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 experience...
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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.
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, a...
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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.
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 l...
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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.
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 prominen...
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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.
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 te...
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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 disabil...
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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. H...
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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 t...
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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.
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 ...
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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.
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 ...
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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.
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