The Economic Advantages of Knowledge Support Systems

This article presents both the short-arid long-term economic considerations that should drive the implementation of knowledge support systems. These systems utilize emerging computer and information technologies to provide employees with training, reference, and expert advice on desk-top computer terminals. Specifically, those considerations that apply to training and performance managers are discussed. In reviewing the short- and long-term aspects of performance in economic terms, these managers will be better equipped to propose and justify knowledge support system technology. This article also addresses how managers can cope with short-term requirements for cutbacks and cost control. Instead of going into a defensive position during times of cost control, managers can propose new knowledge support system technology that will help the corporation move forward with cost controls and prepare for the future requirements of increasing the performance of people. Finally, a knowledge support system that fits the short- and long-term economic requirements of corporations is presented. This knowledge support system integrates the features of computer-based training, hypertext reference systems, and expert systems into one platform.     

Good + Bad = ? Developmental Differences in Balancing Gains and Losses in Value-Based Decisions From Memory

Value-based decisions often involve comparisons between benefits and costs that must be retrieved from memory. To investigate the development of value-based decisions, 9- to 10-year olds (N = 30), 11- to 12-year olds (N = 30), and young adults (N = 30) first learned to associate gain and loss magnitudes with symbols. In a subsequent decision task, participants rapidly evaluated objects that consisted of combinations of these symbols. All age groups achieved high decision performance and were sensitive to gain–loss magnitudes, suggesting that required core cognitive abilities are developed early. A cognitive-modeling analysis of performance revealed that children were less efficient in object evaluation (drift rate) and had longer nondecision times than adults. Developmental differences, which emerged particularly for objects of high positive net value, were linked to mnemonic and numerical abilities.     

Research Findings from the Tennessee Value-Added Assessment System (TVAAS) Database: Implications for Educational Evaluation and Research

The Tennessee Value-Added Assessment System determines the effectiveness of school systems, schools, and teachers based on student academic growth over time. An integral part of TVAAS is a massive, longitudinally merged database linking students and student outcomes to the schools and systems in which they are enrolled and to the teachers to whom they are assigned as they transition from grade to grade. Research conducted utilizing data from the TVAAS database has shown that race, socioeconomic level, class size, and classroom heterogeneity are poor predictors of student academic growth. Rather, the effectiveness of the teacher is the major determinant of student academic progress. Teacher effects on student achievement have been found to be both additive and cumulative with little evidence that subsequent effective teachers can offset the effects of ineffective ones. For these reasons, a component linking teacher effectiveness to student outcomes is a necessary part of any effective educational evaluation system.     

Close enough but not too far: Assessing the effects of university-industry research relationships and the rise of academic capitalism

Analysts assessing the impact of university-industry research relations (UIRRs) and increasing proprietary behavior on the part of universities often focus on single-indicators or adopt promotional or critical stances. However, assessing impacts of shifts toward a more proprietary university is inherently complex because of potential countervailing or mediating factors within working relationships. From interviews with 84 biological scientists at nine universities we find scientists view UIRRS and university intellectual property (IP) policies in complex and often conflicting ways. For example, university scientists believe UIRRs are valuable for increasing contact with scientists, but are problematic because working with industry can restrict communication among scientists. Also scientists believe university IP policies should shield their work from opportunistic behavior and at the same time be designed to attract industry partners. In addition scientists believe universities use their IP policies primarily as revenue raising vehicles and secondarily to address public good issues such as technology transfer.