English is an analytical language, so in English, the word order is important for understanding sentences and phrases. Practicing construpcting noun phrases with the correct order of adjective requires constant practice with feedback. As teachers' time for verifying assignments is limited, we propose to use a tutoring system, which can generate step-by-step feedback. It will help students develop the skill of arranging adjectives in the correct order.

Keywords: tutoring system, ontological modeling, natural language processing, English, adjective order, hypernyms, automated verification of the learners' answers

The article discusses approaches to solving natural language processing problems such as extracting key concepts or terms, as well as semantic relationships between them to build data-driven IT solutions. The subject of the work is relevant due to the constant growth of volumes of low-structured and unstructured digital text. The extracted information can be used to improve numerous processes: automatic tagging, optimization of content search, construction of word clouds and navigation sections; furthermore, to create draft versions of dictionaries, thesauri, and even bases for expert systems.

Keywords: natural language processing, term, lemma, semantical relationship, statistical processing, machine learning, word2vec

Designing function interface in the form of function prototype is an important skill in programming. In this article, we discuss the problems of designing function prototypes in the C programming language. This learning task requires performing 5 different steps. Students can make mistakes at each step. These mistakes can concern misconceptions of function's action, identification of data elements, determining of conceptual-level data types for data elements, data elements' directions, inconvenient naming of function and parameters, and syntax of the C language. Modern solutions for analyzing program code and checking student-written code do not detect all these mistakes in the function-interface design. Existing approaches are based on general solutions for seeking errors in program code so they cannot provide informative feedback about semantic mistakes in designing function interfaces. We propose developing an automatic tutor which should guide students through the process of designing function prototypes step by step, detecting and explaining mistakes at each step and providing hints to help fixing the found mistakes. The tutor should allow natural variability in the function interface. In order to deal with various possible representations of the data passed to and from function, the tutor should work with a formal subject-domain model and a model of the target programming language.

Keywords: automatic tutors, requirements for tutoring systems, introductory programming learning, tasks with complex result, multi-step tasks, online education, mixed education, automatic grading

To share and transfer knowledge, they must be presented in an explicit form that is understandable to both humans and computers. The paper proposes an approach to ontological modeling of WHAT-knowledge, which allows representing knowledge simultaneously in two forms: a) in the form of a visual model (ORM2-diagram), understandable to humans, and b) OWL2-ontology, computer understandable. To convert the knowledge representation from one form to another one, it is proposed to use ontological patterns (mapping rules). Currently, there is no software toolkit that allows a) to build an ORM2-diagram and mapping it in the OWL2-ontology, and b) based on OWL2-ontology, build a visual model in the form of an ORM2-diagram. Therefore, we are developing a Protege-plugin, which should provide a) the creation and editing of WHAT-knowledge by building an ORM2-diagram and mapping it in the OWL2-ontology, and b) visualization of WHAT knowledge in the form of an ORM2-diagram, extracting instances of ontological patterns from the OWL2-ontology. The paper provides a functional and structural description of the plugin; examples of its use are given.

Keywords: WHAT-knowledge, explicit knowledge representation, ontological modeling, ontology, visual model, intermediate model, ontological pattern, ORM2-diagram