Adopting Jaya Algorithm for Team Formation Problem

This paper presents a simple and mighty metaheuristic algorithm, Jaya, which is applied to solve the team formation (TF) problem and it is a very fundamental problem in many databases and expert collaboration networks or web applications. The Jaya does not need any distinctive parameters that require comprehensive tuning, which is usually troublesome and inefficient. Among several optimization methods, Jaya is chosen for TFP because of its simplicity and it always avoids the worst solutions and moving towards the global best solution. This victorious nature makes Jaya Algorithm more powerful and significant as compared to any other contemporary optimization algorithms. To evaluate the efficiency of the Jaya Algorithm (JA) against another metaheuristic algorithm, Sine-Cosine Algorithm (SCA), both algorithms are tested and assessed for the TF problem solution using an ACM dataset containing experts and their skills. The experimental results validate the improved performance of the optimization solutions and the potential of JA with fast convergence for solving TF problems which are better than SCA

Team Formation for Scheduling Educational Material in Massive Online Classes

Whether teaching in a classroom or a Massive Online Open Course it is crucial to present the material in a way that benefits the audience as a whole. We identify two important tasks to solve towards this objective, 1 group students so that they can maximally benefit from peer interaction and 2 find an optimal schedule of the educational material for each group. Thus, in this paper, we solve the problem of team formation and content scheduling for education. Given a time frame d, a set of students S with their required need to learn different activities T and given k as the number of desired groups, we study the problem of finding k group of students. The goal is to teach students within time frame d such that their potential for learning is maximized and find the best schedule for each group. We show this problem to be NP-hard and develop a polynomial algorithm for it. We show our algorithm to be effective both on synthetic as well as a real data set. For our experiments, we use real data on students' grades in a Computer Science department. As part of our contribution, we release a semi-synthetic dataset that mimics the properties of the real data

Synergistic Team Composition

Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. Key factors influencing team performance are competences and personality of team members. Hence, we present a computational model to compose proficient and congenial teams based on individuals' personalities and their competences to perform tasks of different nature. With this purpose, we extend Wilde's post-Jungian method for team composition, which solely employs individuals' personalities. The aim of this study is to create a model to partition agents into teams that are balanced in competences, personality and gender. Finally, we present some preliminary empirical results that we obtained when analysing student performance. Results show the benefits of a more informed team composition that exploits individuals' competences besides information about their personalities

A Team-Formation Algorithm for Faultline Minimization

In recent years, the proliferation of online resumes and the need to evaluate large populations of candidates for on-site and virtual teams have led to a growing interest in automated team-formation. Given a large pool of candidates, the general problem requires the selection of a team of experts to complete a given task. Surprisingly, while ongoing research has studied numerous variations with different constraints, it has overlooked a factor with a well-documented impact on team cohesion and performance: team faultlines. Addressing this gap is challenging, as the available measures for faultlines in existing teams cannot be efficiently applied to faultline optimization. In this work, we meet this challenge with a new measure that can be efficiently used for both faultline measurement and minimization. We then use the measure to solve the problem of automatically partitioning a large population into low-faultline teams. By introducing faultlines to the team-formation literature, our work creates exciting opportunities for algorithmic work on faultline optimization, as well as on work that combines and studies the connection of faultlines with other influential team characteristics