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.

Conference and special track committees responsible for the 2009 FLAIRS conference.

LEGO Mindstorms robots are very popular with colleges and universities for teaching computer concepts and programming.  These robots elicit excitement in students and provide a nontrivial, real-world platform for exploring algorithmic concepts.  We created a simple algorithmic language, called Robolang, and wrote a translator that turns it into Lejos code, a variant of Java that can be run on the RCX version of the LEGO Mindstorms robots.  Seeing that students were eager to explore programming with the RCX robots at home, we wrote a graphical simulator to visualize actions of our penbot, a configuration of the RCX robot that we used in most assignments.  Using an emulator approach, we intercept the ROM calls to the RCX's hardware made by the TinyVM, the stripped-down Java Virtual Machine that runs compiled Java bytecodes.  Our system then forwards these calls to a software model that represents the actual robot hardware.  The software model creates the graphics to mimic the penbot using Java2D. This approach greatly simplified coding by capitalizing on existing software, namely the Java compiler and the JVM.  Students can program either in Robolang or in actual Lejos and use the simulator to visualize the actions of the robot acting as a sort of visual debugger.

This study evaluates the robustness of established computational indices used to assess text relatedness in user-language. The original User-Language Paraphrase Corpus (ULPC) was compared to a corrected version, in which each paraphrase was corrected for typographical and grammatical errors. Error correction significantly affected values for each of five computational indices, indicating greater similarity of the target sentence to the corrected paraphrase than to the original paraphrase. Moreover, misspelled target words accounted for a large proportion of the differences. This study also evaluated potential effects on correlations between computational indices and human ratings of paraphrases. The corrections did not yield assessments that were any more or less comparable to trained human raters than were the original paraphrases containing typographical or grammatical errors. The results suggest that although correcting for errors may optimize certain computational indices, the corrections are not necessary for comparing the indices to expert ratings.

Providing the first few letters of a missing word in a sentence gives information about this word. This paper attempts to measure the information transmitted in that case. In order to do so, we analyzed response accuracy for open cloze questions, that is fill-in-the-blank questions without multiple choice answers. In this study, native and non-native speakers of English answered a series of open cloze questions that were semi-automatically generated. Hints were provided that consisted of the first few letters of the missing word. Results showed that question difficulty, hence the quantity of information transmitted, is related to the number of letters that are provided, to physical properties of these letters and to syllables formed by these letters. Performances did not appear to depend on letter or syllable frequency. Controlling hint level in a word completion task is critical in order to provide practice exercises adapted to student levels.

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.

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.

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.

Students must be able to competently compose essays in order to succeed in school and progress into the workplace. Current intelligent tutoring systems (ITS) attempt to provide individual training that is lacking in the current educational system. To provide efficient individual training through ITS, the systems must be able to effectively assess writing input from students. Necessary components for computer-based writing tutors are algorithms that mimic human judgments of writing. The current study attempts to establish a connection between paragraph position and human ratings of paragraph type through the use of computational measures provided by Coh-Metrix. We find that expert raters do not easily identify paragraph type and ratings of paragraph type do not map onto paragraph position.

Our past work has investigated the use of the Cognitive Model Software Development Kit (SDK) for creating the cognitive models that underlie model-tracing Cognitive Tutors. Though successful at increasing the number of people who could author such a cognitive model, for certain kinds of situations the Cognitive Model SDK proved cumbersome. The present work discusses a new authoring system, xPST, that allows an example-based tutor to be built on top of existing software.