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Statistical Analysis on the Effect of Internet-based Education in Student Communities during COVID-19 2

Statistical Analysis on the Effect of Internet-based Educati...

引用

2nd international conference on Sustainable computing and Smart Systems (ICSCSS)

作者： Rajeshkumar, C. Rajakumari, K. Bharath Inst Higher Educ & Res BIHER Dept Comp Sci Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

In light of the COVID-19 epidemic, many educational institutions throughout the globe now need online instruction. Using digital tools and platforms, include teaching, testing, and encouraging student participation. These are some of the main features and difficulties of teaching remotely during the epidemic. To hold lessons and communicate with students, teachers use a variety of online platforms including Zoom, Google Meet, Microsoft Teams, or specialised learning Management Systems (LMS) like Canvas or Moodle. To effectively engage students and transmit concepts, teachers modify their teaching strategies to fit the needs of online settings. This includes adding multimedia materials, interactive exercises, and virtual simulations. It might be difficult to evaluate pupils' learning progress from a distance. Instructors can measure their student's comprehension through online tests, projects, assignments, and peer evaluations. For students to progress, timely feedback via digital channels is crucial. The inequitable availability of technology and dependable internet connections among students may impede their engagement in remote learning environments (RLE). It could be necessary for educators to come up with other strategies to guarantee that every student has access to learning resources and can engage in class activities. The use of Online Education Technology (OET) has been expedited by the epidemic. Online instruction will probably continue to be a crucial component of education even after the epidemic passes, necessitating continuous funding for curriculum creation, infrastructure, and training. A statistical analysis of how the student communities were impacted by internet-based education during the COVID-19 pandemic using Machine learning (ML).

关键词： LMS RLE OET Machine learning

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An Analysis of learning Analytics Approaches for Course Evaluation 17th

An Analysis of Learning Analytics Approaches for Course Eval...

引用

17th international conference on Blended learning. Intelligent computing in Education (ICBL)

作者： Wong, Billy T. M. Li, Kam Cheong Liu, Mengjin Hong Kong Metropolitan Univ Hong Kong Peoples R China

ISBN: (纸本)9789819744411;9789819744428

Course evaluation plays a crucial role in analysing the effectiveness of a course. Despite the emerging trend of applying learning analytics approaches to course evaluation, only limited research has been conducted on reviewing and examining the features of relevant practices. This study analysed the learning analytics approaches used for supporting course evaluation. It covered 27 empirical studies collected from Scopus that were published between 2013 and 2022. The results show the purposes of course evaluation based on learning analytics, including the enhancement of learning experience, effectiveness in learning and teaching, and learning performance and engagement. They also highlight the popular types of data for the learning analytics approaches, such as student performance, feedback, and online learning behaviours, as well as the analytical methods frequently applied, such as statistical tests, content analysis, and descriptive statistics. Additionally, the data visualisation methods most frequently used are also identified, such as tables, bar charts, and line charts. These findings inform the use of learning analytics in course evaluation and provide practical references for its implementation.

关键词： learning analytics course evaluation learning behaviour data visualisation

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AnaVu: A scalable Anatomical 3D visualization system for classroom teaching

AnaVu: A scalable Anatomical 3D visualization system for cla...

引用

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

作者： Harish, Pawan Sivaswamy, Jayanthi Srivastava, Priyanka Yohannan, Doris George Bandi, Sirisha Sattiraju, Sarada Devi Narayanan, P. J. Muni Animat New Delhi India Int Inst Informat Technol Hyderabad India Govt Med Coll Trivandrum Kerala India Bhaskar Med Coll Hyderabad India

ISBN: (纸本)9781665453318

In this paper, we present AnaVu, a light weight visualization system for teaching 3D anatomy at classroom scale. We propose a stereoscopic system along with an easy to use interface as a scalable 3D visual aid as opposed to learning from traditional 2D images. This is an alternative to VR/XR devices that can only serve a handful of students and are heavy on computational resources. For large scale classes (similar to 50-150 students) 3D visualization provides good feedback of spatial relations, with stereoscopic projection further providing depth cues to distinguish fine structures. The visualization is controllable by the lecturer with the ability to control interactive operators along with labels, animations and multimedia capabilities. Lessons can be premeditated and loaded quickly in class to integrate with the ongoing lecture. A quantitative evaluation on 43 students yielded results which show the proposed solution to be viable and effective for learning.

关键词： Anatomy Visualization Stereoscopy Computer Graphics learning evaluation

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Automated Essay Grading of Constructive Response Test Responses for Mechanical engineering Students

Automated Essay Grading of Constructive Response Test Respon...

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

作者： Dung, Do Tien Mima, Hideki Triawan, Farid Cross, Jeffrey Scott Tokyo Inst Technol Sch Environm & Soc Meguro Ku Tokyo Japan Kyushu Univ Promoting Org Future Creators Nishi Ku Fukuoka Japan Sampoerna Univ Dept Mech Engn Jakarta Indonesia

ISBN: (纸本)9781665453318

Machine learning education related applications have increased with the appearance of large language models. While automatic essay grading (AEG) has been studied extensively in the past, most of these studies have focused on evaluating English competence instead of assessing knowledge competence in an engineering field. This study aimed to develop an AEG model to evaluate student's mechanical engineering Constructive Response Test (CRT) question responses which were instructor graded. Because of the small number of student responses (45), a synthesized set of responses was also generated by using text-to-text paraphrasing models. A neural network grading engine was built and trained to assess comprehension utilizing the Bidirectional Encoder Representation Transformer (BERT) and related models on student and synthesized responses. This study showed that the AEG based Natural Language Processing (NLP) model showed high accuracy and a higher degree of consistency in grading student responses compared to instructor-graded responses.

关键词： Automatic Essay Grading Constructive Response Test Mechanical engineering

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Benchmarking Quantum Models for Time-series Forecasting 5

Benchmarking Quantum Models for Time-series Forecasting

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2024 international conference on Quantum computing and engineering

作者： Jones, Caitlin Kraus, Nico Bhardwaj, Pallavi Adler, Maximilian Schroedl-Baumann, Michael Manrique, David Zambrano BASF Digital Solut GmbH Ludwigshafen Germany Aqarios GmbH Munich Germany SAP SE Walldorf Germany

ISBN: (纸本)9798331541378

Time series forecasting is a valuable tool for many applications, such as stock price predictions, demand forecasting or logistical optimization. There are many well-established statistical and machine learning models that are used for this purpose. Recently in the field of quantum machine learning many candidate models for forecasting have been proposed, however in the absence of theoretical grounds for advantage thorough benchmarking is essential for scientific evaluation. To this end, we performed a benchmarking study using real data of various quantum models, both gate-based and annealing-based, comparing them to the state-of-the-art classical approaches, including extensive hyperparameter optimization. Overall we found that the best classical models outperformed the best quantum models. Most of the quantum models were able to achieve comparable results and for one data set two quantum models outperformed the classical ARIMA model. These results serve as a useful point of comparison for the field of forecasting with quantum machine learning.

关键词： Quantum computing Machine learning Forecasting

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An experience report on the use of Active learning in Empirical Software engineering Education: Understanding the pros and cons from the student's perspective 46

An experience report on the use of Active Learning in Empiri...

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ACM/IEEE 44th international conference on Software engineering: Software engineering Education and Training (ICSE-SEET)

作者： Costa Meireles, Maria Alcimar Rocha, Sabrina Maldonado, Jose Carlos Conte, Tayana Univ Fed Amazonas Inst Technol Dev Manaus Amazonas Brazil Univ Fed Amazonas Manaus Amazonas Brazil Univ Sao Paulo Sao Carlos SP Brazil

ISBN: (纸本)9798400704987

Context: Recent years have witnessed a noteworthy surge in the emphasis on research and practice in Empirical Software engineering (ESE). However, teaching this discipline through traditional pedagogical methods presents a challenge. Students typically acquire theories and concepts through lectures or expository classes but have limited opportunities to apply this knowledge. Goal: This report aims to share our experiences with the instructional process, using the Active learning approach within the context of ESE education. Method: We describe an ESE course that was taught using active methodology principles. At the end of the course, we conducted a personal opinion survey to gather students' feedback. Results: The findings indicated that Active learning principles can offer several advantages in ESE education. These include improved comprehension of course material, better retention of knowledge gained during classes, and enhanced preparation for involvement in scientific research. Conclusion: This experience provides insights into incorporating active learning principles into an ESE course. We also discuss lessons learned and suggest areas for improvement in future ESE courses.

关键词： Active learning Empirical Software engineering teaching Process

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teaching Design Thinking to a Large Cohort, A Process Perspective

Teaching Design Thinking to a Large Cohort, A Process Perspe...

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

作者： Kaur, Ameek Huang, Qian Willems, Thijs Hayat, Abdullah Aamir Elara, Mohan Rajesh Singapore Univ Technol & Design Singapore Singapore

ISBN: (纸本)9781665453318

Design thinking has been gaining importance in engineering education worldwide. There is ample information available on design thinking course content, but limited information is available about details of execution of the course especially for a large group of students. At the Singapore University of Technology and Design (SUTD), all the 400 over first-year undergraduate students go through the introductory course on design thinking which has a strong focus on technology. We use this context to look at the processes involved in the effective delivery of the design thinking course. The process components discussed are - course delivery format, assignment design, diverse mentorship, access to past projects and teamwork facilitation. Through our discussion we highlight how these processes - reinforce the design thinking philosophy of balancing divergence and convergence (course delivery, assignment design), inculcate the habit of exploring beyond boundaries (assignment design), develop the capability of processing diverse input (diverse mentorship), reinforce the importance of following the design thinking process for problem solving (access to past projects), and support the pedagogical approach of project based learning (teamwork facilitation). The article intends to stimulate further exploration on course development through process perspective.

关键词： engineering education design thinking course design project-based learning

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MLQAOA: Graph learning Accelerated Hybrid Quantum-Classical Multilevel QAOA 5

MLQAOA: Graph Learning Accelerated Hybrid Quantum-Classical ...

引用

2024 international conference on Quantum computing and engineering

作者： Bach, Bao Falla, Jose Safro, Ilya Univ Delaware Comp & Informat Sci Quantum Sci & Engn Newark DE 19716 USA Univ Delaware Phys & Astron Newark DE USA

ISBN: (纸本)9798331541378

learning the problem structure at multiple levels of coarseness to inform the decomposition-based hybrid quantum-classical combinatorial optimization solvers is a promising approach to scaling up variational approaches. We introduce a multi-level algorithm reinforced with the spectral graph representation learning-based accelerator to tackle large-scale graph maximum cut instances and fused with several versions of the quantum approximate optimization algorithm (QAOA) and QAOA-inspired algorithms. The graph representation learning model utilizes the idea of QAOA variational parameters concentration and substantially improves the performance of QAOA. We demonstrate the potential of using multilevel QAOA and representation learning-based approaches on extensive graphs by achieving high-quality solutions much faster. Reproducibility: Our source code and results are available at https://***/bachbao/MLQAOA

关键词： Quantum Optimization Multilevel QAOA

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Open Digital Badges and Micro-accreditation in the international engineering Educator Certification Program (IEECP) 26th

Open Digital Badges and Micro-accreditation in the Internati...

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26th international conference on Interactive Collaborative learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Maria Palmieri, Juan Cukierman, Uriel Vendrell Vidal, Eduardo Univ Tecnol Nacl Mozart 2300C1407IVT Buenos Aires DF Argentina Univ Politecn Valencia Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031530210;9783031530227

Open digital badges (OB) and micro-accreditation (MA) linked to micro-credentials (MC) are effective means for recognizing skills and competencies, of any kind, in an open, digital, and shareable way. Latin American engineering universities are currently discussing the benefits of using these strategies as a complement to the student's final degree. In this scenario, the organizers of the international engineering Educator Certification Program (IEECP) decided to address the use of OB in this program through its integration into a Moodle based learning Management System (LMS). This paper describes the ongoing experience developed by the IGIP Accredited Training Center (ATC) InnovaHiEd Academy, regarding the use of OB in the IEECP, the motivation and educational decisions that led to the use of this open and universal method for the recognition of competencies, and its integration into the IEECP LMS. Moreover, this study presents evidence regarding the impact of OB and MA on the program teaching practices and participant's engagement. This paper is expected to contribute as a reference for other investigations that address these topics, for those institutions interested in considering this approach in their learning programs and for other IGIP ATCs.

关键词： Online learning Moodle Open digital badges Micro-accreditation learning design Student engagement

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Soft kind of computing Based Least Resource Data finding which is Used for Analysis purpose 4

Soft kind of Computing Based Least Resource Data finding whi...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Rani, L. Pattathu Surendran, S. Monica, D. Kumar, V. Senthil Rajaram, Gnanajeyaraman Dwibedi, Rajat Kumar Simats University Saveetha School of Engineering Chennai India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering India Aarupadai Veedu Institute of Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

Deep learning (DL) approaches are utilized across three distinct contexts to showcase their applicability in commercial environments with restricted data for training. This study showcases the application of unsupervised DL with autoencoders that vary & illustrates how federation learning serves as a successful collaboration approach for machine learning (ML) that safeguards confidentiality of data. © 2024 IEEE.

关键词： Federated learning

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