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.
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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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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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.
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.
Game development education has become more popular all over the World. A review shows several topics, which deal with games in application to teaching and learning: gamification, designing separate courses, and design...
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ISBN:
(纸本)9783031835193;9783031835209
Game development education has become more popular all over the World. A review shows several topics, which deal with games in application to teaching and learning: gamification, designing separate courses, and designing full educational programs. Within this last topic presented article discusses the issues of training specialists in the field of computer games development. The authors analyze the world and landscape of this area and discuss the experience of building systematic training within the framework of the educational program of higher education in the field of "Software engineering". In addition, the authors consider the issues of scientific and applied projects related to the field of computer games development, as well as their mutual influence on each other and on the training of specialists.
Aiming at the problems of large consumption of evaluation time and low completeness of evaluation information in traditional evaluation methods, this paper puts forward a satisfaction evaluation method of compound tea...
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Aiming at the problems of large consumption of evaluation time and low completeness of evaluation information in traditional evaluation methods, this paper puts forward a satisfaction evaluation method of compound teaching mode from the perspective of MOOC concept. Firstly, the MOOC curriculum model is analysed, the satisfaction evaluation index system is established for the compound teaching model, and five first-class indexes and 18 second-class indexes are obtained. Then, the evaluation matrix is established to sort the evaluation indexes of teaching satisfaction, calculate the consistency ratio of the index system, and obtain the weight of each evaluation index by means of objective assignment. Finally, cloud computing technology is used to complete the analysis of satisfaction evaluation data. The experimental results show that the time consumption of this method is between 27.62 s and 34.59 s, the completeness of evaluation information is between 0.923 and 0.951, and the accuracy of evaluation results is between 91.7% and 96.3%.
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.
Large Language Models form the foundation for an explosion of Generative AI tools deployed for a variety of purposes ranging from chatbots to translation. They are having a significant impact on education, and hold bo...
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ISBN:
(纸本)9783031856488;9783031856495
Large Language Models form the foundation for an explosion of Generative AI tools deployed for a variety of purposes ranging from chatbots to translation. They are having a significant impact on education, and hold both positive and negative potential for computing pedagogy. The goal of this work is to use code generated by two of these large language models, Google Gemini and OpenAI ChatGPT to help inform a discussion of appropriate coding assistant policy.
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