Academic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one.

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The recent development of the WorldWideWeb, information, and communications technology have transformed the world and moved us into the data era resulting in an overload of data analysis. Students at high school use, most of the time, the internet as a tool to search for universities/colleges, university?s majors, and career paths that match their interests. However, selecting higher education choices such as a university major is a massive decision for students leading them, to surf the internet for long periods in search of needed information. Therefore, the purpose of this study is to assist high school students through a hybrid recommender system (RS) that provides personalized recommendations related to their interests. To reach this purpose we proposed a novel hybrid RS approach named (COHRS) that incorporates the Knowledge base (KB) and Collaborative Filtering (CF) recommender techniques. This hybrid RS approach is supported by the Case based Reasoning (CBR) system and Ontology. Hundreds of queries were processed by our hybrid RS approach. The experiments show the high accuracy of COHRS based on two criteria namely the accuracy of retrieving the most similar cases and the accuracy of generating personalized recommendations. The evaluation results show the percentage of accuracy of COHRS based on many experiments as follows: 98 percent accuracy for retrieving the most similar cases and 95 percent accuracy for generating personalized recommendations.

Recommender systems in education improve the teacher's working process by providing relevant resources to aid his course design in addition to learning new teaching methodologies. However, these systems have limited adaptability according to a global evaluation of teacher's activities. This approach of user profiling is convenient, but not adequate for teacher's context description. In our approach, it is assumed that the utilization of teacher's emotions has an inevitable role to accomplish a full contextual description for teacher. Teacher context ontology (TCO) provides a representation for the teacher's living and working contexts along with the main educational concepts. In this paper, we introduce a conceptual integration approach between Moodflow@doubleYou emotional data as a concept and TCO ontology. Furthermore, we intend to prove the importance of integrating such concept for sufficient teacher's context description. The impact of utilization emotional data in educational recommender systems is discussed. Finally, this paper represents the conducted experiments' results which show the advantage of such integration.