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

From Manifesta to Krypta: The Relevance of Categories for Trusting Others

In this paper we consider the special abilities needed by agents for assessing trust based on inference and reasoning. We analyze the case in which it is possible to infer trust towards unknown counterparts by reasoning on abstract classes or categories of agents shaped in a concrete application domain. We present a scenario of interacting agents providing a computational model implementing different strategies to assess trust. Assuming a medical domain, categories, including both competencies and dispositions of possible trustees, are exploited to infer trust towards possibly unknown counterparts. The proposed approach for the cognitive assessment of trust relies on agents' abilities to analyze heterogeneous information sources along different dimensions. Trust is inferred based on specific observable properties (Manifesta), namely explicitly readable signals indicating internal features (Krypta) regulating agents' behavior and effectiveness on specific tasks. Simulative experiments evaluate the performance of trusting agents adopting different strategies to delegate tasks to possibly unknown trustees, while experimental results show the relevance of this kind of cognitive ability in the case of open Multi Agent Systems

Beyond the Bubble: Technology and the Future of Student Assessment

Provides an overview of information technology's potential to enable better assessments of student achievement. Outlines promising models for testing complex skills, cognition, and learning and for utilizing such assessments to improve instruction

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Detecting Communities and Analysing Interactions with Learning Objects in Online Learning Repositories

The widespread use of online learning object repositories has raised the need of studies that assess the quality of their contents, and their user’s performance and engagement. The present research addresses two fundamental problems that are central to that need: the need to explore user interaction with these repositories and the detection of emergent communities of users. The current dissertation approaches those directions through investigating and mining the Khan Academy repository as a free, open access, popular online learning repository addressing a wide content scope. It includes large numbers of different learning objects such as instructional videos, articles, and exercises. In addition to a large number of users. Data was collected using the repository’s public application programming interfaces combined with Web scraping techniques to gather data and user interactions. Different research activities were carried out to generate useful insights out of the gathered data. We conducted descriptive analysis to investigate the learning repository and its core features such as growth rate, popularity, and geographical distribution. A number of statistical and quantitative analysis were applied to examine the relation between the users’ interactions and different metrics related to the use of learning objects in a step to assess the users’ behaviour. We also used different Social Network Analysis (SNA) techniques on a network graph built from a large number of user interactions. The resulting network consisted of more than 3 million interactions distributed across more than 300,000 users. The type of those interactions is questions and answers posted on Khan Academy’s instructional videos (more than 10,000 video). In order to analyse this graph and explore the social network structure, we studied two different community detection algorithms to identify the learning interactions communities emerged in Khan Academy then we compared between their effectiveness. After that, we applied different SNA measures including modularity, density, clustering coefficients and different centrality measures in order to assess the users’ behaviour patterns and their presence. Using descriptive analysis, we discovered many characteristics and features of the repository. We found that the number of learning objects in Khan Academy’s repository grows linearly over time, more than 50% of the users do not complete the watched videos, and we found that the average duration for video lessons 5 to 10 minutes which aligns with the recommended duration in literature. By applying community detection techniques and social network analysis, we managed to identify learning communities in Khan Academy’s network. The size distribution of those communities found to follow the power-law distribution which is the case of many real-world networks. Those learning communities are related to more than one domain which means the users are active and interacting across domains. Different centrality measures we applied to focus on the most influential players in those communities. Despite the popularity of online learning repositories and their wide use, the structure of the emerged learning communities and their social networks remain largely unexplored. Our findings could be considered initial insights that may help researchers and educators in better understanding online learning repositories, the learning process inside those repositories, and learner behaviou