# Intelligent Educational Environments: Recent Trends, Modeling, and Applications
## 5 Results
### Practical Applications of Intelligent Educational Systems
#### 5.3.1 Personalized Teaching and Learning  
For the automatic conduct of personalized learning and tutoring, the IES stores knowledge about learners in profiles (class _Profiles_) and manages this knowledge. Several mechanisms for storing structured knowledge and methods of its usage in organizing the tutoring process (reasoning mechanisms) are used. Various knowledge modeling methods are applied to develop students' models, domain models, tutoring, and expert models in IES, including ontology-based, knowledge graph-based methods [55], mathematical or simulation models, and data-driven methods. Knowledge-based models have built-in or use reasoning mechanisms, including model-based reasoning [40], case-based reasoning [56], and rule-based reasoning [57]. A combination of these approaches is usually applied. Ontology-based knowledge models used by the class _EducationalTechnology_ are the primary way to store knowledge in a machine-processable way and, in this way, support automated reasoning. Ontologies are used as knowledge sources, supporting the following tasks in IESs: (i) natural language processing (for text analysis or students' natural language input) [57]; providing (ii) adaptive learning paths (based on ongoing assessments); (iii) personalized and adaptive teaching-learning; (iv) domain knowledge representation allowing explicit revealing of the relationships between concepts; (v) improved semantic understanding by utilizing the structured relationships in ontologies; and (vi) content generation, annotation, recommendation, and reuse (class _Authoring_). The described tasksare implemented by _TeachingLearning_ and _EducationalTechnology_ classes with the help of _ResourceLibrary_ and _DigitalRepository_ classes (Figure 7). Personalization in an intelligent educational system allows adaptation to each student's learning knowledge, skill level, needs, and style. Personalization of the teaching-learning process includes mapping the student's profiles (learning styles, preferences, skills, etc.) with learning resources. It should be dynamic in the context of changes in some student characteristics, such as knowledge level and learning needs. Hence, an appropriate tutoring model for storing these characteristics and performance in the learning process is essential. The system collects data on student participation, time spent on tasks, specific tasks or overall performance, etc. The integrated IoT devices such as motion sensors, cameras, and microphones regularly provide data for the presence and behavior of students in the smart classrooms, i.e., for their involvement in the educational process. In the UML model (Figure 7), these devices are represented by the base class _IoTDevice_ and its subclasses, which connect to the system using instances of _IoTGateway_. These data are used to timely inform both students and tutors to help them identify trends, predict outcomes, or suggest interventions. The IES utilizes data gathered within learning interactions (class _Interaction_), processes them using the class _Analytics_, which supply learning analytics algorithms and other intelligent technologies, and provides them to the _TeachingLearning_ and _EducationalTechnology_ classes. The latter handles intelligent tutoring, adaptation, and personalization. This functionality enables the IES to obtain insights into students' strengths, weaknesses, and learning patterns. Thus, the students' performance is assessed to determine where they are struggling, and the educational approaches, learning content, or other parameters of the teaching process are adjusted accordingly. Based on the students' learning interactions and outcomes, the system can change the applied teaching approach or activities and recommend specific educational resources from _ResourceLibrary_ and _DigitalRepository_ utilizing ontology representation of knowledge. For example, if students cope with some problems/tasks quickly, they might be presented with more challenging ones; otherwise, the IES could offer simpler tasks or supplementary explanations.