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检索条件"任意字段=International Conference on Teaching and Learning in Computing and Engineering"

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Students' Reflective learning in the Prototyping Process in STEM Education

Students' Reflective Learning in the Prototyping Process in ...

引用

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

作者： Hou, Chunyu Huang, Biyun Jong, Morris Siu-Yung Luk, Eric Tsun-Hin Chai, Ching-Sing Chinese Univ Hong Kong Ctr Learning Sci & Technol Hong Kong Peoples R China Chinese Univ Hong Kong Dept Curriculum & Instruct Hong Kong Peoples R China

ISBN: (纸本)9781665453318

This paper aims to present and discuss a study that we conducted to probe into junior secondary students' learning experiences in the prototyping process in STEM education, particularly their reflective experiences. In the course of developing their prototypes, the participants (N=75) were required to engage in reflecting on their learning process at the beginning, mid, and end, using the same open-ended questionnaire. Grounded theory analysis was employed to analyze the qualitative data collected through the questionnaire. In addition to unveiling their desirable learning experiences in the prototyping process, this research also revealed the challenges and difficulties they encountered, the corresponding support they needed, and the reflective changes they experienced. The findings showed that integrating reflective learning into the prototyping process is instrumental in understanding students' learning experiences in STEM education.

关键词： reflective learning prototyping project-based learning group-based learning design education

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Exploration of Professional Master's Talent Cultivation in Service computing under the New Situation 2

Exploration of Professional Master's Talent Cultivation in S...

引用

2nd IEEE international conference on Software Services engineering (SSE)

作者： Mo, Tong Li, Ziyu Li, Weiping Peking Univ Sch Software & Microelect Beijing Peoples R China

ISBN: (纸本)9798350368529;9798350368512

In recent years, with the rapid development of artificial intelligence technology, new services represented by the combination of large models and fields have become a strong driving force for social, technological, and economic development. The new situation has brought tremendous changes to the technical connotation of service computing, and has also brought new requirements and challenges to the cultivation of professional talents in service computing. Based on the objective laws of talent cultivation, the School of Software and Microelectronics at Peking University has conducted a series of explorations and innovations in the teaching practice of service computing, targeting the characteristics of the engineering field and the unique needs of new engineering disciplines. By setting up specialized research directions and using a modular curriculum system as the content support for targeted teaching, and through a series of characteristic training links integrating industry and education, a trinity training model of "direction content process" has been formed. This training model is highly popular among students, and the popularity of enrollment and application has been increasing year by year. The talents cultivated have also received praise from the industry.

关键词： service computing professional master's degree talent cultivation artificial intelligence and model innovation

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Leveraging ChatGPT to Enhance Computational Thinking learning Experiences

Leveraging ChatGPT to Enhance Computational Thinking Learnin...

引用

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

作者： Ouaazki, Abdessalam Bergram, Kristoffer Holzer, Adrian Univ Neuchatel Informat Management Inst Neuchatel Switzerland

ISBN: (纸本)9781665453318

Given the pervasive reliance on technology in modern society, teaching Computational Thinking (CT) abilities is becoming increasingly relevant. These abilities, such as modeling and coding, have become crucial for a larger audience of students, not only those who wish to become software engineers or computer scientists. Recent advances in Large Language Models (LLMs), such as ChatGPT, provide powerful assistance to complete computational tasks, by simplifying code generation and debugging, and potentially enhancing interactive learning. However, it is not clear if these advances make CT tasks more accessible and inclusive for all students, or if they further contribute to a digital skills divide, favoring the top students. To address this gap, we have created and evaluated a novel learning scenario for transversal CT skills that leveraged LLMs as assistants. We conducted an exploratory field study during the spring semester of 2022, to assess the effectiveness and user experience of LLM-augmented learning. Our results indicate that the usage of ChatGPT as a learning assistant improves learning outcomes. Furthermore, contrary to our predictions, the usage of ChatGPT by students does not depend on prior CT capabilities and as such does not seem to exacerbate prior inequalities.

关键词： Large Language Models Interactive learning Environments Collaborative learning Human-Computer Interaction

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Problem-based learning: A Catalyst for Teacher and Student Motivation in K-12 Schools

Problem-based Learning: A Catalyst for Teacher and Student M...

引用

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

作者： Badjie, Ousman Velankar, Yogesh Amrita Vishwa Vidyapeetham Amrita Sch Sustainable Futures Kollam Kerala India

ISBN: (纸本)9781665453318

The conventional, teacher-centered ways of teaching have given way to more contemporary, participatory techniques. Active learning, problem-based learning (PBL), and collaborative activities are becoming more prominent in order to increase student engagement. These methods support deeper comprehension and long-term information retention while fostering critical thinking, problem-solving, and cooperation. Therefore, through a critical review of relevant literature, this paper presents the implementation process of PBL and the opinion of scholars on its impact on teacher and student motivation in K-12 settings. It is revealed that students who are motivated are often more involved in their learning process and produce superior academic results. Teachers are crucial in influencing the next generation and the level of instructional delivery is largely impacted by their commitment as well as motivation. It is critical to identify and address the motivating elements for teachers and students in the application of instructional methods. Therefore, this paper also creates a pathway for further empirical research.

关键词： motivation problem-based learning students teachers K-12 schools

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Education and teaching Research of IoT Embedded Subject under the National Vocational and Technical Skills Standards 6

Education and Teaching Research of IoT Embedded Subject unde...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Yanglu Wang, Xingchen Yadav, Amit Shah, Abhishek Pratap College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Darwin Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to teach), faculty team (who teaches), and teaching evaluation (how well the teaching is) under the current status of embedded teaching on the Internet of Things (IoT) engineering discipline. Guided by the latest National Occupational Technical Skill Standards for IoT engineering professionals and in accordance with the educational philosophy of Chengdu Neusoft University, here we propose a design for teaching system for IoT embedded talent courses, clarified the key elements and formulated new ideas for the current development of talents in this direction to assist improvement and perfection of related curriculum systems, and to promote the establishment and operation of practical and technological activity platforms. It aims to provide practical references for the construction of professionalism in education and teaching standards of IoT. © 2025 IEEE.

关键词： Curricula

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Enhancing E-learning Accessibility through AI(Artificial Intelligence) and Inclusive Design 6

Enhancing E-learning Accessibility through AI(Artificial Int...

引用

6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Chandramma, R. Babu, Kunda Suresh Ranjith, K.S. Sinha, Akash Kumar Neerugatti, Vikram Somashekhara Reddy, D. Global Academy of Technology Karnataka Bangalore India Narasaraopeta Engineering College Andhra Pradesh Palnadu India Theresa Institute of Engineering and Technology Bangalore India Karnataka Bangalore India

ISBN: (纸本)9798331522667

E-learning has revolutionized the world of teaching and learning by providing learning possibilities for everyone. Making sure that these opportunities are fully inclusive to all learners, including those with disabilities, is the goal of this paper. This paper discusses the application of artificial intelligence and principles of inclusive design to enhance accessibility in e-learning with the ultimate goal of providing all learners with equal access to educational resources. Several applications of AI technologies hold great potential in making this transformation. The NLP and speech recognition tools can convert speech to text and text to speech, benefiting students with auditory or speech impairments. In general, adaptive learning systems with AI can produce personalization in educational content for the students regarding their learning styles and needs, which makes it possible for every student to interact efficiently with the material. Equally, these technologies offer descriptive audio to the visually impaired student and identify inaccessible elements within the educational materials. AI chatbots and virtual assistants support students in every step of their learning process, walking them through intricate concepts by dealing with their questions[9]. In fact, despite its potential to drive innovation, data privacy, technical limitations, and implementation costs are some of the challenges that AI currently faces. Addressing these challenges will be very important in achieving the full potential of AI-enhanced, inclusive e-learning. The current paper underscores the importance of AI and inclusive design toward the creation of an educational environment that is genuinely accessible and equitable for all learners. © 2025 IEEE.

关键词： Contrastive learning

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Immersive learning Constructs for Deaf and Hard-of-Hearing Students in TVET Practical Skill

Immersive Learning Constructs for Deaf and Hard-of-Hearing S...

引用

2024 international Visualization, Informatics and Technology conference, IVIT 2024

作者： Abdul Rahim, Rini Hafzah Mohd Nizam, Dinna Nina Mohd Naim, Nur Faraha Baharum, Aslina Universiti Malaysia Sabah User Experience Group Faculty of Computing and Informatics Sabah Kota Kinabalu Malaysia Sunway University Computing and Information System School of Engineering and Technology Selangor Malaysia

ISBN: (纸本)9798350354447

The advent of advanced educational technologies, and immersive learning utilizing virtual reality (VR), augmented reality (AR), and mixed reality (MR) have emerged as a powerful tool to enhance learning experiences. However, traditional approaches often lack the ability to address the unique needs of Deaf and Hard of Hearing (DHH) students, particularly in practical skill development. The usage of immersive learning for the purpose of DHH students is still limited, especially in Malaysia. This study explores critical elements of immersive learning environments designed to facilitate practical skill acquisition for DHH students. Using the systematic literature review (SLR) determines immersive practical skill learning elements from selected 34 papers to help DHH students learn more effectively. 30 constructs are highlighted in the SLR results as recommendations for creating immersive learning for DHH practical skills. It is thus intended that the highlighted features would contribute to both the process of teaching and learning as well as the enhancement of student performance. © 2024 IEEE.

关键词： Contrastive learning

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learning Gaussian Operations and the Matchgate Hierarchy 5

Learning Gaussian Operations and the Matchgate Hierarchy

引用

2024 international conference on Quantum computing and engineering

作者： Cudby, Josh Strelchuk, Sergii Univ Cambridge Ctr Math Sci DAMTP Cambridge CB3 0WA England

ISBN: (纸本)9798331541378

learning an unknown quantum process is a central task for validation of the functioning of near-term devices. The task is generally hard, requiring exponentially many measurements if no prior assumptions are made on the process. However, an interesting feature of the classically-simulable Clifford group is that unknown Clifford operations may be efficiently determined from a black-box implementation. We extend this result to the important class of fermionic Gaussian operations. These operations have received much attention due to their close links to fermionic linear optics. We then introduce an infinite family of unitary gates, called the Matchgate Hierarchy, with a similar structure to the Clifford Hierarchy. We show that the Clifford Hierarchy is contained within the Matchgate Hierarchy and how operations at any level of the hierarchy can be efficiently learned.

关键词： Quantum computing Matchgates Tomography learning

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FOGFLEET: Fog-Level Federated Transfer learning for Adaptive Transport Mode Detection 12

FOGFLEET: Fog-Level Federated Transfer Learning for Adaptive...

引用

12th international conference on Cloud engineering

作者： Kamalian, Mahdieh Taherkordi, Amir Payberah, Amir H. Ferreira, Paulo Univ Oslo Oslo Norway KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9798331528690;9798331528706

Transport Mode Detection (TMD) systems play a pivotal role in facilitating applications in transport, urban planning, and more. Exploiting the advancements in smartphone sensing capabilities, TMD systems have evolved for mobile applications with local classification on smartphones as a common approach. Yet, local approaches relying on centralized training raise privacy concerns due to the transmission of sensitive data (e.g., GPS logs) over the Internet. In this paper, we propose FOGFLEET, a novel Federated Transfer learning (FTL) framework for TMD, addressing both privacy and performance concerns. Our approach relies on Federated learning (FL) to train a global model on various datasets from different cities while employing transfer learning to adapt the global model to the specific characteristics of individual smartphones and cities. FOGFLEET relies on an architecture that integrates edge, fog, and cloud layers, with dedicated fog nodes for each city to simplify cross-silo federated learning. Experimental results demonstrate the effectiveness of the FOGFLEET framework in higher TMD accuracy by up to 20% than its comparable centralized approach. Furthermore, it outperforms the FL solutions reported in the literature with at least an 8% increase in accuracy. In this work, we also highlight the importance of sufficient training data for distributed training and discuss the impact of smartphone sensor qualities on the performance of TMD systems. Our work contributes to advancing TMD systems by providing an adaptive and privacy-preserving solution suitable for deployment in diverse urban environments and across various geographical locations.

关键词： Transport Mode Detection Federated learning Transfer learning Privacy Accuracy Fog computing Edge computing

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Quantum-Assisted Machine learning Framework: Training and Evaluation of Boltzmann Machines using Quantum Annealers 5

Quantum-Assisted Machine Learning Framework: Training and Ev...

引用

2024 international conference on Quantum computing and engineering

作者： Pinilla, Jose P. Wilton, Steven J. E. Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada

ISBN: (纸本)9798331541378

This paper describes the components and configurations available in a new quantum-assisted machine learning (QAML) framework. QAML is an open source package that provides a new and flexible test-bed for algorithms to train and evaluate Boltzmann machines (BMs) using quantum annealers. Quantum annealing processors enable the training capabilities for both restricted (RBM) and general Boltzmann machines (BM). These methods rely on the fidelity of samples from those devices, which approximate the characteristic Boltzmann distribution of the BM models. The models and optimization functions are built on top of the PyTorch and D-Wave Ocean libraries. The goal of this paper is to introduce developers and researchers to a familiar, yet flexible, software development framework, to explore the use of quantum computing in machine learning applications. The framework is open-source and has been made publicly available.

关键词： Boltzmann machines Machine learning Quantum annealing

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