Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses

Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of Digital Information & Web Technologies (ICADIWT), India, February 2014 (6 pages, 3 figures

The Team Balancing Act - Enhancing Knowledge - Building Activity in On-Line Learning Communities

Online learning in the university sector is a given. Constructivist views of learning (often team based) and the notion of knowledge-building, mediated through the use of ICTs seemingly address many of the imperatives to equip individuals for emergent knowledge-age work practice. While teamwork has many perceived advantages, teams also inexplicably fail despite the apparent quality of the participants. Teams are successful when members address what is a relatively narrow range of actions. However, even within this limited range of actions individuals demonstrate definite preferences towards certain activities and roles. This paper reports on the findings from a study that investigated if knowledge-building activity can be enhanced in tertiary education CSCL environments through the use of groups balanced by Team Role Preference (Margerison & McCann, 1995, 1998). The study found that higher quality knowledge-building activity was more likely to occur in balanced groups than in random groups. The analysis of data revealed that a diversity of ideas was more likely to emerge from within balanced groups than from within random groups particularly when the random groups were heavily skewed towards one team role preference. This provided a compelling reason for explaining why balanced groups may lead to better knowledge-building activity

Limited Epistocracy and Political Inclusion

In this paper I defend a form of epistocracy I call limited epistocracy— rule by institutions housing expertise in non-political areas that become politically relevant. This kind of limited epistocracy, I argue, isn’t a far-off fiction. With increasing frequency, governments are outsourcing political power to expert institutions to solve urgent, multidimensional problems because they outperform ordinary democratic decision-making. I consider the objection that limited epistocracy, while more effective than its competitors, lacks a fundamental intrinsic value that its competitors have; namely, political inclusion. After explaining this challenge, I suggest that limited epistocracies can be made compatible with robust political inclusion if specialized institutions are confined to issuing directives that give citizens multiple actionable options. I explain how this safeguards citizens’ inclusion through rational deliberation, choice, and contestation

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

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