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Oceanic Exchanges: Tracing Global Information Networks in Historical New spaper Repositories, 1840 -1914 (OcEx) brings together leading efforts in computational periodicals res earch from the nine academic partners in US, Mexico, Germany, the Netherlands, Finland an d the UK to examine patterns of information flow across national and linguistic boundaries in nineteenth century newspapers by linking digitized newspaper corpora currently siloed in natio nal collections. OcEx seeks to break through the conceptual, institutional, and political barriers which have limited working with big humanities data by bringing together historical newspaper experts from different countries and disciplines around common questions; by actively cro ssing the national boundaries that have previously separated digitized newspaper corpora through computat ional analysis; and by illustrating the global connectedness of nineteenth-century newspapers in way s hidden by typical national organizations of digital cultural heritage.

This project uses machine-learning algorithms to predict similarity judgments to (1) investigate which algorithms best predict similarity judgments, (2) assess which predictors are most useful in predicting similarity judgments, and (3) determine the minimum number of questions required to accurately predict similarity judgments.

Images created in the digitization of primary materials contain a wealth of machine-processable information for data mining and large-scale analysis, and this information should be leveraged both to connect researchers with the resources they need and to augment interpretation of human culture, as a complement to and extension of text-based approaches. The proposed project, "Image Analysis for Archival Discovery" (Aida), applies image processing and machine learning techniques from computer science to digitized materials to facilitate and promote archival discovery. Beginning with the automatic detection of poetic content in historic newspapers, this project will develop image processing as a methodology for humanities research and analysis. In doing so, it will advance work on two fronts: 1) it will contribute to the reevaluation of newspaper verse in American literary history; 2) it will assess the application of image analysis as a method for discovery in archival collections.

Learning objects (LOs) are important online resources for both learners and instructors and usage for LOs is growing. Automatic LO tracking collects large amounts of metadata about individual students as well as data aggregated across courses, learning objects, and other demographic characteristics (e.g. gender). The challenge becomes identifying which of the many variables derived from tracked data are useful for predicting student learning. This challenge has prompted considerable research in the field of educational data mining and learning analytics. This work advances such research in four ways. First, we bring together two approaches for finding salient variables from separate research areas: hierarchical linear modeling (HLM) from education and Lasso feature selection from computer science. Second, we show that these two approaches have complimentary and synergistic results with some variables considers salient by both and others salient by only one. Third, and most importantly, we demonstrate the benefits of a combined approach that considers a variable salient when either HLM or Lasso consider that variable salient. This combined approach both improves model predictive accuracy and finds additional variables considered salient in previous datasets on student learning. Lastly, we use the results to provide insights into the salient variables to the learning outcome in undergraduate CS education. Overall, this work suggests a combined approach that improves the identification of salient variables in big data and also improves the design of LO tracking systems for learning management systems., The file is in PDF format. If your computer does not recognize it, simply download the file and then open it with your browser.

The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.

Team-based software development projects where teams of learners design and develop software artefacts are common in computing- related degree programmes in the UK and other jurisdictions. Peer assessment is a commonly used approach to ensure learners are fairly recognised and rewarded for their contributions to such projects. This poster presents a preliminary study analysing the relationship between peer marking using the Team-Q rubric and peer feedback from one cohort using open coding and sentiment analysis. The preliminary results from a UK institutional study (N=124) illustrate how learner behaviours within teams appear to impact upon peer scores and the sentiment and intensity of emotion expressed in peer feedback. Additionally, the results provide valuable insights into common behaviours within teams. Given the prominence of team projects in computing curricula, the insights offered from this UK institutional study can shape and inform future learning, teaching and assessment practice.