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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...

引用

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...

引用

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 ...

引用

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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Wireless Instant Fault Detection through Finite-Element Trained Machine learning

Wireless Instant Fault Detection through Finite-Element Trai...

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international conference on computing, Networking and Communications (ICNC)

作者： Bienz, Kalon M. Ghalamchi, Behnam Calif Polytech State Univ San Luis Obispo Dept Elect Engn San Luis Obispo CA 93407 USA Calif Polytech State Univ San Luis Obispo Dept Mech Engn San Luis Obispo CA USA

ISBN: (纸本)9798350371000;9798350370997

The rapid development of Industry 4.0 technologies has brought predictive maintenance into focus, particularly for small and medium-sized enterprises (SMEs) where cost and complexity are major barriers. In this paper, we present an innovative approach to vibration analysis, a key component for fault detection in mechanical systems and the creation of digital twins. Utilizing MatLab, we generated synthetic data points to simulate various vibration scenarios. These synthetic data points served as the training set for our machine learning model. The trained model was then integrated with a lowcost, Bluetooth-enabled accelerometer for real-time monitoring. Our system successfully identified fault conditions, specifically lump mass irregularities, through real-time sensor data. Our findings show promising capabilities for offering a cost-effective and straightforward solution for predictive maintenance. This research not only advances the field of vibration analysis but also opens doors for SMEs to embrace the benefits of digital twin technologies.

关键词： Wireless Communication Vibration Analysis Predictive Maintenance Machine learning Synthetic Dataset

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Uniformly Distributed Data Effects in Offline RL: A Case Study in Gridworld Setting

Uniformly Distributed Data Effects in Offline RL: A Case Stu...

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international conference on Big Data and Smart computing (BigComp)

作者： Tokayev, Kuanysh Park, Jurn-Gyu Nazarbayev Univ Sch Engn & Digital Sci Astana Kazakhstan

ISBN: (纸本)9798350370027;9798350370034

In the emerging landscape of off-policy reinforcement learning (RL), challenges arise due to the significant costs and risks tied to data collection. To address these issues, there is an alternative path for transitioning from off-policy to offline RL, known for its fixed data collection practices. This stands in contrast to online algorithms, which are sensitive to changes in data during the learning phase. However, the inherent challenge of offline RL lies in its limited interaction with the environment, resulting in inadequate data coverage. Hence, we underscore the convenient application of offline RL, 1) starting from the collection of a static dataset, 2) followed by the training of offline RL models, and 3) culminating with testing in the same environment as off-policy RL methodologies. This involves the utilization of a uniform dataset gathered systematically via non-arbitrary action selection, covering all possible states of the environment. Utilizing the proposed approach, the Offline RL model employing a Multi-Layer Perceptron (MLP) achieves a testing accuracy that falls within 1% of the results obtained by the off-policy RL agent. Moreover, we provide a practical guide with datasets, offering valuable tutorials on the application of Offline RL in Gridworld-based real-world applications. The guide can be found in this GitHub(1) repository.

关键词： Offline RL Data Distribution Deep learning DQN Machine learning Tutorial

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A Review of Dynamic RRA Techniques on 5G And Beyond Mobile Networks 27

A Review of Dynamic RRA Techniques on 5G And Beyond Mobile N...

引用

27th IEEE/ACIS international conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed computing (SNPD)

作者： Nokane, Boikobo Isong, Bassey Masonta, Moshe North West Univ Comp Sci Dept Mafikeng South Africa North West Univ UDSC Mafikeng South Africa Council Sci & Ind Res CSIR Pretoria South Africa

ISBN: (纸本)9798350391961;9798350391954

The 5G mobile network aims to enhance wireless communication by providing faster and reliable connectivity. Open radio access network (RAN) architecture, which offers flexibility and innovation in Radio Resource Allocation (RRA), is central for optimal network performance. However, traditional RRA methods fall short of meeting the complex demands of 5G due to scalability issues. Incorporating machine learning (ML) techniques into open RAN can enhance adaptability and intelligence, ensuring that 5G networks meet high performance and service quality standards. This paper presents a comprehensive review of ML-based and traditional RRA methods in meeting the evolving demands of wireless networks. Literature from relevant articles were selected and analysed to highlight the techniques used, trends, strengths, and limitations. The findings reveal the potential and transformative impact of ML on the future of wireless communications, particularly in achieving the key performance indicators and quality of service expected from 5G and beyond networks. It also shows that research in ML-based RRA methods is at its infancy stage and more research is needed to advance the technology.

关键词： RRA 5G QoS Machine learning Mobile network

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Surrogate-Assisted Multi-Class Collaborative teaching and learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

引用

2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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Empowering Education: AI and IoT Integration for Smart learning Environments 15

Empowering Education: AI and IoT Integration for Smart Learn...

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15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sharma, Divya Gupta, Shruti Dagar, Savita Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9798331300579

To improve learning environments and educational outcomes, this article examines how artificial intelligence (AI) and the Internet of Things (IoT) meet in the context of educational settings. Teachers may design intelligent learning environments that cater to the requirements of each individual student, offer individualised learning experiences, and support more productive teaching methods by combining AI and IoT technologies. This paper explores the possible advantages of AI and IoT integration in education, such as better learning analytics, more efficient resource allocation, and more student engagement, through a thorough analysis of previous research and case studies. Additionally, the study addresses issues like digital literacy, privacy, and data security that come with implementing AI and IoT in educational settings. By combining the most recent understanding and perspectives. © Grenze Scientific Society, 2024.

关键词： Contrastive learning

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