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1 – 10 of 242Ying-Hsun Lai, Yu-Shan Lin, Yao-Chung Chang and Shih-Yeh Chen
Education for sustainable development (ESD) is a developing educational concept that aims to achieve economic, social and environmental sustainability through education. Cultural…
Abstract
Purpose
Education for sustainable development (ESD) is a developing educational concept that aims to achieve economic, social and environmental sustainability through education. Cultural sustainability education aims to cultivate awareness and protection of cultural assets, promote sustainable development and foster environmental responsibility. This study establishes a cyber-physical metaverse of cultural sustainability learning to cultivate students' motivation, multicultural identity, critical thinking and sustainability thinking.
Design/methodology/approach
In this study, virtual reality (VR) and 3D printing technologies were utilized to create a cyber-physical metaverse learning environment. This learning environment is designed to allow elementary school children to learn about indigenous cultures and the architecture of slate houses, as well as socio-architectural issues. Learners will be able to experience first-hand the plight of the indigenous tribal areas and the exploration of related cultural knowledge.
Findings
The study collected pre- and post-test data through questionnaires, using covariates to analyze learners' perceptions of learning. The results of this study showed that cyber-physical metaverse learning environment had a significant impact on learning motivation, multicultural identity and sustainability thinking for culturally sustainable education. However, this study’s impact on critical thinking skills in students remains to be confirmed.
Research limitations/implications
This is a quasi-experimental study of a single country’s elementary school children in the indigenous area, so its findings cannot be extrapolated to other areas or to learners of different ages.
Originality/value
This study introduces a framework for incorporating both virtual and real cultures to promote sustainable learning. The cyber-physical metaverse learning is used to supplement teaching activities to enhance learners' motivation in learning multicultural knowledge. Students were able to recognize and protect cultural assets, as well as emphasize the importance of sustainable development.
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Lu Wang, Jiahao Zheng, Jianrong Yao and Yuangao Chen
With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although…
Abstract
Purpose
With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.
Design/methodology/approach
In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.
Findings
On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.
Originality/value
In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.
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Raquel Vieira and João Pedro da Ponte
This paper focuses on prospective teachers’ (PTs) participation in a lesson study (LS) that prompted them to research their own practice. We seek to describe the dimensions of…
Abstract
Purpose
This paper focuses on prospective teachers’ (PTs) participation in a lesson study (LS) that prompted them to research their own practice. We seek to describe the dimensions of PTs’ knowledge of student learning developed during the process and the LS features fostering it.
Design/methodology/approach
The participants were two PTs, a teacher educator, a cooperating teacher and a researcher. The LS was integrated into a Portuguese initial elementary teacher education program. Following a qualitative approach, we used participant observation.
Findings
The PTs developed their knowledge of students’ learning of the concept of area in four dimensions: theories; students’ interests and expectations; ways students interact with the content and students’ strengths and weaknesses in learning the concept. To support this development, the LS design considered follow-up sessions and emphasised collaborative work.
Originality/value
This study focuses on PTs researching their practice and disseminating the results, which has been overlooked in previous research of LS with PTs. The results highlight the potential of LS to motivate PTs to research their practice and emphasise the importance of involving them in disseminating LS results.
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Katherine E. McKee, Haley Traini, Jennifer Smist and David Michael Rosch
Our goals were to explore the pedagogies applied by instructors that supported Black, Indigenous and People of Color (BIPOC) student learning in a leadership course and the…
Abstract
Purpose
Our goals were to explore the pedagogies applied by instructors that supported Black, Indigenous and People of Color (BIPOC) student learning in a leadership course and the leadership behaviors BIPOC students identified as being applicable after the course.
Design/methodology/approach
Through survey research and qualitative data analysis, three prominent themes emerged.
Findings
High-quality, purposeful pedagogy created opportunities for students to learn. Second, a supportive, interactive community engaged students with the instructor, each other and the course material to support participation in learning. As a result, students reported experiencing big shifts, new growth and increased confidence during their leadership courses.
Originality/value
We discuss our findings and offer specific recommendations for leadership educators to better support BIPOC students in their leadership courses and classrooms and for further research with BIPOC students.
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This study aimed to determine the expectations of students from mathematics teachers in the planning phase of lesson study (LS) in mathematics classrooms.
Abstract
Purpose
This study aimed to determine the expectations of students from mathematics teachers in the planning phase of lesson study (LS) in mathematics classrooms.
Design/methodology/approach
This study reported only a part of large-scale action research. The participants were Grade 8 students selected by the convenience sampling method. The data were obtained through open-ended questions. The content analysis method was used to analyze the data.
Findings
Four categories emerged: connection, technology-supported teaching, use of concrete materials, practice, and teacher behavior and teaching style.
Research limitations/implications
This study reveals how students in a different culture and education system, such as Türkiye, want to learn mathematics in the LS process of Japanese origin. It also gives some important clues for applying LS in a different culture.
Practical implications
This study may attract the attention of educational stakeholders who want to implement LS in mathematics classrooms by considering student perspectives.
Social implications
Due to the nature of LS, this study may emphasize teacher–student and teacher–teacher interactions. Thus, it can draw attention to the importance of social learning environments where students take responsibility and interact.
Originality/value
This study emphasizes the importance of listening to student voices in LS. Some ideas about mathematics teaching in Turkey should also be given. Finally, it can provide a good basis for understanding and comparing LS practices in different cultures and understandings.
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Feng Wang, Mingyue Yue, Quan Yuan and Rong Cao
This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of…
Abstract
Purpose
This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.
Design/methodology/approach
Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.
Findings
The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.
Originality/value
Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.
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This article examines curatorial practices, both traditional and digital, in the Guizhou Provincial Museum’s ethnic exhibition to assess their effectiveness in representing ethnic…
Abstract
Purpose
This article examines curatorial practices, both traditional and digital, in the Guizhou Provincial Museum’s ethnic exhibition to assess their effectiveness in representing ethnic minority cultures, fostering learning and inspiring curiosity about ethnic textiles and costumes and associated cultures. It also explores audience expectations concerning digital technology use in future exhibitions.
Design/methodology/approach
A mixed-methods approach was employed, where visitor data were collected through questionnaires, together with interviews with expert, museum professionals and ethnic minority textile practitioners. Their expertise proved instrumental in shaping the design of the study and enhancing the overall visitor experience, and thus fostering a deeper appreciation and understanding of ethnic minority cultures.
Findings
Visitors were generally satisfied with the exhibition, valuing their educational experience on ethnic textiles and cultures. There is a notable demand for more immersive digital technologies in museum exhibitions. The study underscores the importance of participatory design with stakeholders, especially ethnic minority groups, for genuine and compelling cultural representation.
Originality/value
This study delves into the potentials of digital technologies in the curation of ethnic minority textiles, particularly for enhancing education and cultural communication. Ethnic textiles and costumes provide rich sensory experience, and they carry deep cultural significance, especially during festive occasions. Our findings bridge this gap; they offer insights for museums aiming to deepen the visitor experiences and understanding of ethnic cultures through the use of digital technologies.
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R.S. Vignesh and M. Monica Subashini
An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories…
Abstract
Purpose
An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.
Design/methodology/approach
In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.
Findings
By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.
Originality/value
The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.
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Long Zhao, Xiaoye Liu, Linxiang Li, Run Guo and Yang Chen
This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain…
Abstract
Purpose
This study aims to realize efficient, fast and safe robot search task, the belief criteria decision-making approach is proposed to solve the object search task with an uncertain location.
Design/methodology/approach
The study formulates the robot search task as a partially observable Markov decision process, uses the semantic information to evaluate the belief state and designs the belief criteria decision-making approach. A cost function considering a trade-off among belief state, path length and movement effort is modelled to select the next best location in path planning.
Findings
The semantic information is successfully modelled and propagated, which can represent the belief of finding object. The belief criteria decision-making (BCDM) approach is evaluated in both Gazebo simulation platform and physical experiments. Compared to greedy, uniform and random methods, the performance index of path length and execution time is superior by BCDM approach.
Originality/value
The prior knowledge of robot working environment, especially semantic information, can be used for path planning to achieve efficient task execution in path length and execution time. The modelling and updating of environment information can lead a promising research topic to realize a more intelligent decision-making method for object search task.
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Yong Wang, Yuting Liu and Fan Xu
Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating…
Abstract
Purpose
Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.
Design/methodology/approach
In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.
Findings
The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.
Originality/value
Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.
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