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Using a Virtual Reality Tool for Experimental learning in engineering. A Survey of Learner Preferences

Using a Virtual Reality Tool for Experimental Learning in En...

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IEEE international conference on teaching, Assessment and learning for engineering (IEEE TALE)

作者： Molina-Cristobal, Arturo Shah, Jolly Atit Lim, Idris Hussain, Sajjad Univ Glasgow Sch Engn Singapore Singapore Natl Univ Singapore Coll Design & Engn Singapore Singapore Univ Glasgow Sch Engn Glasgow Lanark Scotland

ISBN: (纸本)9781665453318

engineering laboratories are designed to provide 'real' hands-on experience to the students. However, building laboratories that are suitable to serve a large student population is challenging due to the limited resources. We propose that unsupervised laboratory activities be conducted with virtual reality (VR) tools to support existing laboratories infrastructure or aid lecture delivery with VR experiments. The VR based laboratory permits students to interact virtually with the equipment and connect theoretical concepts with virtual experiments. We have designed a sample of undergrad VR laboratory lessons for students in engineering programs at the University of Glasgow Singapore and the Singapore Institute of Technology (SIT). In this paper, our study aims to obtain student feedback through structured questionnaires to identify what characteristics of the VR technology implementation enhance student engagement in an engineering laboratory provision. We present findings from the questionnaire on the learners' preferences. In particular, our findings identify that although the VR tool's characteristics are engaging, students find the implementation a cumbersome process to access VR lessons. Finally, we provide some recommendations for future implementations.

关键词： Virtual Reality engineering Education Kolb's learning cycle

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Developing an AI-Assisted Multilingual Adaptive learning System for Personalized English Language teaching 10

Developing an AI-Assisted Multilingual Adaptive Learning Sys...

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10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Lawrance, Joy Christy Sambath, Priya Shiny, Crispine Vazhangal, Mubashira Prema, S. Bala, B. Kiran Psgr Krishnammal College for women Department of English Coimbatore India Karunya Institute of Technology and Sciences Department of English Coimbatore India Krupanidhi Degree College Department of Englisli Bengaluru India Panimalar Engineering College Department of English Chennai India K.Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798350384369

This research focuses on the design and development of an innovative AI -assisted multilingual adaptive learning system tailored for personalized English language instruction. The proposed system leverages artificial intelligence algorithms to provide a dynamic and personalized learning experience that caters to individual proficiency levels, learning styles, and linguistic backgrounds. The system incorporates advanced Natural Language Processing (NLP) techniques to analyze and understand the unique challenges faced by learners from different linguistic backgrounds. By harnessing the power of machine learning, the system adapts its instructional content, pace, and exercises to suit the specific needs of each learner. The AI component facilitates real-time feedback, allowing learners to receive targeted guidance on pronunciation, grammar, and vocabulary usage. The multilingual aspect is achieved through the integration of translation services and language-specific adaptations within the learning modules. The system employs data-driven insights to track learner progress, identify areas of improvement, and continuously refine the personalized learning experience. Through the integration of adaptive learning technologies, the system evolves with each user interaction, enhancing its effectiveness over time. The proposed NLP method outshines its counterparts in terms of flexibility, scoring impressively at 98.12%, signifying its superior adaptability to diverse tasks and data structures. © 2024 IEEE.

关键词： Contrastive learning

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Performance Evaluation of Multi-Access Edge computing for Blended learning Services 21

Performance Evaluation of Multi-Access Edge Computing for Bl...

引用

21st international learning and Technology conference, L and T 2024

作者： Barakat, Mohammed Saeed, Rashid A. Edam, Salaheldin Elnaim, Areeg Ali Nasar, Ibrahim Sudan University of Science and Technology School of Electronics Engineering Khartoum Sudan Taif University Department of Computer Engineering Taif21944 Saudi Arabia Alneelain University College of Engineering and Communication Khartoum Sudan Icon Training Company Training and Consultancy Makkah Saudi Arabia

ISBN: (纸本)9798350393569

Multi-access Edge computing (MEC) is a technology that enables the deployment of cloud computing capabilities at the edge of the network. It is an emerging technology that has been identified as a key enabler for 6G networks. Blended learning is an educational approach that combines traditional face-to-face instruction with online learning activities, and MEC is a technology that enables the delivery of cloud computing services at the edge of a network This paper examines the potential of blended learning over Multi-Access Edge computing (MEC) for improved educational outcomes. Blended learning is a combination of online and traditional classroom instruction, and MEC is an emerging technology that enables the delivery of cloud computing services at the edge of the network. The paper reviews existing literature on blended learning and MEC and discusses how they can be used together to improve educational outcomes. Finally, it provides recommendations for further research in this area. The paper concludes that blended learning over MEC has great potential to improve educational outcomes, but more research is needed to understand how best to implement it. © 2024 IEEE.

关键词： Edge computing

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Design and Development of an Intelligent Student learning Prediction Model using Enhanced Deep learning Principles

Design and Development of an Intelligent Student Learning Pr...

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2024 international conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Indumathi, A. Uma, N. Saktheeswari, R. Sugacini, M. Kavishree, S. Sukant, B. Sri Venkateswara College of Engineering Department of Information Technology Pennalur Tamilnadu Sriperumbudur602117 India

ISBN: (纸本)9798331543617

The Intelligent Student learning Prediction Model using Enhanced Deep learning Principles (EDLP) platform is a major leap in the domain at the intersection of educational technology and artificial intelligence. We present a new, predictive model that combines AlexNet (a state-of-the-art convolutional neural network architecture for image recognition) with a very deep DNN tailored to analyze diverse data types from traditional academic records as well as complex input based on pictures. By taking advantage of these strengths-high-precision image-based feature extraction using AlexNet, and comprehensive data analysis with a DNN-the EDLP model is designed to make the most accurate predictions yet regarding student learning *** research provides a comprehensive description of the methodologies used to develop the EDLP model, which includes data preprocessing, feature engineering, and their articulation with machine learning technologies. This is importantly on application of the model instituted a prediction rate of 97.6%, which indeed marked a prompted potential revolution in educational assessment and personalized learning strategies. This level of predictive accuracy, despite being more oriented towards the very effective profiling and prediction of the needs of a single student, places more emphasis on the great value the model offers as a tool to help educators and policy-makers be effective in improving teaching and learning. Thus, the implications of EDLP also relate not only to its ability to predict academic performance but are equally important regarding learning success-influencing factors as well as related intervention strategies. © 2024 IEEE.

关键词： Prediction models

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The Intelligent Generation of College Schedule Based on Machine learning 6

The Intelligent Generation of College Schedule Based on Mach...

引用

6th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2024

作者： Zhang, Chunling Heilongjiang University of Technology Heilongjiang Jixi158100 China

ISBN: (纸本)9781837242672

In order to solve the problems of heavy workload and low efficiency of college resources scheduling. This article adopts reinforcement learning technology to schedule courses on a weekly week. The core of this method is the reward mechanism. By exploring the combination of hard conflict rewards and soft conflict rewards, we can find out the best action at which the average of the cumulative rewards reach maximum with each step, so as to generate the optimal schedule. In terms of computing power, the hybrid and parallel strategy is adopted. The process of learning and convergence is more stable and faster. The generated schedule performs well in many indicators, such as classroom utilization rate, the extent of the difference between the daily teaching time and the average. It has certain application value. © The Institution of engineering & Technology 2024.

关键词： Contrastive learning

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Opportunities and Challenges of AI to Support Student Assessment in computing Education: A Systematic Literature Review 16

Opportunities and Challenges of AI to Support Student Assess...

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16th international conference on Computer Supported Education, CSEDU 2024

作者： dos Santos, Simone C. Junior, Gilberto A.S. Centro de Informática Federal University of Pernambuco Recife Brazil

ISBN: (纸本)9789897586972

This study investigates how Artificial Intelligence (AI) can support student assessment in computing education through a systematic literature review of twenty studies from the past decade. AI's evolution has significantly impacted various fields, including education, offering advanced capabilities for personalized teaching, continuous evaluation, and performance prediction. Analysing these studies, evidence showed a focus on undergraduate students and the employment primarily face-to-face teaching methods, with engineering education and serious games being more cited contexts. These studies also reveal AI's potential to create personalized learning experiences using techniques like fuzzy logic, KNN algorithms, and predictive models to analyse student interactions and performance, particularly in educational games and online courses. The positive findings demonstrate AI's effectiveness in classifying students' learning profiles, predicting employability, providing real-time assessments, facilitating targeted interventions, and improving learning outcomes through personalization. Automated assessments via AI have been shown to reduce teachers' workload by offering accurate, real-time feedback. However, the studies also highlighted challenges concerning student engagement, teacher material quality, model generalization, and technical obstacles such as natural language processing, algorithm stability, and data cleaning. These data-driven factors emphasize the necessity for further advancements in AI to enhance continuous and effective student assessment as part of the personalized learning process. Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Students

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The Application of University Computer Network Course teaching Platform Based on Moodle+LAMP Technology 1

The Application of University Computer Network Course Teachi...

引用

1st IEEE international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Zhu, Yinghui Jiang, Yuzhen Hanshan Normal University School of Computer and Information Engineering Chaozhou521041 China

ISBN: (纸本)9798350323795

In order to solve the shortcomings of Moodle's question bank exercise activity module and make it better meet the specific needs of this course, a college computer network course teaching platform based on Moodle + lamp technology is proposed. Using Moodle's development technology and lamp to design and develop the exercise subsystem of the question bank, using the knowledge of software engineering to increase the possibility of its implementation, and finally designed and implemented the system, and investigated the implementation effect. Compared with the original question bank exercise module in four aspects, the system has improved and convenient topic import function, supports comprehensive random volume issuance, the operation is simple and fast, and improves the flexibility of the system. The result finding that the 'feedback' activity was launched in the Moodle platform, and a simple survey was conducted on 40 students participating in the teaching experiment, and 36 answered, indicating that the teaching of College Computer Foundation Based on Moodle + lamp environment is effective. Most students are willing to accept this kind of learning, it proves that the platform improves the course activity function supported by the teaching platform and helps students learn the course better. © 2023 IEEE.

关键词： Students

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Exploring the Fusion of Mixed Reality and Digital Game-Based learning: The Case of Puzzle Box Games for Education

Exploring the Fusion of Mixed Reality and Digital Game-Based...

引用

IEEE international conference on teaching, Assessment and learning for engineering (IEEE TALE)

作者： Lee, Boon Giin Tang, Huimin Wen, Xinlei Univ Nottingham Ningbo China Sch Comp Sci Ningbo Peoples R China

ISBN: (纸本)9781665453318

The traditional offline teaching landscape has faced unprecedented challenges due to the COVID-19 pandemic, leading to the emergence of online teaching as a novel educational trend. However, online teaching has certain limitations. It lacks robust interaction between teachers and students, making it challenging for teachers to evaluate students' real-time progress in learning. Moreover, online classrooms often suffer from low engagement, causing students to easily lose focus, resulting in reduced student autonomy and unsatisfactory learning outcomes. In recent years, various studies have proposed game-based learning approaches that integrate instructional content and knowledge. These methods effectively reshape the conventional "teacher-led, passive learning" model and address the issue of limited student participation in the classroom. This approach proves advantageous in fostering students' diverse and comprehensive qualities. To accomplish this objective, the study aims to investigate the fusion of mixed reality technology and digital game-based learning, drawing inspiration from the concept of a puzzle box. The design of virtual digital puzzles revolves around user-centered human-computer interaction, encompassing elements such as user experience satisfaction, interface design for mixed reality technology, and the presentation format in which puzzles serve as the primary medium. The study has demonstrated that students generally exhibit motivation to embrace the utilization of mixed reality puzzle box games for educational purposes. This motivation arises from the allure of gaming elements and the ease of interaction with technology, which contributes to an enhanced learning experience.

关键词： digital puzzles educational technology game-based learning mixed reality puzzle box

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Product Competitor Analysis for First-Year engineering Students

Product Competitor Analysis for First-Year Engineering Stude...

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IEEE international conference on teaching, Assessment and learning for engineering (IEEE TALE)

作者： Maneeratana, Kuntinee Pongsugitwat, Suvanchai Tonanon, Nattaporn Thongnuek, Peerapat Preuksarattanawut, Charasphat Phongtongjalearn, Sirikanya Hoonsopon, Danupol Chulalongkorn Univ Dept Mech Engn Bangkok Thailand Chulalongkorn Univ Dept Met Engn Bangkok Thailand Chulalongkorn Univ Dept Chem Engn Bangkok Thailand Chulalongkorn Univ Interdisciplinary Program Biomed Engn Bangkok Thailand Chulalongkorn Univ Fac Engn Bangkok Thailand Chulalongkorn Univ Chulalongkorn Business Sch Dept Mkt Bangkok Thailand

ISBN: (纸本)9781665453318

This paper presented a learning model for product competitor analysis to first-year engineering students. Students were assigned to watch a video which contained an example before class. In a 45-minutes allotted class time, students were grouped, on-site or online, and assigned to complete an exercise on the product and direct competitor of their choice. The exercise focused on the 4Ps model for marketing implementation - product, price, place, and promotion - of the product and competitor and concluded with the strength, weakness, and unique value proposition of the product. The works were usually rough but acceptable. Students were generally satisfied and 86% found the exercise at least useful in the subsequent design thinking projects. In the future, the ChatGPT could be used to increase the working speed and, thus, reduced the most common complaint of insufficient time while increasing the work quality.

关键词： 4Ps competitor analysis design thinking hybrid learning synchronous class

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Construction and Application of Threshold Judgment Abnormal learning Behavior Monitoring Model Based on Unsupervised learning Technology 14th

Construction and Application of Threshold Judgment Abnormal ...

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14th international conference on Frontier computing, FC 2024

作者： Wang, Fengze Wang, Jing School of Information and Business Management Chengdu Neusoft University Sichuan Chengdu China

ISBN: (纸本)9789819627936

As an important part of digital education, online autonomous learning is expanding its application scope and depth. However, the abnormal learning behaviors associated with it are increasing day by day. Allowing this behavior to develop is obviously harmful to the healthy development of online learning. This paper attempts to put forward a solution of monitoring characteristic index of abnormal learning behavior in online learning system based on unsupervised learning technology, which is used to support the analysis of abnormal data in online learning and to support normal teaching activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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