In the area of student knowledge assessment, knowledge tracing is a model that has been used for over a decade to predict student knowledge and performance. Many modifications to this model have been proposed and evaluated, however, the modifications are often based on a combination of intuition and experience in the domain. This method of model improvement can be difficult for researchers with...

Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in literature, centralized governance supports decision-making and performance measurement technology companies. For this reason, a reliable system with an integrated data model that enables rapid collection transformation stored heterogeneous different sources is ne...

This article presents a survey of model-driven techniques for data model synthesis. During an extensive research, we identified more than 70 research papers in the field and more than 15 different graphical notations used for the source model representation. We have classified the proposed approaches into four distinct groups: function-oriented, process-oriented, communication-oriented and goal...

The data-driven computing paradigm initially introduced by Kirchdoerfer and Ortiz (2016) enables finite element computations in solid mechanics to be performed directly from material data sets, without an explicit model. From a computational effort point of view, the most challenging task is projection admissible states at points onto their closest set. In this study, we compare develop several...

achieving the organizational objectives needs employees’ behavior and their collaboration with management. strategic behavior-driven is depend on different factors. the aim of this article is to determine the impact of factors on strategic behavior-driven. a conceptual model was developed and tested on a survey in social insurance company of tehran which employees’ participated. data collected ...

Which samples should be labelled in a large dataset is one of the most important problems for training deep learning. So far, variety active sample selection strategies related to learning have been proposed literature. We defined them as Active Deep Learning (ADL) only if their predictor or selector model, where basic learner called and labeling schemes are selector. In this survey, we categor...