Suchergebnisse

This study was to examine the big-fish-little-pond effects (BFLPE) on mathematics achievement by the students' positive attitude toward mathematics (PATM) of Taiwanese eighth-grade students in the Trends of International Mathematics and Science Study (TIMSS). The SLM (students like learning mathematics), SVM (students value mathematics), and SCM (students confident in mathematics) have been three critical factors of students' PATM. The sample comprised 5,042 Taiwanese eighth-grade students from 150 schools that participated in the TIMSS. The two-level hierarchical linear model analysis extends the BFLP positive association between students' individual mathematics achievement with SCM to include SLM and SVM levels. In addition, the BFLPE negative correlations between school-average mathematics achievements and aggregated self-concepts have also been confirmed with these three variables. The interaction study indicated that students with high achievements experienced a greater BFLPE on SLM and SCM scores than did students with low achievements. Implications of the findings and suggestions for future study are discussed.

In the past several decades, Taiwanese companies have been successful in the world market. However, knowledge about how these firms use information technology is very limited. Investigates the application of decision support systems (DSS) and executive information systems (EIS) in Taiwan. Two mail surveys were conducted to explore who used the system, where the systems were applied, what decision models were used and reasons for not using these systems. The results indicate that: more than 20 per cent of the firms already used DSS or EIS and an additional 37 per cent had plans to adopt them shortly; 87 per cent of the firms considered DSS and EIS to be important to their competitiveness; middle‐ and lower‐level managers were major DSS and EIS users; half of them used the system every day; the most troublesome problem in developing DSS/EIS was the difficulty in determining information requirements; and information aggregation and what‐if analysis were major functions for decision support. Furthermore, quantitative models were employed only in large and old companies. The most popular models included linear programming, PERT/CPM, and regression analysis, whereas the most popular domains for application were finance and production management.

ABSTRACTThis paper examines cognitive considerations in developing model management systems (MMSs). First, two approaches to MMS design are reviewed briefly: one based on database theory and one based on knowledge‐representation techniques. Then three major cognitive issues—human limitations, information storage and retrieval, and problem‐solving strategies—and their implications for MMS design are discussed. Evidence indicates that automatic modeling, which generates more complicated models by integrating existing models automatically, is a critical function of model management systems.In order to discuss issues pertinent to automatic modeling, a graph‐based framework for integrating models is introduced. The framework captures some aspects of the processes by which human beings develop models as route selections on a network of all possible alternatives. Based on this framework, three issues are investigated: (1) What are proper criteria for evaluating a model formulated by an MMS? (2) If more than one criterion is chosen for evaluation, how can evaluations on each of the criteria be combined to get an overall evaluation of the model? (3) When should a model be evaluated? Finally, examples are presented to illustrate various modeling strategies.

This study examines the gender differences in the enjoyment of recreational sports participation among Taiwanese adults. Data were obtained using the 2007 Taiwan Social Change Survey. The questionnaire included a topical module of the International Social Survey Program regarding leisure time and sports. Results showed that male subjects were more likely to participate in recreational sports to improve their appearance and on account of their personal interest. In addition to these factors, female subjects also experienced greater motivation to participate when Taiwanese athletes performed well in international sporting competitions. This study confirmed that the factors influencing enjoyment of recreational sports participation differ among men and women. These results can be used to better inform public health professionals and other regulatory organizations formulating physical activity intervention strategies.

ABSTRACTRecently, artificial neural networks (ANN) have gained attention as a promising modeling tool for building intelligent systems. A number of applications have been reported in areas varying from pattern recognition to bankruptcy prediction. In this paper, we present a creative methodology that integrates computer simulation, semi‐Markov optimization, and ANN techniques for automated knowledge acquisition in real‐time scheduling. The integrated approach focuses on the synergy between operations research and ANN in eliciting human knowledge, filtering inconsistent data, and building competent models capable of performing at the expert level. The new approach includes three main components. First, computer simulation is used to collect expert decisions. This step allows expert knowledge to be obtained in a non‐intrusive way and minimizes the difficulties involved in interviewing experts, constructing repertory grids, or using other similar structures required for manual knowledge acquisition. The data collected from computer simulation are then optimized using a semi‐Markov decision model to remove data redundancies, inconsistencies, and errors. Finally, the optimized data are used to build ANN‐based expert systems. The integrated approach is evaluated by comparing it with the human expert and using ANN alone in the domain of real‐time scheduling. The results indicate that ANN‐based systems perform worse than human experts from whom the data were collected, but the integrated approach outperforms human experts and ANN models alone.