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Du, Qiao; Subramanian, Murali; Pan, Daohua
doi: 10.1080/10447318.2023.2301251pmid: N/A
Abstract With the formation and growth of the company, corporate law is continuously established and enhanced. It significantly contributes to the company’s healthy growth and brings business operations inside the legal framework. As a result, corporate law education is crucial for employees, particularly directors. Fundamentally, corporate law education is a learning process. The metaverse period has increased learners’ needs for learning environments, and human-computer interaction technology will offer all-encompassing support for the smart learning environment. The board of directors needs to adequately monitor each director’s learning level as they study corporate law, which will inevitably be detrimental to the company’s long-term growth. The most efficient method to address this issue is to use eye movement data to mine different eye movement patterns, followed by an analysis of the learning state of corporate law of directors. The scanning path analysis is used to examine the similarities and differences of the directors’ eye movement behaviors during the study of corporate law to enhance the state of corporate law learning. However, the learning status of corporate law cannot be determined only by the eye tracking of directors. We employ the convolutional neural networks (CNN) -based emotion recognition model to provide the directors constructive criticism about their learning state and offer suggestions for the learning mode. The experimental results demonstrate that time series-based eye movement pattern mining can identify directors’ viewing habits, and clustering can reveal different learning strategies that can be used to evaluate directors’ corporate law learning status. Additionally, the CNN-based emotion recognition model experiment also shows that the established model has an accuracy of 97.0035% and an F1 of 0.9412 in the CASIA-FaceV5 dataset, which helps evaluate the emotions of directors when learning company law.
Alzubi, Tareq Mahmod; Alzubi, Jafar A.; Singh, Ashish; Alzubi, Omar A.; Subramanian, Murali
doi: 10.1080/10447318.2023.2206758pmid: N/A
Abstract The rise of digitalization and computing devices has transformed the educational landscape, making traditional teaching methods less productive. In this context, early and continuous user interaction is crucial for designing and developing effective learning applications. The field of Human-Computer Interaction (HCI) has seen significant technological growth, enabling educators to provide quality educational services through smart input and output channels. However, to prevent students from discontinuing their studies and help them grow their careers, a multimodal HCI approach is needed. This paper proposes a multimodal deep learning multi-layer Convolutional Neural Network (CNN) to improve the educational experience. Our designed system aims to create a promising solution for improving the educational experience and enabling educators to provide high-quality educational services to students. Our implementation results show promising real-time performances, including a high success rate in a constriction learning concept, a quality interaction experience, and enhanced educational services. We evaluated the accuracy of five multimodal inputs, including Finger Touch (FT), Hands Up (HU), Hands Down (HD), Voice Command (VC), and Click/Typing (CT). The results indicate an average accuracy of 90.8%, 87%, 88.6%, 91.8%, and 87%, respectively, demonstrating the effectiveness of our proposed approach.
Priya, K. V.; Dinesh Peter, J.
doi: 10.1080/10447318.2023.2204697pmid: N/A
Abstract The design of human-machine interfaces is more precise and demanding in the medical and healthcare industries. Medical monitoring equipment demands more consistent and effective interpretation, as well as fast and straightforward operation due to its monitoring and reference functions. Consequently, it is crucial to consider how people interact with computers when designing the interface for medical monitoring devices. Nowadays people are giving more importance to health than anything in the world. Therefore, as it is related to peoples’ safety, the architecture of human-computer communication must be carefully considered in the studies and development of high-end medical equipment. The price of training physicians and other medical professionals is rising dramatically. Most of the countries have stepped forward from the traditional medical teaching system to a more human computer interactive teaching and learning environment with innovative technologies. This article focuses on the related researches, existing HCI applications for healthcare and the application of deep neural network for disease classification. The proposed work is to develop a healthcare learning platform to offer healthcare education to both medical practitioners and also for common people. This can be implemented as Mobile apps using human-computer interface technology and also as a website with Artificial Intelligence and Machine Learning Techniques. This proposal’s primary goal is to provide anytime, everywhere access to healthcare education for physiological and medical teaching courses, consequently advancing national health care.
Wang, Dongxuan; Han, Lu; Cong, Lin; Zhu, Hongwei; Liu, Yu
doi: 10.1080/10447318.2023.2199632pmid: N/A
Abstract The rise of knowledge economy has drawn much attention to Innovation and Entrepreneurship Education (IEE). IEE is conducive to helping graduates relieve employment pressure, and also to cultivating students’ innovation ability and entrepreneurial willingness, which is also of great significance for promoting high-quality quantitative and sustainable development of social economy. However, the current IEE still has some limitations, such as the students’ insufficient entrepreneurial willingness, low innovation practice ability, and unreasonable evaluation methods of innovation and entrepreneurship teaching, which hinder the further development of IEE. Therefore, for the purpose of further promoting the development of IEE, this article studied the evaluation of innovation and entrepreneurship teaching, and proposed an innovation and entrepreneurship teaching evaluation system combining Human–Computer Interaction (HCI) and Deep Learning (DL). The evaluation system was used to evaluate the innovation and entrepreneurship teaching of Class A and Class B. The evaluation result showed that Class A’s innovation and entrepreneurship teaching evaluation score was 7; Class B’s innovation and entrepreneurship teaching evaluation score was 7.1. The evaluation of innovation and entrepreneurship teaching in Class A and Class B was still not high enough, and the teaching quality and effect still needed to be improved; the innovation and entrepreneurship teaching evaluation system combined with HCI and DL had strong operability.
doi: 10.1080/10447318.2023.2194709pmid: N/A
Abstract In order to realize the deep integration of Internet and education, and realize the efficient allocation and comprehensive coverage of educational resources, the remote language learning platform based on human-computer interaction technology is proposed. The overall architecture of the system is designed with the help of PaaS and IaaS, and the hardware and software of the system are designed according to the overall architecture. In the hardware design, Hibernate framework and SpringJDBC are used to realize the interaction between business logic layer and data layer, and the interaction of language learning function and practice function. In addition, the remote wireless communication module is designed to complete the rapid communication of the system. The learning analysis model is constructed in the software design to determine the actual online learning needs of college students, and the collaborative filtering algorithm is used to recommend online interactive learning information data, so as to realize the function of long-distance language online interactive learning system in colleges and universities. The experimental results show that with the increase of the number of users, the page jump time of the three functions such as sending information, submitting homework and asking for leave are all below 0.04 s, especially the page jump time of the function of sending information always keeps the lowest. The login or registration function takes a long time to go back to the page. The maximum value is about 0.07 s. The results show that the system can still show excellent operation ability for large-scale users, and the sending information rate is fast, and the remote online interaction effect has obvious advantages.
Qin, Fei; Sun, Qian; Ye, Yongyan; Wang, Le
doi: 10.1080/10447318.2023.2188531pmid: N/A
Abstract The experimental results of this paper showed that the final grades of most students would be improved in the class with low English proficiency in the case of textbooks of the same difficulty. However, when multimodal instruction was used, the failure rate in the second semester decreased by 13 percentage points compared with the first semester, while the high-achieving students increased by 6 percentage points. Therefore, by using the multimodal teaching method, the learning effect of students with low English proficiency from the mid-term to the end of the term has been significantly improved, which showed that the multimodal teaching method was more superior than the conventional teaching method. Before and after the use of multi-modal teaching, students in class B had a 24% increase in grades from mid-term to final, mainly because the English level of class B was generally higher.
doi: 10.1080/10447318.2023.2171536pmid: N/A
Abstract This study is an innovative study on intelligent online language teaching that is based on the development of online Chinese learning resources and learner needs. The recommendation technology of collaborative filtering algorithm is used to find, analyze and recommend online Chinese learning resources that meet the needs of learners and teachers. This study assists learners and teachers in quickly searching for various learning resources under specified conditions, analyzing the resources obtained in detail, and providing results based on machine learning and big data sample analysis. The research is structured as follows to highlight the characteristics of Chinese learning resources and the personalized demand orientation of recommendation results: (1) Based on the personalized demand orientation of learners’ language learning and the goal oriented theoretical standards of Chinese teaching, a recommendation system based on multiple collaborative filtering hybrid algorithms is designed; (2) Evaluate the operation of the recommendation system using teaching practice; (3) This article investigates the influence of online language learning from technical factors and the operation mechanism of Chinese learning resource recommendation. The results of the experiments show that this hybrid approach has some advantages when it comes to recommending Chinese learning resources. HIGHLIGHTS A system for recommendating online Chinese learning resources is constructed. The recommendation results based on learners’ personalized needs and learning goal oriented design have high accuracy. The recommendation system based on hybrid mode has a high recommendation effect. The recommendation system can find and predict learners’ learning needs. Cultural differences, mental states, and learning psychology from evaluators may lead to differences in recommendation results.
doi: 10.1080/10447318.2023.2169526pmid: N/A
Abstract Multimodal corpus is a new type of multimedia teaching aid tool that was born and widely used in the current social development and educational reform process. It is mainly based on computer technology and network technology, and uses various multimedia materials as corpus to establish a more comprehensive English database. Corpus is increasingly used in English-Chinese multimodal teaching. Combining the content of the corpus with modern information technology will help to better understand the meaning, usage and collocation of English and Chinese multimodality, and then improve the initiative of autonomous learning. Furthermore, English-Chinese multimodality is a fixed or semi-fixed programmed language, which can be directly extracted from memory according to the context during use, which can effectively improve learners’ writing efficiency and overcome the negative impact of native language transfer. Facing the task of sentiment analysis, this article has taken the text semantic representation technology based on representation learning as the main method, and carries out a series of researches from the definition of implicit emotion, the characteristics of language expression and the recognition method. Finally, in the language system of modern Chinese, it is found that there are many sentence patterns that are very similar to the middle verb structure in language expression, but the efficiency is still improved by 3.6%. Compared with traditional methods, the English-Chinese multimodal sentiment corpus method based on artificial intelligence can better reflect the application value of language, which also helps to feel the real context, change the way of thinking, and make the way of thinking more suitable for the language awareness of native speakers, thereby improving the vividness of language expression.
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