International audienceThis paper introduces a new context modeling approach for the business process management field. The proposed approach aims at identifying and formalizing the contextual knowledge relevant to business processes in order to be able to adapt business processes according to the context. This approach has the particularity to be generic and extensible; it can be integrated with many business process modeling approaches. It is based on ontologies and has two layers, i.e. generic layer and specific layer. Throughout the paper we compare the proposed approach with the related work in order to clearly demonstrate why we propose this approach
Universities around the world are keen to develop study plans that will guide their graduates to success in the job market. The internship course is one of the most significant courses that provides an experiential opportunity for students to apply knowledge and to prepare to start a professional career. However, internships do not guarantee employability, especially when a graduate's internship performance is not satisfactory and the internship requirements are not met. Many factors contribute to this issue making the prediction of employability an important challenge for researchers in the higher education field. In this paper, our aim is to introduce an effective method to predict student employability based on context and using Gradient Boosting classifiers. Our contributions consist of harnessing the power of gradient boosting algorithms to perform context-aware employability status prediction processes. Student employability prediction relies on identifying the most predictive features impacting the hiring opportunity of graduates. Hence, we define two context models, which are the student context based on the student features and the internship context based on internship features. Experiments are conducted using three gradient boosting classifiers: eXtreme Gradient Boosting (XGBoost), Category Boosting (CatBoost) and Light Gradient Boosted Machine (LGBM). The results obtained showed that applying LGBM classifiers over the internship context performs the best compared to student context. Therefore, this study provides strong evidence that the employability of graduates is predictable from the internship context.
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