Development of a Tool for Team Formation in Engineering Education

Efficient team formation presents challenges both for the industry and the academia, especially among first year students. In academia, the difficulty is due to a lack of familiarity between instructors and new students at the beginning of each semester while in the industry, the issue is the incomplete picture of new employee’s personality by the supervisors. The quality of the team greatly affects both the team member experience as well as the outcome of assigned projects. There is a strong need to create a tool or a program that allows instructors and supervisors to create effective teams with evenly distributed skills amongst the teams in a timely fashion. Studies show that the balance of skills, rather than the presence of highly skilled individuals, leads to successful teams. The ultimate goal is to create a tool that will give teams the opportunity to operate at their maximum potential. This paper focuses on the creation of teams for first year students of engineering. The outcome is based on the results of a project assigned to a team of second year engineering students. The choice of second year students was dictated by the need to have students who had already experienced the adverse effects of malfunctioning teams during their previous projects. The goal of the project was to design a software and user interface for a tool that instructors could use to create optimal project teams in an efficient manner.

A novel state space reduction algorithm for team formation in social networks

Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

A Chemistry-Oriented Cost-Effective Expert Team Formation in Social Networks

<p>The growth of social networks in modern information systems has enabled the collaboration of experts at an unprecedented scale. Given a social network and a task consisting of a set of required skills, Team Formation (TF) aims at finding a team of experts who can cover the required skills and can communicate in an effective manner. However, this definition has been interpreted as the problem of finding teams with minimum communication cost which neglects two aspect of team formation in real life. The first is that in reality experts are multi-skilled, hence communication cost cannot be a fixed value and should vary according to the channels employed. The second ignored aspect is disregarding teams with high expertise level who can still satisfy the required communication level. To tackle above mentioned issues, I introduce a dynamic formof communication for multi-facet relationships and use it to devise a novel approach called Chemistry Oriented Team Formation (ChemoTF) based on two new metrics; Chemistry Level and Expertise Level. Chemistry Level measures scale of communication required by the task andExpertise Level measures the overall expertise among potential teams filtered by Chemistry Level. Moreover, I adopt a personnel cost metric to filter costly teams. The experimental results on the corpus compiled for this purpose suggests that ChemoTF returns communicative and cost-effective teams with the highest expertise level compared to state-of-the-art algorithms. The corpus itself is a valuable output which contains comprehensive scholarly information in the field of computer science.</p>

A Chemistry-Oriented Cost-Effective Expert Team Formation in Social Networks

<p>The growth of social networks in modern information systems has enabled the collaboration of experts at an unprecedented scale. Given a social network and a task consisting of a set of required skills, Team Formation (TF) aims at finding a team of experts who can cover the required skills and can communicate in an effective manner. However, this definition has been interpreted as the problem of finding teams with minimum communication cost which neglects two aspect of team formation in real life. The first is that in reality experts are multi-skilled, hence communication cost cannot be a fixed value and should vary according to the channels employed. The second ignored aspect is disregarding teams with high expertise level who can still satisfy the required communication level. To tackle above mentioned issues, I introduce a dynamic formof communication for multi-facet relationships and use it to devise a novel approach called Chemistry Oriented Team Formation (ChemoTF) based on two new metrics; Chemistry Level and Expertise Level. Chemistry Level measures scale of communication required by the task andExpertise Level measures the overall expertise among potential teams filtered by Chemistry Level. Moreover, I adopt a personnel cost metric to filter costly teams. The experimental results on the corpus compiled for this purpose suggests that ChemoTF returns communicative and cost-effective teams with the highest expertise level compared to state-of-the-art algorithms. The corpus itself is a valuable output which contains comprehensive scholarly information in the field of computer science.</p>

Using Technology to Support Distributed Teams: A Work System-Based Study of an Interdisciplinary Team Responding to COVID-19

Effective teams are essential to meet the complex and dynamic requirements during pandemic response. This case study analyses the work system of mobileSHIELD, a distributed team developing a diagnostic test in response to the COVID-19 pandemic. We conducted interviews with 18 team members to understand how work system design influences the use of technology to support distributed teams. We identified six work system barriers and facilitators. The barriers related to rapidly adopting new technologies and not utilizing features of technologies that support relationships. The facilitators were related to the use of technology to support informal communication, synchronous and asynchronous communication, and mobile technology to improve productivity and collaboration. Our findings indicate technology that is mobile, cloud based, simple and user-friendly can support distributed teams, in particular by improving asynchronous communication. Future research will holistically explore implications for work system design to support interdisciplinary teams responding to societal crises.

Current Approaches to Team Building in Management

The relevance of the study stems from the fact that teamwork is increasingly seen in corporate environments as the foundation for impressive productivity. Creation of a favorable environment for cooperation is becoming more and more important. The purpose of this study is to present approaches to the formation of teams, to determine the fundamental principles. The practical relevance of this study will enable organizations and enterprises to strategize in the context of project management in order to form highly effective teams.

Exploring the Impact of Coordination in Human–Agent Teams

Teaming permits cognitively complex work to be rapidly executed by multiple entities. As artificial agents (AAs) participate in increasingly complex cognitive work, they hold the promise of moving beyond tools to becoming effective members of human–agent teams. Coordination has been identified as the critical process that enables effective teams and is required to achieve the vision of tightly coupled teams of humans and AAs. This paper characterizes coordination on the axes of types, content, and cost. This characterization is grounded in the human and AA literature and is evaluated to extract design implications for human–agent teams. These design implications are the mechanisms, moderators, and models employed within human–agent teams, which illuminate potential AA design improvements to support coordination.