Tekkotsu is an open source application development framework for intelligent mobile robots. Originally designed for undergraduate computer science majors, recent refinements to the framework have led us to explore its use with high school students. We developed a pilot course curriculum to introduce high level robotics to students with little or no programming experience in a way that provides improved feedback and error detection on multiple levels. The use of visualization tools and pair programming techniques scaffolds the learning process and provides a systematic way to introduce robotics as a fun and worthwhile endeavor to novices, and helps instructors efficiently address students’ concerns in a real-time manner.

In this paper we present Myro-C++, developed at the University of Tennessee. Myro-C++ is a C++ port ofthe Python Myro library that was written by the Institute for Personal Robots in Education (IPRE) at Georgia Tech and Bryn Mawr College. Myro-C++ is publicly available, open source software, released under the GPLv3 open source license. At the time of writing, the library has been used six semesters for the CS1 courseat the University of Tennessee, Knoxville. The library contains functions for control of the robot and access to sensor information, and provides the ability to display the live camera image from the robot into a video window. This library is used as a teaching tool in our CS1 course where students learn basic programming fundamentals using multiple artificial intelligence based labs. In addition to the software, the IPRE book, Learning Computing with Robots, has been edited to use C++ examples and explanations, and is freely available. We also present example programs that we use as laboratory assignments in our Introduction to Computer Science course, which are also freely available.

The call for papers were Yutao Wang and Neil Heffernan for "The attracted 179 submissions, across 13 different'Assistance' Model: Leveraging How Many tracks. Special tracks are a vital part of the Hints and Attempts a Student Needs," a submission FLAIRS conferences, with 12 held at FLAIRSto the Special Track on Intelligent Tutoring 24. Over 90 percent of the papers were reviewed Systems; Simon Delamarre for "The Utility of by four or more reviewers, and all papers were Combinatory Categorical Grammar in Designing reviewed by at least three. These reviews were a Pedagogical Tool for Teaching Languages," coordinated by the program committees of the a submission to the Special Track on Computation general conference and the special tracks. The Linguistics; and Rachel M. Rufenacht, accepted submissions include 94 papers and 37 Philip M. McCarthy, and Travis A. Lamkin for poster papers that appear in these proceedings.

We describe a four-week series of assignments in an undergraduate AI course at a liberal arts college developing a supervised learning solution to the problem of classifying Twitter accounts as either a person account or a non-person account (e.g. organization or spambot). This problem employs real data in an ongoing research project by the first author, yet is accessible to students with limited programming expertise.The students were able to experience a complete cycle of creating a machine learning solution: exploring raw data,creating a training set, engineering features, comparing different classifiers, evaluating the results, and performing erroranalysis. We received positive feedback from the students and intend to refine the assignment and make it available (together with the created training data) for use by the research community.

The primary goal of this study is to assess two approaches for detecting comprehension processes in R-SAT (Reading Strategy Assessment Tool). One approach is based on Latent Semantic Analysis (LSA) while the other is a combination of literal word matching and soundex. A secondary goal is to assess the potential for detecting specific reading comprehension strategies, either in isolation or combination. Participants typed “think-aloud” protocols while reading texts presented on computers. Human judges rated these protocols for the presence of the various reading comprehension strategies. LSA, word, and combined algorithms were compared and the results showed that a combination of both approaches yielded the best results. However, performance of the combined algorithm varied in terms of the type of processes and the grain size of the human coding system. Lastly, the use of reading strategies (either in isolation or combination) is positivity related to students’ Gates–MacGinitie reading comprehension scores, which illustrates the merit of this approach for assessing comprehension skill.

An important aspect of Intelligent Tutoring Systems is providing assistance to students as well as assessing them. The standard state-of-the-art algorithms (Knowledge Tracing and Performance Factor Analysis) for tracking student knowledge, however, only look at the correctness of student first response and ignore the amount of assistance students needed to eventually answer the question correctly. In this paper, we propose the Assistance Model (AM) for predicting student performance using information about the number of hints and attempts a student needed to answer the previous question. We built ensemble models that combine the state-of-the-art algorithms and the Assistance Model together to see if the Assistance Model brings improvements. We used an ASSISTments dataset of 200 students answering a total of 4,142 questions generated from 207 question templates. Our results showed that the Assistance Model did in fact reliably increase predictive accuracy when combined with the state-of-the-art algorithms.

This paper intends to demonstrate how Applicative and Combinatory Categorial Grammar (ACCG) can be drawn on to design powerful software applications for the teaching of languages. To this end, we present some modules from our “pictographic translator”, a software that performs syntactical analysis of sentences in natural language directly written by the user, and then dynamically displays series of pictograms that illustrate the words and structure of the user’s sentences. After a short presentation of our application and an introduction to ACCG, we will examine how this formalism enables the building of several high-level functions in our system, such as disambiguation, structure exhibition and grammatical correction/validation. We finally open a short discussion concerning the potential (and limits) of this architecture with regards to multilingualism.

It is widely acknowledged that one-on-one human tutoring is one of the most effective ways to provide learning, however, the source of its effectiveness is still unclear. Tutor-centered, student-centered, and interaction hypotheses have been proposed as possible explanations of the effectiveness of human tutoring. Most research has addressed this question by analyzing tutorial sessions at the dialogue move or speech act level. The present paper adopts a different approach by focusing on word usage patterns in 50 naturalistic tutorial sessions between human students and expert tutors. Specifically, each unique word in the session was designated as a student initiative word, a tutor initiative word, or a shared-initiative word. Comparisons of the frequencies as well as the weights of the words assigned to each of these categories indicated that the student and tutor share initiative even though the tutor’s are considerably more verbose. The implications of the results for the development of an ITS that aspires to model expert tutors are discussed.

List of the organizers and conference committees for FLAIRS-24, including special tracks.

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.