Intelligent Educational Systems Research Articles

Intelligent speech elderly rehabilitation learning assistance system based on deep learning and sensor networks

Recently, deep learning has been proved to significantly improve the quality of speech recognition. Convolutional neural networks are often used in speech recognition tasks because of their special network structure and powerful learning function. In order to solve the problem that the traditional convolutional neural network can not reflect the one-dimensional basic attributes of speech signal, this paper proposes to set the number of frames for one dimension of convolution kernel, and use one-dimensional model and two-dimensional convolutional network model for speech recognition. By moving the convolution kernel of time axis and frequency band, it can adapt to the time change of speech signal and maintain the correlation between frequency bands to a great extent. At the same time, this paper also discusses the speech signal preprocessing, feature parameter extraction and regularization algorithm. Due to the lack of hospital resources, the lag of information technology, the poor ability of accompanying and other reasons, the current accompanying service for elderly rehabilitation can not meet the needs of elderly patients. The rapid development and wide use of information technology provide opportunities for the optimization of health services for the elderly. In view of the difficulty of word memory and interpretation in current rehabilitation courses, a VR intelligent teaching system based on intelligent voice technology is developed. The application results show that the system can effectively improve the ability of language expression and word writing. At present, the system of intelligent speech function has not been completed, and lacks speech synthesis function. The next research will focus on the use of speech synthesis technology, in order to realize the man-machine dialogue between people and the system, and show a more real training situation.

THE EVOLUTION OF LEARNING: A SURVEY OF AI TECHNOLOGIES IN EDUCATION

AI technology has revolutionized education, simplifying management, enabling personalized learning, and enhancing student engagement. From initial Intelligent Teaching Systems (ITS) to advanced adaptive learning algorithms, AIs impact on education has been profound. This piece delves into AIs history in schools and its applications, including personalized learning, language education, and administrative efficiency. While AI offers benefits like automated tasks and 24/7 student support, it also raises concerns such as discrimination and privacy breaches. Balancing these advancements with ethical considerations is crucial to ensure equitable and responsible implementation of AI in education.

Using explainable AI to unravel classroom dialogue analysis: Effects of explanations on teachers' trust, technology acceptance and cognitive load

AbstractDeep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI‐powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty‐nine pre‐service teachers were recruited and randomly assigned to either a treatment (n = 30) or control (n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI‐powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI‐powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning‐based models in the context of classroom dialogue analysis.Practitioner notesWhat is already known about this topic Classroom dialogue is recognized as a crucial element in the teaching and learning process. Researchers have increasingly utilized AI techniques, particularly deep learning methods, to analyse classroom dialogue. Deep learning‐based models, characterized by their intricate structures, often function as black boxes, lacking the ability to provide transparent explanations regarding their analysis. This limitation can result in teachers harbouring distrust and underutilizing these models. What this paper adds This paper highlights the importance of incorporating explainable AI approaches to tackle the interpretability issues associated with deep learning‐based models utilized for classroom dialogue analysis. Through an experimental study, this paper demonstrates that providing model explanations enhances teachers' trust in and technology acceptance of AI‐powered classroom dialogue models, without increasing their cognitive load. Teachers express satisfaction with the model explanations provided by explainable AI. Implications for practice and/or policy The integration of explainable AI can effectively address the challenge of interpretability in complex AI‐powered models used for analysing classroom dialogue. Intelligent teaching systems designed for classroom dialogue can benefit from advanced AI models and explainable AI approaches, which offer users both automated analysis and clear explanations. By enabling users to understand the underlying rationale behind the analysis, the explanations can contribute to fostering trust and acceptance of the AI models among users.

Unlocking Vital Professional Skills of EFL Educators to Cultivate Student English Core Competencies in the Digital Era

With the rapid development of artificial intelligence (AI) technology, the education sector has witnessed transformative changes. In the domain of secondary English language teaching, the advent and implementation of intelligent teaching systems have necessitated a reevaluation of Teacher Professional Competency (TPC). However, there is limited research exploring English as a Foreign Language (EFL) teachers’ perspectives on the TPCs required for fostering student English core competencies in the ear of AI. This study aimed to investigate the need for relevant TPCs among English teachers. A sample of 1089 English teachers from 335 schools across China participated in this research. The findings identified a total of 12 TPCs, with variations observed based on gender, years of teaching experience, school type, school level, and school location. Notably, Teaching Design Competency (TDC), English Knowledge and Application Competency (EKAC), and Teaching Implementation and Management Competency (TIMC) were identified as the most crucial TPCs. These results provide valuable insights into the components of EFL teacher professional competencies and offer guidance for competency-based teacher digital education programs.

The role of artificial intelligence in building an adaptive educational environment

   Purpose: of the article is to study the influence of artificial intelligence on the construction of an adaptive educational environment that takes into account the positive and negative impact of artificial intelligence, as the core of digital technologies, on the well-being of participants in the educational process.   Methods: the work uses a complex of theoretical methods, including axiomatic, formalization, abstraction, logical analysis, historical retrospection. The most signifi cant practical research methods include statistical, operationalization and evaluation, comparative analysis.   Results: the article provides a statistical substantiation of the demand for artifi cial intelligence in the education. The current educational problems are shown, in solving which it is advisable to use artifi cial intelligence. The article describes the trends in the use of artificial intelligence in the Russian education. The interpretation of the adaptive educational environment is given and the possibilities of using artificial intelligence as a tool for its construction are shown. The logical sequence for constructing an adaptive intelligent teaching system is proposed. The analysis of the impact of digital technologies used in hybrid learning (including artifi cial intelligence) on the well-being of participants in the educational process was carried out. The need for a rational, balanced and cautious approach to the use of artifi cial intelligence in education is emphasized, which gives rise to numerous ethical problems, ignoring which can negatively aff ect the values of education.   Conclusions and Relevance: in the context of digitalization of the economy and society, the key technology of which is artifi cial intelligence, education is facing technological challenges that force it to adapt to new operating conditions. The penetration of artifi cial intelligence into the field of education is the pattern of scientific and technological progress that cannot be resisted. The positive aspects and threats of using artificial intelligence in education should be critically assessed, and informed decisions should be made on this basis. Considering the specifics of the educational environment in which the foundation of personality is laid, and the need to preserve the values of education as a factor of cultural progress, priority in goal-setting and the meanings of education should remain with a person whose assistant can be artifi cial intelligence.

Network Ideological and Political Education System for College Students Based on Multimedia Service Architecture

Today, the network society has become an integral part of human society, and the network life marked by digitization, informatization and networking has become a new state of existence; a new society marked by interactivity, virtuality and innovation A mode of operation has been created. A network, in simple terms, is to connect, and can be called a computer network. Such highest degree of social informatization in my country. It is a new idea designed by making full use of computer networks, multimedia technology and modern communication technology and closely combining the cognitive hotspots of contemporary college students. way of political education. In response to the above problems, this article uses deep learning technology to develop an intelligent education system based on detection technology. The system takes identifying whether the standard is met as the main goal, and gives comprehensive evaluation and improvement suggestions for the quality of action completion. It gets rid of the constraints of the venue.

Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems

Cognitive diagnosis assessment is a fundamental and crucial task for student learning. It models the student-exercise interaction, and discovers the students' proficiency levels on each knowledge attribute. In real-world intelligent education systems, generalization and interpretability of cognitive diagnosis methods are of equal importance. However, most existing methods can hardly make the best of both worlds due to the complicated student-exercise interaction. To this end, this paper proposes a symbolic cognitive diagnosis~(SCD) framework to simultaneously enhance generalization and interpretability. The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters. Meanwhile, the accompanying challenge is that we need to tunnel the discrete symbolic representation and continuous parameter optimization. To address this challenge, we propose to hybridly optimize the representation and parameters in an alternating manner. To fulfill SCD, it alternately learns the symbolic tree by derivative-free genetic programming and learns the student and exercise parameters via gradient-based Adam. The extensive experimental results on various real-world datasets show the superiority of SCD on both generalization and interpretability. The ablation study verifies the efficacy of each ingredient in SCD, and the case study explicitly showcases how the interpretable ability of SCD works.

Zero-1-to-3: Domain-Level Zero-Shot Cognitive Diagnosis via One Batch of Early-Bird Students towards Three Diagnostic Objectives

Cognitive diagnosis seeks to estimate the cognitive states of students by exploring their logged practice quiz data. It plays a pivotal role in personalized learning guidance within intelligent education systems. In this paper, we focus on an important, practical, yet often underexplored task: domain-level zero-shot cognitive diagnosis (DZCD), which arises due to the absence of student practice logs in newly launched domains. Recent cross-domain diagnostic models have been demonstrated to be a promising strategy for DZCD. These methods primarily focus on how to transfer student states across domains. However, they might inadvertently incorporate non-transferable information into student representations, thereby limiting the efficacy of knowledge transfer. To tackle this, we propose Zero-1-to-3, a domain-level zero-shot cognitive diagnosis framework via one batch of early-bird students towards three diagnostic objectives. Our approach initiates with pre-training a diagnosis model with dual regularizers, which decouples student states into domain-shared and domain-specific parts. The shared cognitive signals can be transferred to the target domain, enriching the cognitive priors for the new domain, which ensures the cognitive state propagation objective. Subsequently, we devise a strategy to generate simulated practice logs for cold-start students through analyzing the behavioral patterns from early-bird students, fulfilling the domain-adaption goal. Consequently, we refine the cognitive states of cold-start students as diagnostic outcomes via virtual data, aligning with the diagnosis-oriented goal. Finally, extensive experiments on six real-world datasets highlight the efficacy of our model for DZCD and its practical application in question recommendation. The code is publicly available at https://github.com/bigdata-ustc/Zero-1-to-3.

Role of Artificial Intelligence in Higher Education- An Empirical Investigation

The importance of artificial intelligence (AI) is growing in all economic sectors and thus also in higher education. In recent years, there have been significant developments in this concept of "Artificial Intelligence in Education (AIED)". The purpose of this study was to find out how the concept of artificial intelligence can be applied to teaching and learning in higher education and the implications of the use of artificial intelligence in higher education. The impact of the development of technologies on learning is often studied on the methods and scope of learning and teaching. Artificial intelligence enables higher education services to become easily accessible with extraordinary speed, not only in the classroom but also outside the classroom. This report seeks to explore how AI will become an integral part of universities and seeks to examine its immediate and future impact on various aspects of higher education. The challenges of implementing AI in these institutes were also explored. As artificial intelligence (AI) research in education increases, many researchers in the field believe that the role of teachers, schools and leaders in education will change. In this regard, the aim of this study is to investigate which are the possible scenarios for the arrival of artificial intelligence in education and what impact it can have on the future of schools. In this research, it confirmed that artificial intelligence has been widely adopted and used in various forms in education, especially educational institutions. Artificial intelligence was initially implemented in the form of computers and computer-related technologies, moving to web-based and web-based intelligent educational systems, and finally with the use of embedded computing systems and other technologies such as humanoid robots and web-based chatbots teachers & tasks and assignments independently or with tutors. With these platforms, teachers could perform various administrative tasks such as grading and Work more effectively and efficiently and achieve higher quality in your learning activities. On the other hand, because the systems use machine learning and adaptability, the curriculum and content are adapted which improved uptake and retention, which improved the student experience and the overall quality of education.

Improving recognition accuracy for facial expressions using scattering wavelet

One of the most evident and meaningful feedback about people’s emotions is through facial expressions. Facial expression recognition is helpful in social networks, marketing, and intelligent education systems. The use of Deep Learning based methods in facial expression identification is widespread, but challenges such as computational complexity and low recognition rate plague these methods. Scatter Wavelet is a type of Deep Learning that extracts features from Gabor filters in a structure similar to convolutional neural networks. This paper presents a new facial expression recognition method based on wavelet scattering that identifies six states: anger, disgust, fear, happiness, sadness, and surprise. The proposed method is simulated using the JAFFE and CK+ databases. The recognition rate of the proposed method is 99.7%, which indicates the superiority of the proposed method in recognizing facial expressions.

Physical education teaching mode assisted by artificial intelligence assistant under the guidance of high-order complex network

This study explores the integration of artificial intelligence (AI) teaching assistants in sports tennis instruction to enhance the intelligent teaching system. Firstly, the applicability of AI technology to tennis teaching in schools is investigated. The intelligent teaching system comprises an expert system, an image acquisition system, and an intelligent language system. Secondly, employing compressed sensing theory, a framework for learning the large-scale fuzzy cognitive map (FCM) from time series data, termed compressed sensing-FCM (CS-FCM), is devised to address challenges associated with automatic learning methods in the designed AI teaching assistant system. Finally, a high-order FCM-based time series prediction framework is proposed. According to experimental simulations, CS-FCM demonstrates robust convergence and stability, achieving a stable point with a reconstruction error below 0.001 after 15 iterations for FCM with various data lengths and a density of 20%. The proposed intelligent system based on high-order complex networks significantly improves upon the limitations of the current FCM model. The advantages of its teaching assistant system can be effectively leveraged for tennis instruction in sports.

Empowering Learning through Intelligent Data-Driven Systems

The evolution of educational systems is closely tied to technological advancements, particularly the emergence of machine learning. This technology offers a sophisticated system capable of predicting, explaining, and influencing behavior. Many efforts have aimed to integrate machine learning into education, focusing on specific cases using ad-hoc models. This paper introduces an intelligent educational system that relies on data-driven student models, aiming to surpass the limitations of these ad-hoc systems. The approach outlined in this endeavor adopts a comprehensive and methodical modeling methodology centered on machine learning techniques. By employing Long Short-Term Memory (LSTM), the proposed approach enables predictive student models based on historical educational data. The effectiveness of this method was tested through experimentation on an intelligent tutoring system using 5-fold cross-validation, revealing that the smart educational system achieved a remarkable 96% accuracy rate. Furthermore, a comparison between the importance scores of features with and without the student models demonstrated the practicality and effectiveness of the proposed methodology.

Exploration of Intelligent Teaching Means of Ideological and Political Education in Colleges and Universities Under the Background of “Mass Entrepreneurship”

The concept of “great ideological and political education” requires comprehensive ideological education to better achieve the educational goals of “innovation and entrepreneurship.” This article analyzes and explores the intelligent teaching methods of ideological and political education in universities under the background of “innovation and entrepreneurship,” and constructs an intelligent teaching system of ideological and political education in universities under this background. The experimental results show that the stability of the system reaches 95.2%, and the learning efficiency of students has been improved by 11.5%. This work provides important application guidance for big data analysis and IoT sensor data.

Learning consistent representations with temporal and causal enhancement for knowledge tracing

Knowledge tracing is a crucial component of intelligent educational systems and deep learning technologies have significantly propelled its advancement. However, most existing models suffer from the incomplete data modeling problem of knowledge tracing, leading to inconsistent representations of students’ actual knowledge states. This paper proposes a new approach called Temporal- and Causal-enhanced Knowledge Tracing to improve the consistency of students’ knowledge state representations in intelligent educational systems. In particular, our method introduces a causal self-attention mechanism based on front-door adjustment theory, which improves interaction representation and reduces prediction errors caused by dataset bias. To effectively integrate the features of interval and response times into our model, we further use forget and input gates to simulate knowledge forgetting and acquisition, respectively. This maintains consistent learning behavior representations and improves model predictions. The results of our extensive experiments on the three datasets demonstrate that our method outperforms previous knowledge-tracing methods in predicting student scores.

Speech recognition enhancement based on wireless network sensors application in interactive intelligent teaching system

The speech function used in the multi-dimensional speech recognition model based on MTL and RNN is the commonly used MFCC function, and in feature extraction, after various filtering and conversion, deleting part of the speech information is a requirement for 3D audio recognition. Use multidimensional information as much as possible. Based on this, this article not only brings the development of convolutional neural network intelligent teaching, but also proposes a new education model. Heuristic education like flipped classroom is still the content of education research in recent years, and it is important to the current education form in China. It is mainly based on the traditional lecture mode or the classroom form. This article focuses on undergraduate education. It is designed in the classroom based on the video data of the undergraduate interactive activities and the interactive system. It has important research significance and practicality. At the same time, the interactive content of the traditional teaching method is used to perform voice recognition, and the recognition effect of the main body of the interactive system is further improved through the voice information. The spatial perspective is used to divide the lens perspective into seminars and experimental forms, and the pixel change value of each frame provides a specific reference for it to determine the reflection of student interaction activities and the evaluation of student seminars. This article introduces the implementation process of interactive content and interactive activities of the teaching system in the classroom, introduces the speech recognition framework of the MARS Chinese teaching system, uses the model principle, and compares the general speech data set test with the actual ARST model audio. The results show that, MAS model performance is more advantageous.

Intelligent System in Education: Requirements and Design Method

Intelligent systems in education have proven to be highly beneficial in supporting self-learning among students, particularly in remote learning scenarios. This study proposes the necessary requirements for an intelligent educational system that serves two primary functions: querying course knowledge and evaluating learner proficiency through multiple-choice testing. Furthermore, this study builds a solution to design the knowledge base, inference engine, and tracing system based on a knowledge model that integrates ontology and knowledge graph.

Approach for Enneagram personality detection for Twitter text: a case study

Understanding people’s emotions and orientations attracts researchers nowadays. Current personality detection research concentrates on models such as the big five model, the three-factor model. The Enneagram is deeper than these models for providing a comprehensive view. This theory is a unique personality model because it illustrates what drives human behavior. This recognition helps in building smarter recommendation systems and intelligent educational systems. Enneagram personalities are realized through a long questionnaire-based test. People are not concerned about doing a test because it is time-consuming. A proposed case study employs Twitter’s text to detect Enneagram personality because it requires no time or effort. The proposed case study is based on an approach that uses a combination of ontology, lexicon, and statistical technique. This proposed case study uses the biography description text and 40 tweets of a Twitter profile text. The highest probability percentage is peacemaker personality which is 15.58%. This result means that the identified personality is the peacemaker. The outcome is equivalent to the determination of the Enneagram’s specialized people. This result promises more positive outcomes. This is the first automated approach to determine the Enneagram from text.

Artificial intelligent based teaching and learning approaches: A comprehensive review

<span lang="EN-US">The goal of this study is to investigate the potential effects that Artificial intelligence (AI) could have on education. The narrative and framework for investigating AI that emerged from the preliminary research served as the basis for the study’s emphasis, which was narrowed down to the use of AI and its effects on administration, instruction, and student learning. According to the findings, artificial intelligence has seen widespread adoption and use in education, particularly by educational institutions and in various contexts and applications. The development of AI began with computers and technologies related to computers; it then progressed to web-based and online intelligent education systems; and finally, it applied embedded computer systems in conjunction with other technologies, humanoid robots, and web-based chatbots to execute instructor tasks and functions either independently or in partnership with instructors. By utilizing these platforms, educators have been able to accomplish a variety of administrative tasks. In addition, because the systems rely on machine learning and flexibility, the curriculum and content have been modified to match the needs of students. This has led to improved learning outcomes in the form of higher uptake and retention rates.</span>

Intelligent Teaching of Reading and Writing Skills in Higher Education Languages by Integrating Data Mining Technology

Abstract In this paper, we first construct a reading comprehension model based on BERT and EDA strategies and, at the same time, propose sliding-window dynamic cropping to reduce the chapter material to a range that BERT can adapt. Secondly, the TF-IDF model for writing material sentences is proposed, and the algorithm is further improved using TFC. A material sentence quality evaluation mechanism is proposed to extract the most useful sentences in writing based on Bi-Attention. Then, the intelligent teaching system is built and analyzed functionally. Finally, a controlled experiment is established to evaluate the intelligent teaching model. According to the study, the experimental class’s reading comprehension scores increased from 23.83 to 33.25, while the control class’s scores did not improve significantly. As for writing, the five aspects of writing improved in the experimental class, and the posttest percentages were 1.0, 0.9, 0.8, 0.8, 0, and 0.98, respectively. Intellectualized teaching can improve both reading comprehension and writing ability.

Using Descriptive Logic in Intelligent Educational Systems

The purpose of the article is to study and solve the problems of using the basic principles of the modern formalism of descriptive (terminological) logic in intelligent learning systems that use knowledge bases of educational content. The research methods are the main methodological approaches and technological tools for the development of intelligent learning systems. In particular, systemic and comparative analyses to identify the features of intelligent learning systems, the method of expert evaluation, which involves the analysis of literature and information resources, interviews and surveys of experts in the field of information technology and mathematical logic, as well as the process of developing and testing an intelligent system. The scientific novelty of the study is the development of a system for obtaining expert knowledge for an intelligent system that combines the formation of an expert knowledge base, and the formalism for representing which is descriptive logic. The proposed approach of formalized knowledge representation using descriptive logic can be used in the development of authors’ intelligent learning systems in the field of online education and cognitive skills development. Conclusions. The scope of application of descriptive logic in intelligent systems is constantly expanding, increasing the capabilities of these systems to formally derive new knowledge, including in conditions of incomplete certainty. Descriptive logic, used in intelligent learning systems, provides ample opportunities for building a conclusion (i.e., forming an answer), in particular in situations such as forming and answering various types of queries to the system’s knowledge base (e.g., fully defined and clearly formulated, incompletely defined and vaguely formulated, completely vague, incompletely defined and clearly formulated, completely vague, completely vague and vaguely formulated), helping to specify the query. To do this, it is necessary to formulate a rigorous number system (to build an axiomatic theory). Thus, as a means of solving semantic and logical problems, descriptive logic has become a unique and powerful mechanism for representing knowledge. The successful use of descriptive logic in the author’s intelligent educational system will ensure the improvement of the quality of the educational process; monitoring of the learning process and control of its results; will facilitate the integration of this system into many modern information educational systems; will provide various consultations, advice, interpretations, explanations regarding the educational content or educational process, as well as a wide range of topics of educational content and tests of different levels.