Lois Wright Hawkes

On nearness measures in fuzzy relational data models

Abstract It has been widely recognized that the imprecision and incompleteness inherent in real-world data suggest a fuzzy extension for information management systems. Various attempts to enhance these systems by fuzzy extensions can be found in the literature. Varying approaches concerning the fuzzification of the concept of a relation are possible, two of which are referred to in this article as the generalized fuzzy approach and the fuzzy-set relation approach. In these enhanced models, items can no longer be retrieved by merely using equality-check operations between constants; instead, operations based on some kind of nearness measures have to be developed. In fact, these models require such a nearness measure to be established for each domain for the evaluation of queries made upon them. An investigation of proposed nearness measures, often fuzzy equivalences, is conducted. The unnaturalness and impracticality of these measures leads to the development of a new measure: the resemblance relation, which is defined to be a fuzzified version of a tolerance relation. Various aspects of this relation are analyzed and discussed. It is also shown how the resemblance relation can be used to reduce redundancy in fuzzy relational database systems.

Individualized tutoring using an intelligent fuzzy temporal relational database

The student record (SR) is a major source of input for any decision making done by an Intelligent Tutoring System (ITS) and is a basis of the individualization in such systems. However, most ITSs still have “generalized” student models which represent a type of student rather than a particular one. Until the SR becomes truly representative of each individual student, the goal of providing individualized tutoring cannot be attained. In this paper we describe an Intelligent Fuzzy Temporal Relational Database (IFTReD), an intelligent system-independent SR which allows for almost any degree of individualization the designer wishes to incorporate. It is anticipated that this IFTReD will provide a significant improvement over standard AI storage techniques for the SR. These improvements will be realized in terms of: (1) intelligence; (2) greater storage efficiency; (3) greater speed in retrieval and query; (4) ability to handle linguistic codes, ranges, fuzzy possibilities, and incomplete data in student models; (5) friendliness of query language; (6) availability of temporal knowledge to give a history of past performance; and (7) a more holistic view of the student, permitting greater individualization of the tutor.

Fuzzy extension of inheritance hierarchies

Abstract In this paper, we present a fuzzy extension of inheritance hierarchies. The purpose of this extension is to provide a more refined construct that facilitates the representation of relations among concepts under uncertainty conditions. The extension is done in two steps. First, the synonymy relation is incorporated in the inheritance hierarchy, resulting in a new construct denoted a concept-network . Second, the relations of the concept-network are fuzzified, yielding a new construct denoted a fuzzy concept-network . We show how a fuzzy concept-network can be interpreted as a collection of inheritance hierarchies. Thus simplicity, which is an important characteristic of inheritance hierarchies, is preserved. We discuss how fuzzy concept-networks can be used in the application of reusable software retrieval in object-oriented software development.

A prototype interface for collaborative mobile learning

An architecture is presented that is a user-centered model for collaborative mobile learning. A paper prototype was first implemented to determine an optimum interface layout for mobile interaction between users via PDAs for the domain of reading-comprehension using Question-Answer Relationship (QAR). A prototype was then constructed using the results of the paper prototype test, the Java 2 Mobile Environment (J2ME), and the Java Wireless Toolkit 2.1. Unlike other systems, the interface developed provides collaborative interaction in mobile learning tasks by allowing textual or voice input.

Advances in local student modeling using informal fuzzy reasoning

Abstract This paper presents an approach to local student modeling in mathematics intelligent tutors. A knowledge representation is developed that stores separately the semantic and structural information needed to represent math word problems. Based on this representation, and the assignment of weights to semantic labels for problem sets, the student is allowed considerable flexibility in the development of solutions. The technique used in matching student solutions to acceptable solutions is based on imprecision or fuzziness, i.e. exact matches to stored solutions are not required. Moreover, this imprecise approach, together with a “collapsing” of the tree of all possible solutions, substantially reduces storage and search requirements, addressing some combinatorial explosion and speed problems of modeling. This method can support a variety of instructional strategies.

Neural networks applied to knowledge acquisition in the student model

Knowledge acquisition for the student model of intelligent tutoring systems (ITSs) remains a difficult problem, partly because of the complexity associated with understanding both how people learn and how it is best to tutor, much of which relates to metacognition and problem-solving skills. The bottleneck associated with this area significantly increases the development times of ITSs. Neural networks have made a marked impact in many artificial intelligence areas such as pattern recognition, speech learning, speech understanding, and hand-written character recognition. Neural networks are noted for their ability to handle noisy and approximate data, to generalize over situations they have not handled before, and to be represented in a way amenable to parallel processing. In addition, they have the ability to learn, a characteristic which should prove very useful in the development of ITSs. In this paper, we show that neural networks can address the knowledge acquisition bottleneck associated with the student model. We demonstrate that incomplete knowledge obtained from the expert can be refined and expanded by a neural network to provide a more complete, and hence more accurate, student model.

Families of optimal fault-tolerant multiple-bus networks

Optimally fault-tolerant partial-connection multiple-bus networks and their fault-tolerant routing algorithms are presented in this paper. The proposed networks are scalable and provide flexibility in the choice of network parameters determining construction cost, system performance, and fault tolerance, given a fixed number of processors. In this design, when performance begins to fall due to contention, the simple addition of a bus can improve performance without adding costly processors or changing the whole topology, as required for other multiple-bus designs. Also, in situations requiring high reliability, for a fixed number of processors, excellent fault tolerance can be obtained.

A Paper Prototype Study of the Interface for a Children's Collaborative Handheld Learning Application

The use of handheld computers in the classroom environment is an area generating much interest among researchers. The questions of how to manage power and graphics while handling small screen space remain issues to be examined. In this study, a paper prototype test of the collaborative interface features was implemented using five user tasks: logging in, reading a text, answering text related questions, chatting, and entering data into a personal workbook. The test results illustrated the need for clear instructions and menu options for younger users, and that speech and written input were preferred over other methods. The feedback obtained from the test will drive the development of the actual system in future studies.

Order-(t − 1) balanced multiple-valued filing scheme using difference vectors*

A balanced multiple-valued filing scheme of order (t − 1) is constructed based on the finite projective geometry PG(t, q), (t + 1) not prime, t odd. The attributes and attribute values are defined using a spread of flats from PG(t, q) as in Yamamoto et al. (Inform. Contr. 21 (1972), 7291) and Berman (Inform. Contr. 32 (1976), 128138). Certain structural properties of PG(t, q) are exploited, resulting in an easily constructed bucket structure. The (t − 2)-flats, which represent the buckets, are partitioned into orbits each of which is uniquely identifiable by its difference vector. The storage and retrieval algorithms are based on these difference vectors. There is no scanning of buckets or sub-buckets. The concepts in this paper are illustrated by an example based on PG(5, 2).

The STARS Leadership Corps: Case studies in broadening participation in computing

We have formed the STARS Alliance to address alarming declines in computing enrollments and the need to broaden participation in computing. Our innovative STARS Leadership Corps engages college students in a community dedicated to giving back, persevering, and striving for excellence. The successful Corps model incorporates best practices in recruiting and retention in computing, including student experiences in outreach, research, and service. We present case studies of three diverse implementations of the STARS Leadership Corps that demonstrate how the Corps can be tailored to individual institutions and student interests.