Methodical model for teaching foreign professional communication students of “Agricultural Engineering” programme based on content and language integrated learning

Methodical model is a set of interconnected components that form a single system aimed at achieving a common goal – the formation of foreign language communicative competence among students in the “Agricultural Engineering” programme based on content and language integrated learning. The teaching system is built on the basis of system, competency-based, per-sonal-activity, communicative-cognitive approaches, as well as content and language integrated learning. These approaches are implemented in practice using a number of didactic and methodical principles. We describe in detail the methodical principles of content and language integrated learning: principle 4 “C”, the principle of cognition, the principle of unity of thought and speech activity, the principle of integration of a foreign language and subject content, the principle of gradual complication of content, the principle of duality of reliance on native and foreign lan-guages, the principle of optimality, the principle of knowledge personalization, the principle of in-teractivity and the principle of language adaptation. Content and language integrated learning of students is carried out on the basis of the following teaching methods: interactive, communicative, information-reproductive, productive, tandem-method, vocational teaching and control methods. The content of teaching based on content and language integrated modeling reflects the profile specifics of training specialists in the framework of a particular specialty. Learning tools are a set of tools of a teacher for the purpose of development, teaching and upbringing. We present and de-scribe in detail all the components of the methodical model.

Integrating Semantic Knowledge with Web Usage Mining for Personalization

Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional approaches such as collaborative filtering. Despite their success, such systems, as in more traditional ones, do not take into account the semantic knowledge about the underlying domain. Without such semantic knowledge, personalization systems cannot recommend different types of complex objects based on their underlying properties and attributes. Nor can these systems possess the ability to automatically explain or reason about the user models or user recommendations. The integration of semantic knowledge is, in fact, the primary challenge for the next generation of personalization systems. In this chapter we provide an overview of approaches for incorporating semantic knowledge into Web usage mining and personalization processes. In particular, we discuss the issues and requirements for successful integration of semantic knowledge from different sources, such as the content and the structure of Web sites for personalization. Finally, we present a general framework for fully integrating domain ontologies with Web usage mining and personalization processes at different stages, including the preprocessing and pattern discovery phases, as well as in the final stage where the discovered patterns are used for personalization.

Integrating Semantic Knowledge with Web Usage Mining for Personalization

Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional approaches such as collaborative filtering. Despite their success, such systems, as in more traditional ones, do not take into account the semantic knowledge about the underlying domain. Without such semantic knowledge, personalization systems cannot recommend different types of complex objects based on their underlying properties and attributes. Nor can these systems possess the ability to automatically explain or reason about the user models or user recommendations. The integration of semantic knowledge is, in fact, the primary challenge for the next generation of personalization systems. In this chapter we provide an overview of approaches for incorporating semantic knowledge into Web usage mining and personalization processes. In particular, we discuss the issues and requirements for successful integration of semantic knowledge from different sources, such as the content and the structure of Web sites for personalization. Finally, we present a general framework for fully integrating domain ontologies with Web usage mining and personalization processes at different stages, including the preprocessing and pattern discovery phases, as well as in the final stage where the discovered patterns are used for personalization.

Integrating Semantic Knowledge with Web Usage Mining for Personalization

Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional approaches such as collaborative filtering. Despite their success, such systems, as in more traditional ones, do not take into account the semantic knowledge about the underlying domain. Without such semantic knowledge, personalization systems cannot recommend different types of complex objects based on their underlying properties and attributes. Nor can these systems possess the ability to automatically explain or reason about the user models or user recommendations. The integration of semantic knowledge is, in fact, the primary challenge for the next generation of personalization systems. In this chapter we provide an overview of approaches for incorporating semantic knowledge into Web usage mining and personalization processes. In particular, we discuss the issues and requirements for successful integration of semantic knowledge from different sources, such as the content and the structure of Web sites for personalization. Finally, we present a general framework for fully integrating domain ontologies with Web usage mining and personalization processes at different stages, including the preprocessing and pattern discovery phases, as well as in the final stage where the discovered patterns are used for personalization.

Integrating Semantic Knowledge with Web Usage Mining for Personalization

Web usage mining has been used effectively as an approach to automatic personalization and as a way to overcome deficiencies of traditional approaches such as collaborative filtering. Despite their success, such systems, as in more traditional ones, do not take into account the semantic knowledge about the underlying domain. Without such semantic knowledge, personalization systems cannot recommend different types of complex objects based on their underlying properties and attributes. Nor can these systems possess the ability to automatically explain or reason about the user models or user recommendations. The integration of semantic knowledge is, in fact, the primary challenge for the next generation of personalization systems. In this chapter we provide an overview of approaches for incorporating semantic knowledge into Web usage mining and personalization processes. In particular, we discuss the issues and requirements for successful integration of semantic knowledge from different sources, such as the content and the structure of Web sites for personalization. Finally, we present a general framework for fully integrating domain ontologies with Web usage mining and personalization processes at different stages, including the preprocessing and pattern discovery phases, as well as in the final stage where the discovered patterns are used for personalization.