors has been chosen. We then present a general algorithm NetExp for this problem, and provide theoretical bounds on its performance dependent on structural properties of the underlying network. We evaluate our methodology on various simulated problem instances as well as on data collected from social question answering system deployed within a large enterprise.Comment: Longer version of IJCAI'15 pape

Toward Project-based Learning and Team Formation in Open Learning Environments

Open Learning Environments, MOOCs, as well as Social Learning Networks, embody a new approach to learning. Although both emphasise interactive participation, somewhat surprisingly, they do not readily support bond creating and motivating collaborative learning opportunities. Providing project-based learning and team formation services in Open Learning Environment can overcome these shortcomings. The differences between Open Learning Environments and formal learning settings, in particular with respect to scale and the amount and types of data available on the learners, suggest the development of automated services for the initiation of project-based learning and team formation. Based on current theory on project-based learning and team formation, a team formation process model is presented for the initiation of projects and team formation. The data it uses is classified into the categories “knowledge”, “personality” and “preferences”. By varying the required levels of inter-member fit on knowledge and personality, the team formation process can favour different teamwork outcomes, such as facilitating learning, creative problem solving or enhancing productivity. The approach receives support from a field survey. The survey also revealed that in every-day teaching practice in project-based learning settings team formation theory is little used and that project team formation is often left to learner self-selection. Furthermore, it shows that the data classification we present is valued differently in literature than in daily practice. The opportunity to favour different team outcomes is highly appreciated, in particular with respect to facilitating learning. The conclusions demonstrate that overall support is gained for the suggested approach to project-based learning and team formation and the development of a concomitant automated service

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