We studied the effect of post-practice reflection on learning, using programming tutors, and multiple-choice format for reflection. We conducted in-vivo controlled studies with introductory programming students from multiple schools over 3 semesters, and used mixed-factor ANOVA to analyze the collected data. We found that reflecting on the concept underlying each problem neither promotes greater learning, measured as pre-post increase in the average score per problem, nor promotes faster learning, measured as the problems solved per concept learned. We conjecture that the benefits of reflecting on the concept underlying each problem may be limited if a tutor already promotes deep understanding of the domain.

Generating a schedule for a professional sports league is an extremely demanding task. Good schedules have many benefits for the league, for example higher incomes, lower costs and more interesting and fairer seasons. This paper presents a successful solution method to schedule the Finnish 1st division ice hockey league. The solution method is an improved version of the method used to schedule the Finnish major ice hockey league. The method is a combination of local search heuristics and evolutionary methods. An analyzer for the quality of the produced schedules will be introduced. Finally, we propose a set of test instances that we hope the researchers of the sports scheduling problems would adopt. The generated schedule for the Finnish 1st division ice hockey league is currently in use for the season 2008-2009.

Using the already validated Coh-Metrix tool, this study examines whether there are significant linguistic and discourse differences between biomedical abstracts for American and Korean English. Also, the current study accounts for variation among journals’ countries of origin, distinguishing between biomedical journals published in the United States from biomedical journals published in South Korea. The significance of these studies regards the growing number of second language (L2) biomedical researchers attempting to publish their research in national and international English-language journals, but who find themselves locked out of the discussion because of differences in linguistic and discourse conventions. The present study aims to provide a more thorough and quantitative understanding of the prototypical linguistic components in biomedical rhetoric, and to suggest how word-, sentence-, and discourse-level structures can be researched, taught, and developed into materials. This improved understanding is expected to provide a powerful apparatus for the promotion of L2 English writers in the biomedical field.

The field of artificial intelligence needs to attract new researchers to the field to continue current explorations and look for novel approaches to tomorrow's problems. One approach involves providing students with learning tools that excite their imagination and help them obtain an appreciation for what artificial intelligence can do. The tools described here are used in an undergraduate course at Sam Houston State University. They include heuristic-driven search in a potential game's terrain map, reinforcement learning in a tank battle game, and game tree search techniques in tic-tac-toe.

Ensuring the consistency and completeness of Semantic Web ontologies is practically impossible, because of their scale and highly dynamic nature. Many web applications, therefore, must deal with vague, incomplete and even inconsistent knowledge. Rules were shown to be very effective in processing such knowledge, and future web services are expected to depend heavily on them. RuleML, which is the earliest effort to define a normalized markup for representing and exchanging rules on the web, is currently limited to Horn rules. Significant research efforts are underway to extend RuleML with more flexible representation and reasoning capabilities. This paper presents an extension of the current rule format intended to accommodate uncertain and/or inconsistent knowledge, and shows how one truth maintenance logic can be adapted and extended to support such rules.

Computer Science has a bad reputation among non-CS majors. This paper describes three assignments from a gentle introduction to programming course for non-majors that uses robots and simple game programming as a hook to get students interested in the subject. In each of the assignments presented, what might be considered a trivial twist to an instructor was a key factor in making an otherwise standard project into something that is more engaging.

Using robots as part of any curriculum requires careful management of the significant complexity that physical embodiment introduces. Students need to be made aware of this complexity without being overwhelmed by it, and navigating students through this complexity is the biggest challenge faced by an instructor.  Achieving this requires a framework that allows complexity to be introduced in stages, as students' abilities improve. Such a framework should also be flexible enough to provide a range of application environments that can grow with student  sophistication, and be able to quickly change between applications.  It should be portable and maintainable, and require a minimum of overhead to manage in a classroom. Finally, the framework should provide repeatability and control for evaluating the students' work, as well as for performing research. In this paper, we discuss the advantages of a mixed reality approach to applying robotics to education in order to accomplish these challenges.  We introduce a framework for managing mixed reality in the classroom, and discuss our experiences with using this framework for teaching robotics and AI.

A neural net with multiple output nodes is capable of distinguishing among a set of related input classes even in the absence of training. It can do so with an accuracy that is markedly better than random guessing. This is because each class will tend to activate a different set of output nodes. We refer to this tendency as the net's 'inherent' bias. Ascertaining a net's inherent bias  may be thought of as learning the net. One may learn the net either instead of training it, or prior to training it. Furthermore, one only needs a small number of samples from each input class in order to reliably learn the net. If a net has been previously trained on a different, related set of classes, then ascertaining the inherent bias is a form of knowledge transfer. When such a net is trained to respond in accordance with its inherent bias, one may obtain substantially higher accuracies than is provided by nets trained in the standard fashion. Furthermore, when using a deep net, we were able to obtain such improvements while only allowing the top layer of the net to train. This layer contained only about 5.7% of the net's free parameters.

We propose a new approach for Temporal Diagnosis Problems. This approach is an extension of  Bouzid and Ligeza's  method for temporal diagnosis problems. In this latter work, the authors define a Temporal Causal Graph (TCG) where time delays are expressed as temporal instants. We extend the TCG by including two quantitative relations in order to handle temporal intervals. We call ExTCG this new model. Solving a temporal diagnosis problem represented by the ExTCG consists of finding all possible explanations. It is performed using a backtrack search algorithm. In many diagnosis applications, the generation of all possible explanations is not necessary. For this reason, we augment the ExTCG in order to consider the degree of causality between symptoms. We call weighted ExTCG this extended model. Solving it consists of finding the explanation  having the highest probability to occur. Through a real world diagnosis application in medicine, we illustrate the weighted ExTCG and its corresponding solving algorithm.

Emotions are a ubiquitous component of motivation and learning. We have developed an affective behavior model for intelligent tutoring systems that considers both the affective and knowledge state of the student to generate tutorial actions. The affective behavior model (ABM) was designed based on teachers' expertise obtained through interviews. It relies on a dynamic decision network with a utility measure on both student learning and affect to generate tutorial actions aimed at balancing the two. We have integrated and evaluated the ABM in an educational game to learn number factorization. We carried out a controlled user study to evaluate the impact of the affective model on learning. The results show that for the younger students there is a significant improvement on learning when the affective behavior model is incorporated.