NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Main Menu Session 3129 Course Management Systems: Expectations and Outcomes Debra T. Burhans1, Carl Alphonce2, Helene Kershner3, Barbara Sherman 4, Deborah Walters5 1 Canisius College/ 2,3,4,5University at Buffalo (SUNY) In the 1999-2000 academic year, an ambitious overhaul of a large, introductory computer literacy course at the University at Buffalo (SUNY) was initiated with the help of a grant from the Pew Learning and Technology Program1. This course enrolls approximately 1200 students per year, divided into lecture sections of 200 and laboratory sections of 20-40 students. With the help of on-line and LAN-based technologies for teaching, testing, and course management, we transformed the course from a traditional lecture format with short, unstructured laboratory meetings to a more student-centered model with fewer lectures and more structured laboratory experiences. While this course is not specifically targeted towards science and engineering students, the issues addressed herein are of interest to all educators who use or are considering using a course management system. Faculty for the course are technology-savvy computer scientists whose familiarity with computing technology influences their expectations regarding content and performance of off-the-shelf educational technology products. Specifically, expectations with regard to the course included the following: · A course management system (CMS) would decrease time spent on course administration. · Grading time would be reduced by shifting to on-line and web-based training and assessment tools. The CMS would facilitate on-line grade maintenance. · Lecture time would decrease due to the use of interactive, on-line tutorials for topics that have proven difficult to cover in lecture. These interactive materials would be integrated into the CMS. · As a result of the above-mentioned technologies, there would be a cost savings in faculty time and lab assistant expenses that could be recouped by the department (dollars) and by the faculty members involved in the course (more time for research). There were positive outcomes related to the incorporation of technology materials into the course, including a measurable increase in student learning and satisfaction as well as cost savings for the department. However, there were also a number of negative outcomes. Administrative time spent on the course increased considerably, and grading time did not decrease. In addition, troubleshooting computer-related glitches, including problems such as network bottlenecks, software performance, and inability of different pieces of software to communicate with one another, required much more faculty time than anticipated. The expectation that the CMS would allow for coordination and integration of different on-line resources was not realized. While there have been a number of studies of the impact of technology on student learning and outcomes, our focus is on the faculty perspective of teaching with technology. Our experiences using on-line materials including a CMS in a large undergraduate course have led to the identification of a number of factors that influence the effectiveness of computer-based educational technologies. These findings should prove valuable Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition Copyright Ó 2002, American Society for Engineering Education Main Menu

ARTY is a week-long program for middle school students to teach them programming of robots and allow them to express themselves artistically. It was started in 2013 and ran its fourth edition in 2016. We describe the ideas behind the inception of this program, its curriculum, our experiences during the 2016 workshop and challenges/future directions for the program. Our primary intent in this paper is to convey the program curriculum and its design, including the way in which robots can be viewed as vehicles for artistic expression. Some results from a brief attitudinal survey that was administered before and after the workshop are also included along with a discussion of outcomes assessment and issues.

We report on Shallow Blue (SB), an autonomous chess agent constructed by a small group of faculty and undergraduate students at Canisius College. In addition to pushing the limits of consumer grade components at low cost, SB is a focal point for interdisciplinary student projects spanning computer science, engineering, and physics. We demonstrate that undergraduate students can engage in rich, long-term robotic design and applied Artificial Intelligence (AI) from both hardware and software perspectives. Student outcomes of SB include senior theses, conference presentations, peer-reviewed publications, and admission to graduate programs. Students who participated also report substantial development in skills and knowledge applicable to their post-undergraduate education and careers.

We report on Shallow Blue (SB), an autonomous chess- playing agent designed on a limited budget with off-the-shelf consumer components. The system combines vision, piece manipulation, and decision-making functions to allow autonomous play against humans. SB demonstrates the increasing sophistication available to educators and students with consumer grade hardware, as well as the growth of opportunities for integrated software and hardware design by advanced undergraduate students. The paper documents the design, implementation, performance, and ongoing development of SB and its component systems.

The design and implementation of a robotic chess agent is described. Shallow Blue, a competitor in the AAAI 2011 Small Scale Manipulation Challenge, is constructed with low-cost components including Lego NXT bricks and is programmed using Java and Lejos.

This paper describes a new undergraduate course that serves two purposes. First, it satisfies a general education requirement in mathematical sciences, and second, it serves as one of four possible first courses for computer science majors. The course has no prerequisites: the student population is drawn primarily from college freshmen. This paper focuses on the curriculum, which blends topics from basic computing, artificial intelligence, and robotics. Results of a class survey are presented and discussed. Overall, satisfaction with both the course and the use of robots was high.

The Fifteenth Annual AAAI Robot Competition and Exhibition was held at the National Conference on Artificial Intelligence in Boston, Massachusetts, in July 2006. This article describes the events that were held at the conference, including the Scavenger Hunt, HumanRobot Interaction, and Robot Exhibition.

The LEGO Mindstorms robots provide an excellent introductory platform for students to explore computer programming and robotics. However, a major drawback is students’ lack of access to robots outside of lab. Our system includes a 2-D graphical simulator and a translator for a simple block-structured language we call Robolang that produces Lejos code. Students can program directly in either Robolang or Lejos, so the system has a longer curricular lifespan, allowing beginners to start with a simpler language and progress to a “real” language, all the while using the simulator to test their programs before downloading to a real robot. While robotics helps introduce AI to students in introductory courses, Robolang can also be used in an AI course to allow students to quickly develop interesting reactive robot agents using the LEGO platform.

Resolution theorem proving provides a useful paradigm for the exploration of question answering. A partition of the clauses generated during resolution refutation based on their syntactic structure is presented. The three classes comprising this partition correspond to semantically intuitive types of answers. This work encompasses and expands upon previous work on question answering in a theorem proving paradigm, which began with the association of answers with proofs. A complete, formal definition of what is meant by answer in the context of resolution theorem proving is presented. In this context, clauses that are relevant are all identified as answers, where relevance is determined with respect to a question and knowledge base: any clause descended from the clause form of a negated question is deemed relevant. This definition of relevance is not in and of itself novel; rather, it is the way in which the set of relevant clauses is partitioned that provides the key to interpreting clauses as answers. The three answer classes identified are: specific, generic, and hypothetical. These classes are formally distinguished by the way in which literals in a clause share variables, with class membership based on a property termed the closure of variable sharing of a literal. The results presented provide a foundation for further work by establishing a context-independent logical pragmatics of question answering.

Starting in the fall of 2005 our department began a large-scale effort to incorporate hands-on robotics in many of our courses, including our introductory computer science sequence. This followed the establishment of a departmental robotics laboratory and the purchase of a number of different robots. Our efforts include testing a variety of projects and surveying students about their satisfaction with robots. A primary contribution is our development of an algorithmic programming environment for robotics (Robotran) and an associated robot simulator.

The snarpy cognitive robotics architecture is a new member of the family of GLAIR architectures that performs grounded reasoning and action on real and simulated robots. We employ the SNePS knowledge representation, reasoning, and acting system and the Pyro robotics toolkit. We describe the architecture together with the methodologies used to realize it. With the snarpy architecture, robotic agents can be very easily and quickly constructed having both high-level reasoning skills and low-level acting and sensing abilities on real robotics hardware. Two prototype snarpy agents are presented.

In the spring of 2005 our department offered a new course entitled “Intelligent Systems” (IS). This course, which will be required for future CS majors, provides an introduction to a number of AI topics including predicate logic, frames, rule-based systems, neural networks, Bayesian networks, decision trees, and fuzzy logic. The theme of agents is woven throughout the course. Each section of the course employs a different simulation tool or language so that students can apply and experiment with the techniques they learn about in the classroom. The culminating project for the course was a two week project where students were given a basic Lego MINDSTORMS robot to experiment with. Their task was to program the robot for a race to be held on the final day of class.

A simple vacuum-cleaner agent is introduced in Russell and Norvig’s artificial intelligence (AI) text [Russell and Norvig, 2003] to illustrate different agent types to beginning AI students. Underlying the different agent types are several simple actions that take place in a vacuum cleaner world consisting of a grid of squares, some of which contain dirt. The agent’s actions include turning left or right, moving forward, and picking up dirt. Having students write a program to simulate the vacuum world is a useful way to provide them with a feeling for different agent types in a simplified environment. However, implementing a vacuum-cleaner agent using a low-cost robotics kit might teach students much more about agents in the real world and could serve to get them interested in and excited about AI in a way that working with purely simulated environments may not. This paper describes the design and implementation of “Dustbot”, a robot based on the Russell and Norvig vacuum-cleaner agent. The Dustbot project was carried out as part of an independent study by a student who had already taken the undergraduate AI course. The purpose of the project was to test and debug the vacuumcleaner robot and to develop a set of instructions that could be used in subsequent offerings of our undergraduate AI course.

The successful implementation of educational programs in bioinformatics presents many challenges. The interdisciplinary nature of bioinformatics requires close cooperation between computer scientists and biologists despite inescapable differences in the ways in which members of these professions think. It is clear that the development of quality curricula for bioinformatics must draw upon the expertise of both disciplines. In addition, biologists and computer scientists can benefit from opportunities to carry out interdisciplinary research with one another. This paper examines the role of computer science in undergraduate bioinformatics education from the perspectives of two bioinformatics program directors. Their respective programs exemplify two substantively different approaches to undergraduate education in bioinformatics due to the fact that they are at markedly different institutions. One institution is a large, technical university, offering both undergraduate and graduate degrees in bioinformatics while the other is a small, Jesuit liberal arts college with an undergraduate program in bioinformatics. Despite these differences there is considerable overlap with respect to the role of computer science. This paper discusses the ways in which computer science has been integrated into these two undergraduate bioinformatics programs, compares alternative approaches, and presents some of the inherent challenges.

Biology has become an increasingly data-driven science. Modern experimental techniques, including automated DNA sequencing, gene expression micro arrays, and X-ray crystallography are producing molecular data at a rate that has made traditional data analysis methods impractical. Computational methods are becoming an increasingly important aspect of the evaluation and analysis of experimental data in molecular biology. Bioinformatics is the term coined for the new field that merges biology and computer science to manage and analyze this data, with the ultimate goal of understanding and modeling living systems [1].

There is considerable effort being devoted to mining information from medical and scientific literature, in particular, from Medline abstracts and from full-text articles. Such information is being used, for example, to reconstruct biological pathways, identify pathogenic mechanisms and, importantly, to identify functional relationships that can be used to predict disease onset and its course thereafter. Our interest is in exploring the role of knowledge representation and reasoning (KR&R) as it relates to the problem of understanding biomedical text. The role we envision for a KR&R system in this context is as a knowledge store for a small, focused subset of abstracts gleaned from the Medline corpus that is relevant to a problem of interest. The system will infer new knowledge from the represented abstracts which can then be stored in a larger data repository and reported to a biologist. We are specifically interested in designing a system that, given a set of abstracts, can perform many of the same inferences that a biology expert would make if given the same set of abstracts. Inferences that go beyond the predictions of the biologist are particularly interesting, but our initial goal is to emulate the biologist. We have selected the disease neurofibromatosis type 1 (NF1) for our study with the goal of developing a model that can be applied to reasoning about other diseases and problems of interest. This approach is focused narrowly on a particular problem and as such may not lead to solutions relevant for general problem solving. However, we believe there is an important role for specialized problem solvers in the larger context of biomedical text understanding. Working closely with a domain expert in biology is providing valuable insights into how the computational synthesis of information might best serve the needs of a biologist. This preliminary report describes our current work on hand analysis and translation of abstracts and our proposed overall approach to the problem.

In this paper we are specifically interested in the relationship between atural language and the knowledge representation (KR) formalism referred to as ontology. By ontology we mean a taxonomic, hierarchical data structure. The reason we use natural language for terms in ontologies is so that we humans can understand the ontologies. Machines and humans who have to understand ontologies interpret like terms in a given natural language similarly, though their interpretations may not completely coincide. Human understanding of natural language terms involves premise smuggling, by which we mean the unconscious or implicit use of background knowledge and context in the process of interpretation. Machine understanding of ontological terms is explicit, and is based on their positions in an ontology. While two machine ontologies may contain similar terms, they may not be completely homologous. Ontological terms are interpreted beyond the control of the designers of ontologies, by both humans and machines. When atural language is used for ontological terms, the interpretation of these terms by humans is complex and idiosyncratic. In this paper we discuss some of the problems and issues involved in human and machine understanding of natural language ontological terms.

† Department of Computer Science and Engineering, University at Buffalo, SUNY ‡ Department of Computer Science, Canisius College Abstract  Educators wishing to use e-learning materials must choose between homegrown solutions and off-the-shelf materials. Each choice has distinct benefits and drawbacks. Handcrafted materials are often expensive to develop and maintain, yet their content will likely better meet an educator's requirements than a commercial product, and carries with it the potential to become marketable in the future. While the expense associated with commercial products is borne by their developers and the students who purchase the products, the content of such products is less likely to match educator requirements or expectations.

This paper presents the results of a redesign of a large enrollment computer fluency course for non computer science majors. The goals of the redesign were to increase learning and to decrease costs using technology. The paper discusses the motivations and the components of the course redesign. The results show that costs did decrease and that learning did increase in the redesigned course.