This is a presentation of 21 slides at the Leadership Development session of the ASIST 2005 Annual Meeting at Charlotte, N.C. on October 30. It discusses the 2002 virtual community building experiment undertaken by the Arizona Chapter of ASIST. The chapter experimented with three different pieces of software, a wiki, a content management system, and slashcode. This presentation was also video-taped and may become available through the ASIST website, http://www.asis.org/.

"Statistical inference plays an increasingly important role in science, finance and industry. Despite the extensive research and wide application of statistical inference, most of the efforts focus on uniform models. This thesis considers the statistical inference in models with abrupt changes instead. The task is to estimate change-points where the underlying models change. We first study low dimensional linear regression problems for which the underlying model undergoes multiple changes. Our goal is to estimate the number and locations of change-points that segment available data into different regions, and further produce sparse and interpretable models for each region. To address challenges of the existing approaches and to produce interpretable models, we propose a sparse group Lasso (SGL) based approach for linear regression problems with change-points. Then we extend our method to high dimensional nonhomogeneous linear regression models. Under certain assumptions and using a properly chosen regularization parameter, we show several desirable properties of the method. We further extend our studies to generalized linear models (GLM) and prove similar results. In practice, change-points inference usually involves high dimensional data, hence it is prone to tackle for distributed learning with feature partitioning data, which implies each machine in the cluster stores a part of the features. One bottleneck for distributed learning is communication. For this implementation concern, we design communication efficient algorithm for feature partitioning data sets to speed up not only change-points inference but also other classes of machine learning problem including Lasso, support vector machine (SVM) and logistic regression."

Interactivity is defined by Henri (1992) as a three-step process involving communication of information, a response to this information, and a reply to that first response. It is a key dimension of computer-mediated communication, particularly in the one-on-one communication involved in an electronic mentoring program. This report analyzes the interactivity between pairs of corporate research scientists (mentors) and university biology students (protégés) during two consecutive implementations of an electronic mentoring program. The frequency and structure of the interactions within each pair were examined to provide context: 542 messages were posted among the 20 mentors and 20 protégés. These messages were formed into 5-10 threads per pair, with 3-4 messages per thread, indicating a high level of interactivity (there were more responses posted than independent messages). Mentor-protégé pairs rated as effective by both mentors and protégés' posted more messages overall, had well-structured threads, had protégés and mentor postings that were similar in topic coverage and message length, and had little overt "management" behavior by mentors. However, there appears to be no clear recipe for successful interaction. Not only are there a variety of factors at play in developing an online relationship in this context, but mentor-protégés pairs can falter at various stages in the process and in various ways.

This 84-page report defines the Cyberinfrastructure program proposed by the National Science Foundation (NSF). Here is the text of the news release from the University of Michigan School of Information: " Atkins committee issues NSF report on development of cyberinfrastructure (Feb 2003) A National Science Foundation (NSF) committee chaired by University of Michigan professor Daniel Atkins has recommended the organization spend an additional $1 billion per year developing the nation's "cyberinfrastructure" to support scientific research. The Advisory Committee on Cyberinfrastructure argues that investment in a comprehensive cyberinfrastructure can change profoundly what scientists and engineers do, how they do it, and who participates. Its recommendations are detailed in a newly released report titled Revolutionizing Science and Engineering through Cyberinfrastructure. In the same way society now depends on highways, water systems, and power grids, the panel contends, scientific research in the coming years will depend on the quality of the cyberinfrastructure -- the integrated information, computing, and communications systems that tie us together. "It's not just the raw technology, but also the organization and the people," says Atkins, who is professor in the School of Information and the Department of Electrical Engineering and Computer Science at U-M. It's also the standards for interoperability that will allow different disciplines to use the same infrastructure, "just the way we agreed long ago on a standard gauge for railroad tracks." "The path forward that this report envisions ... truly has the potential to revolutionize all fields of research and education," says Peter Freeman, assistant director of the NSF for Computer and Information Sciences and Engineering (CISE), the NSF arm that commissioned the report. The report was issued on the same day the NSF submitted its $5.48 billion budget request for fiscal year 2004. "NSF has been a catalyst for creating the conditions for a nascent cyberinfrastructure-based revolution," says Atkins, a revolution being driven from the ground up. "We've clearly documented extensive grass-roots activity in the scientific and engineering research community to create and use cyberinfrastructure to empower the next wave of discovery." The committee cites NSF support for such projects as the Network for Earthquake Engineering Simulations (NEES), the TeraGrid effort, and the Digital Libraries Initiative as seminal in the development of a cyberinfrastructure. At the same time, the report makes clear that the cyberinfrastructure needed cannot be built with today's off-the-shelf technology, and it argues for increased NSF support for fundamental research in computer science and engineering. The report emphasizes the importance of acting quickly and the risks of failing to do so. Those risks include lack of coordination, which could leave key data in irreconcilable formats; long-term failures to archive and curate data collected at great expense; and artificial barriers between disciplines built from incompatible tools and structures. The NSF has a "once-in-a-generation opportunity," according to the committee, to lead the scientific and engineering community in the coordinated development and expansive use of cyberinfrastructure."

We are finding ourselves in a rapidly growing and complex digital environment which has in turn increased our dependency on information. But there is increasing evidence that our information skills are not keeping pace in any systematic fashion. We all need help to sharpen the techniques and skills to manage information. Present paper is an attempt to present the current status of information literacy and the emerging roles of libraries and schools of LIS education in augmenting the information literacy campaign.

Networked multi-agent systems have attracted lots of researchers to develop algorithms, techniques, and applications.A multi-agent networked system consists of more than one subsystem (agent) to cooperately solve a global problem with only local computations and communications in a fully distributed manner. These networked systems have been investigated in various different areas including signal processing, control system, and machine learning. We can see massive applications using networked systems in reality, for example, persistent surveillance, healthcare, factory manufacturing, data mining, machine learning, power system, transportation system, and many other areas. Considering the nature of those mentioned applications, traditional centralized control and optimization algorithms which require both higher communication and computational capacities are not suitable. Additionally, compared to distributed control and optimization approaches, centralized control, and optimization algorithms cannot be scaled into systems with a large number of agents, or guarantee performance and security. All of the limitations of centralized control and optimization algorithms motivate us to investigate and develop new distributed control and optimization algorithms in networked systems. Moreover, convergence rate and analysis are crucial in control and optimization literature, which motivates us to investigate how to analyze and accerlate the convergence of distributed optimization algorithms.

This item is not the definitive copy. Please use the following citation when referencing this material: Dillon, A. (2000) Designing a better learning environment with the Web: problems and prospects. CyberPsychology and Behavior, 3(1), 97-102. Abstract: In a recent review of the empirical findings on hypermedia and learning outcomes, Dillon and Gabbard (1998) concluded that contrary to many people's assumptions, the use of hypermedia-based instructional systems in education had not produced significant learning gains. Indeed, their review concluded that such instructional technologies rarely showed any benefit for learners over existing paper- or lecture-based instructions. While it is commonplace these days to dismiss as irrelevant any media comparison study, the Dillon and Gabbard review went further, also examining comparisons made between alternative hypermedia implementations (a within-media comparison) and between single and group learners employing this technology. Since hypermedia is the underlying technology of the World Wide Web, their findings made depressing reading for those of us who believe that this technology is important and could be put to powerful instructional use. The present issue contains papers from many leading theorists who advocate the use and exploitation of information technologies such as hypermedia and the World-Wide Web in our classrooms, and I am not completely in disagreement with them. However, I wish to question the very assumptions on which the use of the Web and standalone hypermedia applications are based. What I aim to provide in this paper is a sense of the gaps in our knowledge, and to speculate on why education is so poorly served by the wonderful technologies that are within our grasp.

Telecare and especially Mobile Care Systems are getting more and more popular. They have two major benefits: first, they drastically improve the living standards and even health outcomes for patients. In addition, they allow significant cost savings for adult care by reducing the needs for medical staff. A common drawback of current Mobile Care Systems is that they are rather stationary in most cases and firmly installed in patients’ houses or flats, which makes them stay very near to or even in their homes. There is also an upcoming second category of Mobile Care Systems which are portable without restricting the moving space of the patients, but with the major drawback that they have either very limited computational abilities and only a rather low classification quality or, which is most frequently, they only have a very short runtime on battery and therefore indirectly restrict the freedom of moving of the patients once again. These drawbacks are inherently caused by the restricted computational resources and mainly the limitations of battery based power supply of mobile computer systems. This research investigates the application of novel Artificial Intelligence (AI) and Machine Learning (ML) techniques to improve the operation of 2 Mobile Care Systems. As a result, based on the Evolving Connectionist Systems (ECoS) paradigm, an innovative approach for a highly efficient and self-optimising distributed online machine learning algorithm called MECoS - Moving ECoS - is presented. It balances the conflicting needs of providing a highly responsive complex and distributed online learning classification algorithm by requiring only limited resources in the form of computational power and energy. This approach overcomes the drawbacks of current mobile systems and combines them with the advantages of powerful stationary approaches. The research concludes that the practical application of the presented MECoS algorithm offers substantial improvements to the problems as highlighted within this thesis.

This doctoral dissertation describes a research study that examined the effectiveness of an experimental Supplemental Instruction (SI) program that utilized computer-mediated communication (CMC) rather than traditional SI review sessions. During the Spring 1999 semester, six sections of an introductory computer course were offered via the Internet by a suburban community college district in Texas. Using Campbell and Stanley's Nonequivalent Control Group model, the online SI program was randomly assigned to four of the course sections with the two remaining sections serving as the control group. The students hired to lead the online review sessions participated in the traditional SI training programs at their colleges, and received training conducted by the researcher related to their roles as online discussion moderators. Following recommendations from Congos and Schoeps, the internal validity of the groups was confirmed by conducting independent t-tests comparing the students' cumulative credit hours, grade point averages, college entrance test scores, and first exam scores. The study's four null hypotheses were tested using multiple linear regression equations with alpha levels set at .01. Results indicated that the SI participants earned better course grades even though they had acquired fewer academic credits and had, on average, scored lower on their first course exams. Both the control group and the non-SI participants had average course grades of 2.0 on a 4.0 scale. The students who participated in at least one SI session had an average final course grade of 2.5, exceeding their previous grade point average of 2.15. Participation in one SI session using CMC was linked to a one-fourth letter grade improvement in students' final course grades. Although not statistically significant, on the average, SI participants had slightly better course retention, marginally increased course satisfaction, and fewer student-initiated contacts with their instructors.

By 2007, one half of higher education students are expected to enroll in distributed learning courses. Higher education institutions need to attract students searching the Internet for courses and need to provide students with enough information to select courses. Internet resource discovery tools are readily available, however, users have difficulty selecting relevant resources. In part this is due to the lack of a standard for representation of Internet resources. An emerging solution is metadata. In the educational domain, the IEEE Learning Technology Standards Committee (LTSC) has specified a Learning Object Metadata (LOM) standard. This exploratory study (a) determined criteria students think are important for selecting higher education courses, (b) discovered relationships between these criteria and students' demographic characteristics, educational status, and Internet experience, and (c) evaluated these criteria vis-à-vis the IEEE LTSC LOM standard. Web-based questionnaires (N=209) measured (a) the criteria students think are important in the selection of higher education courses and (b) three factors that might influence students' selections. Respondents were principally female (66%), employed full time (57%), and located in the U.S. (89%). The chi square goodness-of-fit test determined 40 criteria students think are important and exploratory factor analysis determined five common factors among the top 21 criteria, three evaluative factors and two descriptive. Results indicated evaluation criteria are very important in course selection. Spearman correlation coefficients and chi-square tests of independence determined the relationships between the importance of selection criteria and demographic characteristics, educational status, and Internet experience. Four profiles emerged representing groups of students with unique concerns. Side by side analysis determined if the IEEE LTSC LOM standard included the criteria of importance to students. The IEEE LOM by itself is not enough to meet students course selection needs. Recommendations include development of a metadata standard for course evaluation and accommodation of group differences in information retrieval systems.