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

Organizational Information Dissemination Within Collaborative Networks Using Digital Communication Tools

While knowledge is one of an organization’s greatest assets, it remains a challenge to facilitate knowledge transfer between people within an organization. Social influence has been studied in its role of facilitating information diffusion, which is necessary for knowledge transfer to occur. Among this research, tie strength, a quantifiable characteristic of a social network that determines the link between two nodes, has been measured to determine the impact of social influence on knowledge transfer and information dissemination within a social network. Current research that explores the impact of social influence on information diffusion has been conducted within public social networks due to the availability of data that can be gathered from public social online network systems, such as Facebook. With the emergence of collaboration technologies that exist in online social network tools being utilized within organizations, there is an opportunity to digitally collect information regarding information dissemination within a collaborative network. This study captured data from an online social network, specifically a unified communication tool, being used within a collaborative social network at a mid-sized South Central corporation. A content analysis of Lync messages for 1,749 connections was performed to quantitatively measure the influence of tie strength on information dissemination within a collaborative social network. The results demonstrated that tie strength had a significant impact on information dissemination using a collaborative system. Multivariate analysis of variance showed that tie strength had the largest impact on information dissemination using the instant messaging modality of a collaboration system

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems

Early-Childhood Computer-based Testing: Effects of a Digital Literacy Intervention on Student Confidence and Performance

Early-childhood digital students grow up in a fast-evolving age of technology requiring them to use and create with technologies and demonstrate core content knowledge. Although third grade students are mandated to master a new language of standardized testing, a large percentage must also learn a language of technology to complete new computer-based tests to measure content mastery. Krashen (1982) defines high affective filter as negative emotional/motivational factors interfering with understanding and cognition. This high affective filter reduces confidence and negatively impacts measuring content mastery on new computerbased tests. Two third grade classrooms at a high-poverty metropolitan school participated in a quasi-experimental study to measure the effects of a digital literacy intervention on computerbased testing confidence and student performance in social studies and mathematics. The intervention group participated in a digital literacy intervention developing keyboarding and coding skills. The control group participated in a mock digital intervention. Both participant groups received computer-based pretests and posttests in social studies and mathematics, and both groups completed Technology-Use Baseline and computer-based testing (CBT) confidence surveys after each pretest and posttest. A comparison of means was used to analyze change between pretest and posttest. Regression analysis and ANOVA were used to determine any v significant relationships between CBT-Confidence, student performance and digital literacy intervention variables. The study results found a significant relationship with a change in student performance and computer-based testing confidence in social studies but not mathematics. There was also a direct, positive significant relationship with the coding intervention and change in computerbased testing confidence in social studies but not mathematics. The researcher suggests that mode of technology integration within the two classrooms impacted the research study. The research study suggests that learner-centered technology integration within the social studies classroom positively impacted the research study when comparing the teacher-centered technology integration within the mathematics classroom. Research study suggests that school leaders consider providing teacher professional development opportunities for learner-centered technology integration (Chow et al., 2012, Considine et al., 2009). Future research could include larger sample population, using the same teacher to teach both subjects, and implementing longitudinal study to track student performance on standardized testing

Building a Strategic Learning and Evaluation System for Your Organization

The current state of evaluation in the philanthropic and nonprofit sectors points to the need for a more strategic approach to evaluation. In this guide, we provide a framework and set of practices that can help organizations be more systematic, coordinated, and intentional about what to evaluate, when, why, with whom, and with what resources