207,653 research outputs found
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
Profit-aware Team Grouping in Social Networks: A Generalized Cover Decomposition Approach
In this paper, we investigate the profit-aware team grouping problem in
social networks. We consider a setting in which people possess different skills
and compatibility among these individuals is captured by a social network.
Here, we assume a collection of tasks, where each task requires a specific set
of skills, and yields a different profit upon completion. Active and qualified
individuals may collaborate with each other in the form of \emph{teams} to
accomplish a set of tasks. Our goal is to find a grouping method that maximizes
the total profit of the tasks that these teams can complete. Any feasible
grouping must satisfy the following three conditions: (i) each team possesses
all skills required by the task, (ii) individuals within the same team are
social compatible, and (iii) each individual is not overloaded. We refer to
this as the \textsc{TeamGrouping} problem. Our work presents a detailed
analysis of the computational complexity of the problem, and propose a LP-based
approximation algorithm to tackle it and its variants. Although we focus on
team grouping in this paper, our results apply to a broad range of optimization
problems that can be formulated as a cover decomposition problem
Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation
In this paper, we study the problem of Team Member Replacement: given a team
of people embedded in a social network working on the same task, find a good
candidate who can fit in the team after one team member becomes unavailable. We
conjecture that a good team member replacement should have good skill matching
as well as good structure matching. We formulate this problem using the concept
of graph kernel. To tackle the computational challenges, we propose a family of
fast algorithms by (a) designing effective pruning strategies, and (b)
exploring the smoothness between the existing and the new team structures. We
conduct extensive experimental evaluations on real world datasets to
demonstrate the effectiveness and efficiency. Our algorithms (a) perform
significantly better than the alternative choices in terms of both precision
and recall; and (b) scale sub-linearly.Comment: Initially submitted to KDD 201
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 Relational Hyperlink Analysis of an Online Social Movement
In this paper we propose relational hyperlink analysis (RHA) as a distinct approach for empirical social science research into hyperlink networks on the World Wide Web. We demonstrate this approach, which employs the ideas and techniques of social network analysis (in particular, exponential random graph modeling), in a study of the hyperlinking behaviors of Australian asylum advocacy groups. We show that compared with the commonly-used hyperlink counts regression approach, relational hyperlink analysis can lead to fundamentally different conclusions about the social processes underpinning hyperlinking behavior. In particular, in trying to understand why social ties are formed, counts regressions may over-estimate the role of actor attributes in the formation of hyperlinks when endogenous, purely structural network effects are not taken into account. Our analysis involves an innovative joint use of two software programs: VOSON, for the automated retrieval and processing of considerable quantities of hyperlink data, and LPNet, for the statistical modeling of social network data. Together, VOSON and LPNet enable new and unique research into social networks in the online world, and our paper highlights the importance of complementary research tools for social science research into the web
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