Intelligent Learning Environment Research Articles

Research on the Innovative Pathway of ChatGPT Enabling Rural Music Teaching from the Perspective of the Metaverse

This study delves into the innovative integration of ChatGPT within the metaverse context, specifically for rural music education. The metaverse, a converging virtual space, offers unprecedented opportunities for enhancing teaching methods. However, creating engaging and effective digital resources remains a challenge. This paper explores how ChatGPT's advanced natural language processing capabilities can be harnessed to construct dynamic and interactive music teaching materials tailored to rural learners. The proposed approach aims to foster a more immersive, interactive, and intelligent learning environment, ultimately elevating the quality of music education in rural areas.

Lifelong Learning System in the age of Artificial Intelligence: Change, Opportunity and Reconstruction

Artificial Intelligence advances the offline and online education, digital education and virtual education, eventually all the way to lifelong education with online, digital and virtual Characteristics. In the era of “Artificial Intelligence” (AI), computer technology, Internet technology, big data technology, and cloud computing technology have been widely used in all life and work, making the information dissemination space broader and faster. Information Space provides a platform for new knowledge acquisition and learning for education. The lifelong learning system for the whole population has been updated to break through the modes and limitations of traditional education and meet the needs of all people. This paper first analyzes the connotation and characteristics of lifelong learning for all and points out the current policies and opportunities. Finally, it proposed to build a high-quality, efficient, and sustainable comprehensive lifelong learning system to form an excellent social learning situation in which "everyone can learn, everywhere can learn, and always can learn.” To enhance lifelong learning, efforts focus on expanding the main body responsible for learning, building intelligent learning environments, and providing precise guidance based on educational needs. It is also essential to improve feedback mechanisms, service places, and create an integrated online platform, alongside establishing knowledge-based organizations. This research focus on accountability and quality in lifelong learning since it is evolving with new technologies and opportunities. At the same time, it can expand learning, create an intelligent environment, improve feedback, and establish an online educational platform. This will provide inclusive and high-quality educational opportunities to all learners.

Intelligent Analysis System for Teaching and Learning Cognitive Engagement Based on Computer Vision in an Immersive Virtual Reality Environment

The 20th National Congress of the Communist Party of China and the 14th Five Year Plan for Education Informatization focus on digital technology and intelligent learning and implement innovation-driven education environment reform. An immersive virtual reality (IVR) environment has both immersive and interactive characteristics, which are an important way of virtual learning and are also one of the important ways in which to promote the development of smart education. Based on the above background, this article proposes an intelligent analysis system for Teaching and Learning Cognitive engagement in an IVR environment based on computer vision. By automatically analyzing the cognitive investment of students in the IVR environment, it is possible to better understand their learning status, provide personalized guidance to improve learning quality, and thereby promote the development of smart education. This system uses Vue (developed by Evan You, located in Wuxi, China) and ECharts (Developed by Baidu, located in Beijing, China) for visual display, and the algorithm uses the Pytorch framework (Developed by Facebook, located in Silicon Valley, CA, USA), YOLOv5 (Developed by Ultralytics, located in Washington, DC, USA), and the CRNN model (Convolutional Recurrent Neural Network) to monitor and analyze the visual attention and behavioral actions of students. Through this system, a more accurate analysis of learners’ cognitive states and personalized teaching support can be provided for the education field, providing certain technical support for the development of smart education.

Multi-modal Emotion Recognition Using Neural Networks: Deploy on Cloud

This paper explores recent developments in emotion recognition, focusing on methodologies employing Multilayer Perceptron (MLP) classifiers. Emotion recognition is crucial for human-machine interaction across various domains, including healthcare, surveillance systems, and intelligent learning environments. There are many deep learning models that have been employed for emotion detection, this emphasizes the effectiveness of MLP classifiers. The project at hand centers on predicting emotions such as happiness, calmness, fear, and disgust solely utilizing MLP classifiers. The system is trained on audio data, with recordings matched in frequency, time, and speed to the raw data used for training. Additionally, Convolutional Neural Network (CNN) models are used here to analyze facial expressions in real-time video streams by providing neat and complete approach to emotion recognition. Upon deployment on the AWS cloud platform, the system provides real-time emotion predictions in textual format via a web interface created using Flask. Furthermore, the system offers the capability to analyze emotions from live video streams, presenting continuous feedback on emotions through a face-outlined box updated every three seconds. This functionality serves to confirm individuals' emotions and provide self-awareness regarding their emotional states. This project focuses on giving efficiency by using MLP classifiers in conjunction with CNN models for emotion recognition tasks, thereby contributing to the advancement of human-machine interaction. Key Words: MLP Classifier, CNN, AWS Cloud, Flask, emotion, audio, video

Effects of colored lighting on learning processes: Towards a smart classroom

Colour in the classroom, either in isolation or in conjunction with other environmental factors, is an element that has been widely discussed in the scientific literature, albeit not systematically. On the other hand, its evolution towards coloured light, made possible in recent years by light emitting diode (LED) technology, has hardly found a place among researchers. Despite the progressive rise of smart classrooms, intelligent learning environments or references by some authors to "dynamic lighting", its analogous concept, "dynamic colour", hardly appears in reviews of the scientific literature. This exploratory, quasi-experimental study shows how coloured light affects the learning process in primary school classroom environments, integrating cognitive processes, instrumental learning and affective processes, and helping to define the dynamic potential of its use. The experimental part has been carried out for four weeks, collecting data that are analysed descriptively, comparatively with the control group and in the internal variance of the experimental group. The results show positive influence tendencies in the cognitive and affective processes of the students, although not so much in instrumental learning.

Construction of Dependent Learning Mode for College Students Based on Intelligent Learning Environment

With the guidance of advanced information technology, education in China is constantly evolving, and both the teaching methods of educators and the learning methods of students are constantly being updated and optimized. Constructing a dependent learning mode for college students under the intelligent learning environment not only enhances their dependent learning ability, but also breaks the limitations of time and space in traditional teaching, enabling various educational resources to be fully utilized and shared. This paper elaborates on the concept of intelligent learning environment and the core elements of dependent learning, analyzes the significance of dependent learning for college students, and proposes a strategy for constructing a dependent learning mode for college students based on intelligent learning environment for reference.

The Exploration of the “Four-Fold Nature and One Price” Intelligent Learning Environment for Digital Circuit Courses

AI-based educational models are gradually receiving widespread attention and application with the rapid development of information technology. In the course of digital circuits, by combining modern technology with educational theories, constructing an intelligent learning environment can provide students with more efficient and personalized learning experiences. This article explores the significance and challenges of building an intelligent learning environment in the digital circuits course. It combines the characteristics of intelligent education that differentiate it from traditional education and constructs methods and strategies for creating a “four attributes, one value” intelligent learning environment from the perspectives of diversity, adaptability, autonomy, contribution, and optimized instructional assessment.

智慧学习环境下高等数学课堂教学实践研究

智慧学习环境下高等数学课堂教学实践研究

Exploring the Ideas of Integrating the Teaching of Information Technology and English Translation

Abstract The intelligent use of digital teaching resources and the deep penetration of information technology can help promote the change and development of English teaching and improve the sharing, interactivity, interestingness and effectiveness of English translation teaching resources. This paper develops an intelligent classroom teaching model for English translation that combines scaffolding and multimodal discourse teaching using the U Campus teaching platform. To create an intelligent learning environment for English translation, this paper uses distributed crawler technology to crawl English teaching resources and Minhash algorithm to de-weight and optimize the data, constructs an English translation corpus, and introduces a lightweight Transformer network to establish a machine translation model. This paper quantitatively analyzes the teaching effect of the English translation smart classroom teaching model using data from University C students as the research object. The results of the study show that the students in class A1 scored 95.02 points in topic comprehension, the translation theory and skills improved by 8.46 points compared with the students in class A2, and there was a significant difference between the students’ cross-cultural communication skills at the 1% level. By integrating the information technology platform with English translation teaching, students’ translation skills can be enhanced, they can learn more English resources, and their intercultural knowledge and competence can be improved.

Mining User-Object Interaction Data for Student Modeling in Intelligent Learning Environments

Mining User-Object Interaction Data for Student Modeling in Intelligent Learning Environments

Intelligent VR technology empowers the construction of intelligent learning environment for preschool education of kindergartens with emblematic characteristics

Abstract Due to the current kindergarten preschool education being increasingly emphasized, this paper combines intelligent technology to explore, firstly, analyzes the current intelligent learning environment model and designs the intelligent learning environment based on human-computer interaction. Secondly, the emblem elements are integrated into the physical learning environment, combined with intelligent VR technology, gesture interaction recognition based on feature vectors and pose interaction recognition based on vector machines to realize the construction of the smart learning environment. Finally, the interaction performance in the smart learning environment is analyzed based on simulation experiments, and the changes in learning effects are explored. The application yields that young children’s independent ability and social interaction ability increased by 0.5 and 0.48, respectively, and the mathematical thinking ability, artistic creation ability and Hui culture cognitive ability increased by 0.35, 0.39 and 0.32, respectively.

A Hybrid Education Quality Assessment Method based on Heuristic Optimization Algorithm

Hybrid education is the most effective process of education, which is the mixture of two education processes that help increase the student’s understanding level. In the study, it has been discussed that the intelligent learning environment influences the education style, which allows the students and the educator to understand the topic correctly. Also, from the study, a good bond between the teacher and the students increases the achievement rate in an education system. Therefore, the professional life of the students is also more developed. The study aims to consider the disadvantages of the modern or hybrid education system, and the communication gap is one of the significant disadvantages of education.

Development managing stages of hybrid intelligent learning environment for student’s mathematics research activities

Problem and aim. The search for relevant solutions to the restructuring of the architecture of modern education, elements of a new system through the organization of an intellectual learning environment adapted to the research activities of schoolchildren on the development of complex mathematical knowledge is a research problem. Materials and methods. 52 teachers and 234 students of schools in Moscow (Russian Federation) took part in the survey. Diagnostic methods were aimed at obtaining data on the use of various digital resources and tools in everyday learning activities and the level of their intellectualization. The methods of descriptive statistics were used (variation series, histograms, measures of central tendency and scatter), as well as Pearson's χ2-test. The results of the study. About 90% of the surveyed teachers and 80% of schoolchildren spoke in favor of the idea of introducing intelligent systems into the learning process, but more than 75% of respondents cannot give examples of such systems and environments. The opinion of trainees on the introduction of digital tools into the educational process differs significantly from the opinion of their mentors (χ2 emp=33,4> χ2 cr (0,01)=2,65). The main stages of designing an intelligent learning system are presented: the development of methodological provisions for the intelligent management of the educational process; determination of personalized parameters of scientific potential that determine the structure of educational and research content; development of a database and knowledge of the subject of project activities; choice of architecture and functionality of the hybrid neural network. Conclusion. The author's methodological and technological solutions will provide scientists with a reference point for further research in the field of improving high-tech forms of e-learning, and teachers will have the opportunity to explore practical ways to implement personalized and adaptive distance learning through automated intelligent systems. Keywords: intelligent systems, research activities, teaching mathematics, complex knowledge

A strategy for building a smart sports platform based on machine learning models

With the rapid development of big data technology, it has greatly changed the way people get information, and also improved the speed and quality of information. In this context, smart sports has become a new trend in sports development. This paper creates an intelligent learning environment and builds a smart sports platform through advanced concepts and technical means, which can effectively optimize the integration and sharing of sports resources. Starting from the overall architecture design of smart sports, the key technologies of machine learning model to realize smart sports are sorted out. Through the five basic linking stages with machine learning model as the core, the value innovation path of platform construction structure is analyzed. The current status of sports resources application is studied, and the data mining algorithm is used to calculate the user usage data of the smart sports platform and improve the construction of the smart platform. Through the construction of the smart sports platform, people shift from traditional reading books and watching TV programs to getting information through intelligent mobile terminals, and the proportion of attention to sports information is as high as 58.6%. This shows that by building a smart sports platform, it can provide support and guarantee for the sustainable development of sports.

TOWARDS SELF-ADAPTIVE LEARNING PLATFORM IN FUTURE CLASSROOM BASED ON EMOTION COMPUTING

The accurate recognition of the emotion of learners is the basis of harmonious emotional interaction in the intelligent learning environment of the future classroom. This project focuses on the lack of research and practical exploration on harmonious emotional interaction in the future classroom, and constructs the database of learning pictures and expression pictures of learners by employing Convolution Neural Network. Endowing the future classroom with a self-adaptive interaction property at the emotional level by emotion computing can promote to learn effectively. The proposed platform can self-adaptively adjust the visual characteristics of learning picture according to the expression of learners, visual emotion preference and learning picture emotion, and then present the adapted learning picture to learners in real time, so as to realize the adaptive interaction at the emotional level in the future classroom.

Automated detection of learning stages and interaction difficulty from eye-tracking data within a mixed reality learning environmen

PurposeConstruction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.Design/methodology/approachThe study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.FindingsThe classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.Research limitations/implicationsThe findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.Originality/valueThe classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

Analysing adaptive scaffolds that help students develop self‐regulated learning behaviours

AbstractBackgroundProviding adaptive scaffolds to help learners develop effective self‐regulated learning (SRL) behaviours has been an important goal for intelligent learning environments. Adaptive scaffolding is especially important in open‐ended learning environments (OELE), where novice learners often face difficulties in completing their learning tasks.ObjectivesThis paper presents a systematic framework for adaptive scaffolding in Betty's Brain, a learning‐by‐teaching OELE for middle school science, where students construct a causal model to teach a virtual agent, generically named Betty. We evaluate the adaptive scaffolding framework and discuss its implications on the development of more effective scaffolds for SRL in OELEs.MethodsWe detect key cognitive/metacognitive inflection points, that is, moments where students' behaviours and performance change during learning, often suggesting an inability to apply effective learning strategies. At inflection points, Mr. Davis (a mentor agent in Betty's Brain) or Betty (the teachable agent) provides context‐specific conversational feedback, focusing on strategies to help the student become a more productive learner, or encouragement to support positive emotions. We conduct a classroom study with 98 middle schoolers to analyse the impact of adaptive scaffolds on students' learning behaviours and performance. We analyse how students with differential pre‐to‐post learning outcomes receive and use the scaffolds to support their subsequent learning process in Betty's Brain.Results and ConclusionsAdaptive scaffolding produced mixed results, with some scaffolds (viz., strategic hints that supported debugging and assessment of causal models) being generally more useful to students than others (viz., encouragement prompts). Additionally, there were differences in how students with high versus low learning outcomes responded to some hints, as suggested by the differences in their learning behaviours and performance in the intervals after scaffolding. Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's Brain can be further improved to better support SRL behaviours and narrow the learning outcomes gap between high and low performing students.ImplicationsThis paper contributes to our understanding and impact of adaptive scaffolding in OELEs. The results of our study indicate that successful scaffolding has to combine context‐sensitive inflection points with conversational feedback that is tailored to the students' current proficiency levels and needs. Also, our conceptual framework can be used to design adaptive scaffolds that help students develop and apply SRL behaviours in other computer‐based learning environments.

An Empirical Study of College English Smart Teaching Driven by Intelligent Technology

With the continuous integration of artificial intelligence technology and foreign language education, the entire educational ecology, including traditional foreign language education concepts, education models, management structures and governance systems, is undergoing profound changes. This study uses the Rain Classroom Smart Teaching Platform for online-and-offline foreign language teaching, providing a virtual intelligent learning environment for learners. This pedagogy supports the construction of a learning community through the establishment of an online learning space, which seamlessly connects different learning scenarios and forms a multi-way interactive relationship between teachers and students. The questionnaire survey showed that students were more satisfied with this hybrid smart teaching module (88. 78) and were significantly more motivated to learn English (87. 6). The results of the pre- and post-tests conducted before and after the teaching experiment showed a significant increase in students’ scores ( p<. 05), confirming the effectiveness of the teaching model. This study has implications for the practice of EFL teaching in higher education.

5G Network in Content Based Emotion Detection by Sentimental Analysis Integrated with Opinion Mining and Deep Learning Architectures

The rapid growth of social networking sites in the Internet era has made them a necessary tool for sharing emotions with the entire world. To extract emotions from text, a variety of tools and approaches are available in fields of opinion mining as well as sentiment analysis. These researches propose novel technique opinion mining based emotion detection from the input social content using deep learning architectures. Here the input has been obtained as social media content based on opinion miningby 5G networks. The input has been processed for noise removal, smoothening and normalization. This processed input has been segmented using Markov model based convolutional neural networks (MMCNN). The segmented data has been classified using Canonical Correlation AnalysisBayesian neural network.An opinion mining method that analyzes statements regarding computer programming and predicts or recognizes their polarity was implemented, along with an earlier module that was integrated into an intelligent learning environment. These three steps made up the creation of the module. We assessed the corpus, text polarity precision, and emotion recognition. Experimental analysis has been carried out for various social media content collected by opinion mining in terms of accuracy, precision, recall, F-1 score, AUC.Proposed technique attained accuracy of 99%, precision of 96%, recall of 96%, F-1 score of 95%, AUC of 89%.

Interactive Response System to Promote Active Learning in Intelligent Learning Environments

This research used a comparative quasi-experimental design to investigate the impacts of an IRS in the ILE on students’ academic performance, cognitive load, and satisfaction with the lesson. A total of 31 middle school students were divided into the experimental group and the control group. Mann–Whitney U tests yielded three major results. (1) Significant differences in academic performance were noted between the two groups. The knowledge test scores of comprehensive questions showed significant differences between the two groups but those of the strict memorization questions did not. Students in the experimental group had overall higher knowledge test scores than those in the control group. (2) Significant differences in cognitive load were observed between the two groups; specifically, students in the experimental group had lower total cognitive load than those in the control group. (3) Significant differences in satisfaction with the lesson were observed between the two groups; specifically, students who studied with the IRS were more satisfied with the lesson than those who studied without the IRS. These findings imply that the IRS is a useful tool that could promote active learning amongst students in ILEs and help to promote teaching and learning efficiency in ILEs.