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

† 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.

The use of off-the-shelf on-line materials presents several challenges. In this session panelists report on their experiences in evaluating, installing and using such materials. Both positive and negative aspects of such use are discussed. The aim of the session is to provide useful information to those considering using (and those already using) on-line materials in their teaching. The session presents information in three mini-presentations, followed by a general discussion session.

Many early adopters of educational technology report increased costs-both in technology and in faculty time. This research shows how early followers can decrease costs by using existing online supplementary materials and a redesigned course structure that increases face-to-face contact and provides multiple means for students to learn course concepts. The experience of a course team at the University of Buffalo in the redesign of a computer fluency course is related. Based on this experience, evidence of the following prerequisites for early followers' success is provided: (1) if the physical infrastructure necessary for the course does not already exist at an institution, then the cost of providing such infrastructure will reduce any cost savings; (2) it is important to have good technical support before embarking on an early follower project; (3) the existence of adequate course management software is necessary; and (4) the existence of course specific software is needed. This work can serve as a model for other early followers within the university, and can also serve as a model for faculty at other institutions who wis:1 to be early followers and create pedagogical use of technology that is scalable. (Contains 11 references.) (AEF) Reproductions supplied by EDRS are the best that can be made from the original document.

Computer vision is the process of using computers to extract from images useful information about the physical world including meaningful descriptions of physical objects (Ballard and Brown, 1982; Horn, 1986). For example, if an image sensor such as a digitizing video camera captured an image of a physical scene, and the digital image was input to a computer vision system, the desired output would be a description of the physical scene in terms that would be useful for the particular task at hand. Computer vision has many applications including robotics, industrial automation, document processing, remote sensing, navigation, microscopy, medical image analysis, and the development of visual prostheses for the blind (Fischler and Firschein, 1987).

The authors describe how a simple linear associative model with a novel learning rule is used to learn psychologically plausible shape descriptors of simple shapes such as characters, digits, and electronic circuit components. Their results show the power of teaching a neural network to associate general-purpose features with categories instead of discovering these features after trial and error. The use of general-purpose features and the proposed learning rule make it possible to teach the system to discriminate with an accuracy of 94% for digits, characters, and electronic gates with about eight training examples/character. The main advantage in using general-purpose features is the invariance to size, and the speed of recognition and learning. The recognition and learning take about a second on a Sun-3 workstation. Unlike J.A. Anderson and M. Mozer's (1981) model, this model does not overgeneralize, but learns to distinguish between distinct shapes that map on to the same abstract category. The relative importance of the features used in recognition is also discussed.<>

This paper describes general purpose algorithms for segmenting boundary images. Intersecting and incomplete boundaries commonly occur in line-art images, and also in natural images depicting translucent objects. Humans have little difficulty in segmenting such boundaries into sets corresponding to the perceptually significant regions in the image. Many existing machine vision algorithms, however, have difficulty in processing images which contain intersecting or incomplete boundaries. However, Walters' segmentation algorithm based on the p-space representation of oriented edges will correctly segment images with non-acutely intersecting lines and boundaries. This paper suggests a non-iterative, parallel algorithm which will fill large gaps and segment acutely intersecting boundaries. These algorithms are useful in a variety of applications including fake color separation, character recognition, and also in segmenting images depicting translucent objects.

Machine vision algorithms designed to model the human preattentive perception of texture boundaries often define the texture of a region on the basis of a single perceptual property. In addition, humans can segment textures even when the regions are spatially non-homogeneous in the texture properties of the primitive texture elements. This paper proposes a model of texture segmentation, and a texture segmentation algorithm based on the model, which can produce segmentations which agree with human perception.

A Perceptual Significance Hierarchy (PSH) for line art images is developed which represents the relative perceptual significance of each image component. This is possible through the use of a set of image-features which are used by the human visual system. The PSH and related rho-space computer vrsion algorithms can be used to automate the fake color separation process used by the printing industry. This is accomplished by adding rudimentary visual processing capabilities to a computer graphics system. This paper describes an application of Artificial Intelligence techniques to a pre-press problem in the printing industry, color separation. This application area is interesting as it is one where expert systems techniques are not useful, where rulebased reasoning is inappropriate, and where relational knowledge bases make no sense. Instead, AI techniques based on basic visual perceptual computations and the parallel processing of visual information are required. This can be accomplished through the use of a Perceptual Significance Hierarchy (PSH), as described below.

A Perceptual Significance Hierarchy (PSH) for line art images is developed which represents the relative perceptual significance of each image component. This is possible through the use of a set of image-features which are used by the human visual system. The PSH and related rho-space computer vIsion algorithms can be used to automate the fake color separation process used by the printing industry. This is accomplished by adding rudimentary visual processing capabilities to a computer graphics system.

A system is described for the automation of the color separation process. In current color separation systems, humans must visually segment line-art images, and using pen and ink, delineate the segments in a manner that enables a computer graphics system to be used interactively to color in each segment. The goal of this research was to remove the labor-intensive human visual segmentation, by adding rudimentary visual processing capabilities to the computer graphics system. This is possible through the use of computer vision algorithms which incorporate general knowledge about line-art, and are based on image features that are used by the human visual system in the early stages of visual processing. A major color separation company is planning the hardware implementation of a vision-graphics system based on these algorithms, and the State University of New York is applying for two patents based on this research.

An important perceptual task for both human and machine vision is to be able to interpret images in terms of distinct objects. This paper presents a technique for object interpretation in line drawings. The method is based on the use of features which have special perceptual significance for human vision. By using such features, and by devising an orientation-boundary representation, a simple, efficient algorithm can be used to interpret line drawings which can contain both straight and curved lines, and can depict any type of object.