This allows for supporting more adequate and accurate representations of learners, their learning goals, learning material and contexts of its use, as well as more efficient access and navigation through learning resources. The goal is to advance intelligent educational systems, so as to achieve improved e-learning efficiency, flexibility and adaptation for single users and communities of users (learners, instructors, courseware authors, and others). The special track follows the workshop series Ontologies and Semantic Web for e-Learning, which was conducted successfully from 2002-2009 at a number of different conferences. The goals of this track are to discuss the current state-of-the-art in using ontologies and semantic web technologies in e-learning applications; and to attract the interest of the related research communities to the problems in the educational social semantic web and serve as an international platform for knowledge exchange and cooperation between researchers. This special track will be of interest to researchers interested in using ontologies, semantic web and social semantic web technologies in web-based educational systems, distributed hypermedia and open hypermedia systems, as well as in web intelligence and semantic web and social semantic web engineering.

Learners may develop expertise by experiencing numerous different but relevant situations. Computer games and virtual simulations can facilitate these training opportunities, however, because of the relative difficulty in authoring new scenarios, the increasing need for new and different scenarios becomes a bottleneck in the learning process. Furthermore, a one-size-fits-all scenario may not address all of the abilities, needs, or goals of a particular learner. To address these issues we present a novel technique, Automated Scenario Adaptation, to automatically “rewrite” narrative scenario content to suit individual learners’ needs and abilities and to incorporate recent changes from real world learning needs. Scenario adaptation acts as problem generation for intelligent tutoring systems, producing greater learning opportunities that facilitate engagement and continued learner involvement.

The mission of the ARTSI (Advancing Robotics Technology for Societal Impact) Alliance, a consortium of 19 Historically Black Colleges and Universities (HBCUs) and 9 major research universities (R1s), is to enlarge the nation’s engineering and science talent pool by increasing the number of students from underrepresented groups who pursue advanced training in computer science. ARTSI is one of several alliances funded by the National Science Foundation’s Broadening Participation in Computing Program. ARTSI focuses specifically on institutions serving African Americans and uses robotics education to attract and engage students. In this paper we describe the activities comprising ARTSI, our vision of a robotics curriculum for CS undergraduates, and ways to integrate robotics modules into existing CS courses.

Creating natural language tutorial dialogue systems that realize effective strategies is a central challenge for intelligent tutoring systems research. Traditional approaches generally require large development time, do not generalize well across domains, and do not match the flexibility and natural language sophistication of human tutors. A promising approach that may offer several benefits is data-driven system development, in which a dialogue policy is learned from corpora of human tutorial dialogue. To date these learning approaches typically focus on optimizing the tutor’s choice of act, and do not explicitly model the instances in which the tutor chose not to act. This paper reports on a hidden Markov modeling (HMM) approach within human textual tutorial dialogue that explicitly represents the tutors’ choices not to intervene. The results show that an HMM that models tutor non-interventions predicts tutor moves significantly better than a model that does not explicitly represent the non-interventions. The findings have implications for automatically modeling tutorial strategies and for learning dialogue policies from corpora.

Teaching an upper-level undergraduate robotics course at Historically Black Colleges and Universities (HBCUs) is challenging. The lack of suitable teaching materials is one of the biggest challenges, although there are many great masterpieces in developing robotics course materials, which are, however, generally developed for teaching students at elite Research I universities. This paper presents ideas and details in adopting and revising these course materials and creating upper-level undergraduate robotics course materials that are suitable for underrepresented students.

In this paper, we show how to make a cognitive tutoring agent capable of precise causal reasoning by integrating constraints with data mining algorithms. Putting constraints on recorded interactions between the agent and learners during learning activities allows data mining algorithms to extract the causes of the learners’ problems. Subsequently, the agent uses this information to provide useful and customized explanations to learners.

Under the Defense Advanced Research Project Agency's (DARPA) Integrated Crisis Early Warning System (ICEWS), Innovative Decisions, Inc. (IDI) constructed a Bayesian network to combine forecasts produced by a set of social science models. We used Bayesian network structure learning with political science variables to produce meaningful priors. We employed a naive Bayes structure to aggregate the forecasts. In both cases, IDI improved classification by intelligently discretizing continuous variables. The resulting network not only met performance criteria set by DARPA, but also out-performed each of the social science models across all types of forecasted events. We describe the construction of the aggregator as well as a set of experiments performed to explore the nature of the Bayesian EOI Aggregator's performance.

Challenges for Computing include Learning for Life (Taylor et al, 2008). Grand Research Challenges in Information Systems identifies the need to "provide a teacher for These are amongst the key challenges that AIED responds to. What will next generation AIED learning environments be like? GROE report (Woolf, 2010), in order to highlight the expected role of AIED research.

This book presents a methodology and philosophy of empirical science based on large scale lossless data compression. In this view a theory is scientific if it can be used to build a data compression program, and it is valuable if it can compress a standard benchmark database to a small size, taking into account the length of the compressor itself. This methodology therefore includes an Occam principle as well as a solution to the problem of demarcation. Because of the fundamental difficulty of lossless compression, this type of research must be empirical in nature: compression can only be achieved by discovering and characterizing empirical regularities in the data. Because of this, the philosophy provides a way to reformulate fields such as computer vision and computational linguistics as empirical sciences: the former by attempting to compress databases of natural images, the latter by attempting to compress large text databases. The book argues that the rigor and objectivity of the compression principle should set the stage for systematic progress in these fields. The argument is especially strong in the context of computer vision, which is plagued by chronic problems of evaluation. The book also considers the field of machine learning. Here the traditional approach requires that the models proposed to solve learning problems be extremely simple, in order to avoid overfitting. However, the world may contain intrinsically complex phenomena, which would require complex models to understand. The compression philosophy can justify complex models because of the large quantity of data being modeled (if the target database is 100 Gb, it is easy to justify a 10 Mb model). The complex models and abstractions learned on the basis of the raw data (images, language, etc) can then be reused to solve any specific learning problem, such as face recognition or machine translation.

The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems.