Research of intelligent teaching system based on knowledge grid

Computer-based educational systems are becoming increasingly important in all levels and types of education. Intelligent Educational Systems (IESs) are computer-based educational systems encompassing Artificial Intelligence schemes to increase the effectiveness of the learning process. In this paper, we survey patents regarding IESs. As IESs we consider either Intelligent Tutoring Systems (ITSs) or Adaptive Educational Hypermedia Systems (AEHSs) incorporating intelligent techniques. We first present indicative functions implemented in the three basic components of an IES: the domain knowledge, the user (or student) modeling unit and the pedagogical module. Afterwards, we focus on representative patents concerning IESs and present the corresponding work. At the end, we outline current and future developments regarding IESs. Keywords: Intelligent tutoring systems, adaptive educational hypermedia systems, intelligent educational systems, artificial intelligence in education, intelligent E-learning systems, intelligent computer-based training systems, artificial intelligence in Elearning

Paper mainly introduces a kind of design method of building an on-line intelligent assistant teaching system based on dynamic web language program tool. This system uses ASP language as developing-designing tools, and adopts software of Dreamwaver MX, etc in B/S structure, which completing web platform system building. In this paper, we introduce design method and realization process of system parts of sub-system function module design in course <;VB Program Design>, as well as provide some sub-system web pages and corresponding source file in detail. System design study, communication and on line test modules, as well as design homework uploading sub-system, etc, which can be replant into other course and can improve students' study interest and study initiative. In addition, system has background management module by technologies of database SQL and ADO, etc, which can realize on-line manage whole system materials easily.

With the popularization of computers and various mobile intelligent terminals, intelligent teaching systems based on learners are becoming more and more popular among learners. The above phenomenon has greatly affected and changed the current teaching quality diagnosis methods and models. However, the author found through investigation that the current intelligent teaching quality diagnosis still has different degrees of deficiencies in the design and implementation. In response to the above problems, this paper proposes a teaching quality intelligent diagnosis model based on the combination of wireless sensor networks and fuzzy comprehensive evaluation algorithms. First of all, this article is based on the wireless sensor network to link various levels of intelligent teaching systems, and constructs the information transmission structure of the teaching intelligent diagnosis system. Secondly, this article uses fuzzy comprehensive evaluation and convolutional neural network algorithms to evaluate and excavate intelligent teaching information. Finally, the model successfully passed the simulation test and simulation application, which can provide intelligent diagnosis of teaching quality for modern intelligent teaching system.

With the continuous integration and development of data mining technology and education, intelligent teaching and support technology has been more and more widely used in the field of education. In this paper, we take the field of Civics education as the research area, and carry out the design and development of the intelligent teaching aid system for Civics class, which mainly covers deep knowledge tracking and exercise recommendation. Taking the DKVMN model as the framework, a forgettable knowledge tracking model based on IRT is composed of deep forgetting and deep IRT modeling. Introducing the knowledge map in the field of Civics and Politics, mining the implicit relationship between knowledge points, and constructing DRSS, an exercise recommendation model based on deep reinforcement learning and strategy selection, we carry out experiments on the effectiveness of the application of the intelligent teaching and support system for Civics and Politics classes in this text with the students of the first-year class 1 of the Chinese language and literature major in H university culture as the research object. In the dimension of Civics cultural literacy, the students in the experimental class showed significant differences in the pre and post-test scores of the rule of law awareness, public participation, and moral cultivation dimensions (P&lt;0.05), with the scores increasing by 3.38, 2.54, and 1.53 compared to the pre-test, respectively, while in the Civics values and concepts dimensions, the difference in the scores between the pre-test and post-test in the social responsibility dimension and the idealism dimension amounted to 1.77, to 1.45, demonstrating a difference that reaches a significant level (p&lt;0.05).

In recent years, information technology marked by computer vision communication, blended learning, intelligent teaching systems (ITS), and virtual reality has gradually penetrated into the education field, and collaborative innovation has realized the gradual transformation of teaching to intelligence. The purpose of this article is to study the teaching system exploration and algorithm design of “virtual reality (VR) + visual communication design (VCD)” collaborative innovation. This article analyzes the application value of “VR + VCD” collaborative innovation in teaching. By comparing it with traditional teaching, it summarizes the advantages of “VR + VCD” technology application teaching, and proposes a VR-based Co-evolutionary teaching and learning optimization algorithm. The survey data shows that 21% of the students in the experimental class will be very active in learning after class, 60% of the students will be willing to spend time studying after class, 20% of the students in the control class will be very active in learning after class, and 54% of the students will be willing to study after class. This shows that the application of “VR + VCD” technology in the classroom can effectively increase students’ interest in learning and increase their confidence in learning.KeywordsVirtual realityVisual communication designTeaching systemCollaborative innovation

Artificial intelligence technology used in network tutoring system has led to a revolutionary effect on educational notions. This paper analyzes present tutoring system, and put forward a model of intelligent network teaching system based on agent based on study on modern teaching theory and its related application technology on computers. The module partition of intelligent tutoring system based on agent is brought forward. The prototype system is implemented based on SQL Server and .Net platform.

Centralized recommender systems can not resolve the contradiction between high recommendation quality and timely response, as well as that between limited recommendation range and ever rich information on the Web. Distributed recommender systems are expected to improve the recommendation quality while maintaining high performance. Large-scale distributed recommendation involves the coordination of various heterogeneous resources which are located on different nodes and need to be represented uniformly and organized normally into the resource space so that a semantically interactive environment can be formed. In this paper the recommendation task is defined as a knowledge based workflow and the knowledge grid is exploited as the platform for knowledge sharing and knowledge service which provides the functions of knowledge discovery, knowledge fusion and knowledge based workflow definition. The rationale of the knowledge grid based intelligent electronic recommender systems (KGBIECRS) is discussed and the service oriented architecture of the knowledge grid is presented. The knowledge grid depends on the semantic grid as the semantically interactive platform to intelligently coordinate the heterogeneous resources on the grid so that the recommendation task submitted by the knowledge grid as a knowledge based workflow can be performed intelligently and adaptively. How to implement the system is also discussed. Finally an example of travel recommendation is given to elaborate the recommendation process.

MULTIPLE REPRESENTATIONS OF KNOWLEDGE FOR TUTORIAL REASONING

The aim of the work was to demonstrate the possibility of using artificial intelligence technologies and instrumental psychodiagnostics tools to ensure the effective operation of cognitive learning mechanisms. The presented literary review shows the main directions of modern scientific thought in relation to the use of artificial intelligence in education. These are adaptive learning systems, whose main task is to adapt educational content within the framework of personalization of learning, and educational chatbots (virtual assistants, tutors), which assess the student’s progress, monitor what and when the student studied, collect students’ opinions about the teacher, predict educational problems and help to overcome them, predict academic results. The presented analysis of the scientific elaboration of the problem of using artificial intelligence for the tasks of ensuring the cognitive effectiveness of training showed the lack of such elaboration. The possibilities of integrating instrumental psychodiagnostics methods, such as laser Doppler vibrometry, bioradiolocation, eytracking, thermography, recognition and analysis of audio and video recordings, into intelligent educational systems have not been studied at all. The idea that the educational place of the student of the future should be provided with at least several instrumental psychodiagnostic channels has been expressed. At the same time, the artificial intelligence system must coordinate the information received for these various channels. The article presents the results of an analytical foresight study that demonstrates the possibilities of building a class of the future, intensively using the idea of cognitive-effective education, artificial intelligence technology, and modern technical means. This study analyzes the cognitive factors that negatively affect the effectiveness of learning and the possibilities of overcoming them by means of artificial intelligence and instrumental psychodiagnostics, as well as the possible impact of digitalization on cognitive learning processes. The factors are analyzed: of nonpreservation of learning knowledge in long-term memory, of non-homogenity of academic knowledge, of non-formation of the necessary information and cognitive basis of knowledge for a discipline or for further training in general, of insufficient academic attention, of lack of cognitive training, of the formation of recognition in the absence of the possibility of free actualization of knowledge, of the inefficiency of certain methods of digital education, of non-formation of knowledge explication and actualization in a dialogue, of different cognitive readiness of students in the class, of gadget-stress, of misunderstanding of reading (functional illiteracy), and others. The conclusion is made about the possibility and reality of an integrated implementation of artificial intelligence systems and of modern technical means in the educational process to reduce the negative cognitive effects of modern learning.

Towards more intelligent teaching systems

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 1562-1568ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0463Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain

The integration of biosensor technologies, like electroencephalography (EEG), has extended the limits of adaptive, intelligent education systems, offering real-time, personalized learning knowledge. This study explores the use of EEG to track and assess students’ cognitive states, allowing for the improvement of an active, adaptive English learning system that tailors content according to every student’s participation and improvement. Students’ cognitive states serve as the foundation for personalized education responses that motivate and enhance their participation. EEG data are gathered during English language testing to assess the correlation between learners’ cognitive states and their performance. Noise reduction is one of the preprocessing stages that ensures clear and pertinent data for analysis. Power spectral density (PSD) for feature extraction approaches is used to identify key cognitive patterns. Based on real-time EEG data, the personalized education feedback system constantly modified the course material, enhancing motivation and learning results. This research proposed a novel Dynamic Osprey Optimized Intelligent Gradient Boosting Machines (DOO-IGBM) to assess and improve the efficiency of an adaptive intelligent education system. The findings suggest that EEG-based adaptive systems make it possible to significantly progress English learning by offering personalized education paths based on brain activity to other conventional algorithms with 98.5% accuracy, 97.7% precision, 98% recall, and 98.6% F1-score. These outcomes provide precious insights and data to support the future development of adaptive, intelligent education systems for language learning.

There has been much discussion among academics on how pupils may be taught online while yet maintaining a high degree of learning efficiency, in part because of the worldwide COVID-19 pandemic in the previous two years. Students may have trouble focusing due to a lack of teacher–student interaction, yet online learning has some advantages that are unavailable in traditional classrooms. The architecture of online courses for students is integrated into a system called the Adaptive and Intelligent Education System (AIES). In AIESs, reinforcement learning is often used in conjunction with the development of teaching strategies, and this reinforcement-learning-based system is known as RLATES. As a prerequisite to conducting research in this field, this paper undertakes the consolidation and analysis of existing research, design approaches, and model categories for adaptive and intelligent educational systems, with the hope of serving as a reference for scholars in the same field to help them gain access to the relevant information quickly and easily.

PurposeThe development of adaptable and intelligent educational systems is widely considered one of the great challenges in scientific research. Among key elements for building advanced training systems, an important role is played by methodologies chosen for knowledge representation. In this scenario, the introduction of ontology formalism can improve the quality of formative process, allowing the introduction of new and effective services. Ontology can lead to important improvements in the definition of courses knowledge domain, in the generation of adapted learning path and in the assessment phase. The purpose of this paper is to provide an initial discussion of the role of ontology in the context of e‐learning. It seeks to discuss the improvements related to the introduction of ontology formalism in the e‐learning field and to show a novel algorithm for ontology building through the use of Bayesian networks. Finally, it aims to illustrate its application in the assessment process and some experimental results.Design/methodology/approachA novel method for learning ontology for e‐learning is illustrated, using an approach based on Bayesian networks. Thanks to their characteristics, these networks can be used to model and evaluate the conditional dependencies among the nodes of ontology on the basis of the data obtained from student tests. An experimental evaluation of the proposed method was performed using real student data.FindingsThe proposed method was integrated in a tool for the assessment of students during a learning process. This tool is based on the use of ontology and Bayesian network. In particular through the matching between ontology and Bayesian network, it was found that our tool allows an effective tutoring and a better adaptation of learning process to demands of students. The assessment based on Bayesian approach allows a deeper analysis of student's knowledge.Research limitations/implicationsThe proposed approach needs more experimentation with other domains and with more complex ontology.Originality/valueThis paper provides an initial discussion of the role of ontology in the context of e‐learning. The improvements related to the introduction of ontology formalism in the e‐learning field are discussed and a novel algorithm for ontology building through the use of Bayesian Networks is showed. Finally, its application in the assessment process and some experimental results are illustrated.

Towards a Design of an Intelligent Educational System