TASK ANALYSIS: BULIDING MODELS OF CHANGE PROCESSES IN GIFTED EDUCATION

Researchers in the field of gifted education are becoming increasingly aware that a variety of research approaches can make contributions to research on giftedness. This paper describes task analysis, a research methodology derived from counseling process research. Task analysis appears to be a useful research approach for building theoretical models of how therapeutic and educational interventions can facilitate positive changes in gifted children. The paper traces the historical development of task analysis methodology, describes the steps involved in conducting a rational‐empirical task analysis of change processes, and provides an example of how task analysis methodology might be applied to investigations of change processes in the field of gifted education.     

Holographic perspective

Although the principle of holography was conceived as long ago as 1947, interest was limited until the advent of the laser in 1960 made possible the off-axis holography widely used today. Since then holography has found many applications and become so well known outside the laboratory as to be a recognized art form. Within the last few years considerable progress has also been made in acoustic holography.     

From retrieval to robotics: The national science foundation's support of information research

This article describes how the National Science Foundation's support of information research has evolved from NSF's original mission of improving the dissemination of scientific and technical information to its present interest in information robotics and intelligent systems. The pattern of grant support, since a major reorganization of the information science division in 1978, is analyzed in terms of the institutions, the investigators, and the disciplines that have been major grant recipients.     

Advanced,Analytic, Automated (AAA) Measurement of Engagement During Learning

It is generally acknowledged that engagement plays a critical role in learning. Unfortunately, the study of engagement has been stymied by a lack of valid and efficient measures. We introduce the advanced, analytic, and automated (AAA) approach to measure engagement at fine-grained temporal resolutions. The AAA measurement approach is grounded in embodied theories of cognition and affect, which advocate a close coupling between thought and action. It uses machine-learned computational models to automatically infer mental states associated with engagement (e.g., interest, flow) from machine-readable behavioral and physiological signals (e.g., facial expressions, eye tracking, click-stream data) and from aspects of the environmental context. We present 15 case studies that illustrate the potential of the AAA approach for measuring ensgagement in digital learning environments. We discuss strengths and weaknesses of the AAA approach, concluding that it has significant promise to catalyze engagement research.     

Optimal design for manipulation of random consensus over discrete information in networked systems

In distributed and cooperative systems, the network structure is determinant to the success of the strategy adopted to solve complex tasks. Those systems are primarily governed by consensus protocols whose convergence is intrinsically dependent on the network topology. Most of the consensus algorithms deal with continuous values and perform average-based strategies to reach cohesion over the exchanged information. However, many problems demand distributed consensus over countable values, that cannot be handled by traditional protocols. In such a context, this work presents an approach based on semidefinite programming to design the optimal weights of a network adjacency matrix, in order to control the convergence of a distributed random consensus protocol for variables at the discrete-space domain, based on the voter model. As a second contribution, this work uses Markov theory and the biological inspiration of epidemics to find out a dynamical spreading model that can predict the information diffusion over this discrete consensus protocol. Also, convergence properties and equilibrium points of the proposed model are presented regarding the network topology. Finally, extensive numerical simulations evaluate the effectiveness of the proposed consensus algorithm, its spreading model, and the approach for optimal weight design.