The intricate construction of online educational systems lies within three principal activities: Design , implementation and proper post-implementation assessment. There is not enough knowledge or experience in those regards. Efficient execution of these three major activities necessitates the use of design and pedagogical models to achieve cost and time efficiency, as well as high pedagogical quality. Utilization of online educational systems would benefit from a struc-tured approach to design, implementation, and student's assessment. In this paper, we present the design of an online education system and its implementation, and we analyze student's behavior towards the system using the theory of planned behavior and the technology acceptance model. A survey methodology approach was followed. The partial least squares method was used for the assessment of the results discussed. Structural equation analysis provides evidence for the superiority of the theory of planned behavior in explaining student's behavior towards online educational systems. Limitation, implications, design recommendations, and suggestions for future research are discussed.
While many modelling methods have been developed and introduced to predict the actual state of a system at the next point of time, the purpose of this research is to present and discuss two approaches NOT to predict the exact future states, but to identify the potential for final collapse of a system. The first approach is based on kernel methods, a sub category of supervised learning, and attempts to provide a visualization method to classify the active and dead companies and predict the potential collapse of a system. The second method aims to analyze the inclination of a system by looking at the local changes that have been observed over a certain period of time in the past. Application of these modelling approaches to predict collapse in different companies belonging to two industrial sectors by looking at behaviour of their closing stock prices are discussed in this research. Advantages and limitations of each approach are also discussed.
While many modelling methods have been developed and introduced to predict the actual state of a system at the next point of time, the purpose of this research is to present and discuss two approaches NOT to predict the exact future states, but to identify the potential for final collapse of a system. The first approach is based on kernel methods, a sub category of supervised learning, and attempts to provide a visualization method to classify the active and dead companies and predict the potential collapse of a system. The second method aims to analyze the inclination of a system by looking at the local changes that have been observed over a certain period of time in the past. Application of these modelling approaches to predict collapse in different companies belonging to two industrial sectors by looking at behaviour of their closing stock prices are discussed in this research. Advantages and limitations of each approach are also discussed.
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