Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs

Wu, Bing; Wu, Chunya

doi:10.1016/j.compedu.2021.104295

Cited by 30 publications

(8 citation statements)

References 38 publications

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“…Under the influence of virtual energy, two nodes move closer or further, so as to achieve uniform deployment of nodes in the network. VFA mainly regards each node in the network as a virtual charged particle with the same charge, and there are interaction forces between nodes within the communication radius, including attraction and repulsion [13]. When the distance between the node i and the node j is between the rated voltage Rs and the rated current Rc, the two nodes are mainly due to the effect of gravity, which is used to shrink the coverage range.…”

Section: Typical Coverage Methods For Wireless Sensor Networkmentioning

confidence: 99%

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Wang

Feng

et al. 2022

Journal of Sensors

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Objective. To study energy consumption and QoS optimization coverage mechanism in wireless sensor networks based on swarm sensing algorithm. Methods. The swarm sensor algorithm is reported to optimize the configuration of wireless sensor nodes, improve network service performance, and provide feedback on effective sensor placement. Results. Two WSN coverage optimization schemes were proposed in this essay. The first scheme applied drosophila intelligent algorithm to WSN coverage optimization, and the second scheme integrated particle swarm optimization algorithm and improved firefly algorithm to complement each other. Both schemes can achieve good results in practical application. Scheme one can increase the coverage rate by 6.36% compared with previous schemes, while scheme two can increase it by 6.48%. Both schemes are basically the same, so they can be applied in the optimization of WSN node deployment. Conclusion. As an optimization method, group perception algorithm is inspired by biology. Through the continuous enrichment and development of this algorithm, it has been effectively used in the coverage optimization of the wireless sensor networks.

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Section: Typical Coverage Methods For Wireless Sensor Networkmentioning

confidence: 99%

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Wang

Feng

et al. 2022

Journal of Sensors

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“…Chinese universities, European and American universities, and social institutions are all promoting the construction of online courses [3]. In recent years, typical resources of online open courses for college students organized or participated by the Chinese Ministry of Education include the National Higher Education Smart Education Platform [4,5], according to the data released by the Chinese Ministry of Education [6,7].…”

Section: Introductionmentioning

confidence: 99%

IT Monitoring and Management of Learning Quality of Online Courses for College Students Based on Machine Learning

Guo

Xu²

2022

Mobile Information Systems

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Network information technology and distance technology learning provide convenience for college students to learn online courses, but some problems have also been found in practice, and schools need to pay attention to improving students’ learning quality and supervision. The cross-spatial nature of the study can be used to study how to detect students’ learning fatigue and learning concentration in online classrooms. This paper first designs a lightweight convolutional neural network model for eye state classification and verifies the performance of the model. The designed model has a compact structure and a high recognition rate. Combined with the human eye positioning algorithm, the recognition of the opening and closing state of the eyes is realized. Finally, the feasibility of using the PERCLOS value for fatigue detection, Euler pitch angle, and yaw is verified by experiments. Corners can be used to detect student attention. The method can enhance the synergistic supervision of other cooperative methods, thus improving the quality and effectiveness of online learning for college students, promoting the development of digital modern teaching and learning management models, and exploring possible future technologies and corresponding changes in teaching methods and management models.

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“…According to existing references (Graham et al, 2001;Bangert, 2005;Brahimi and Sarirete, 2015;Hewawalpita et al, 2018;Jung and Lee, 2018;Deng et al, 2020;Liu et al, 2020;Reparaz et al, 2020;Blum-Smith et al, 2021;Li et al, 2021;Tang, 2021;Wu and Wu, 2021;Duan, 2022;Du and Qian, 2022), interview results, and a review of the teaching behaviors identified on MOOC platform websites (Such as: China University MOOC, Coursera, Udacity, and edX), the factors that influence effective teaching can be roughly divided into the following categories: (1) effective teachers, (2) effective teaching, (3) effective communication, (4) students' online learning behavior, (5) social support guarantee, and (6) network course design. An effective teaching evaluation index system is constructed by these influencing factors.…”

Section: Discussionmentioning

confidence: 99%

Influencing factors for effective teaching evaluation of massively open online courses in the COVID-19 epidemics: An exploratory study based on grounded theory

Liu

Wang

2022

Front. Psychol.

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Many factors affect the teaching of massively open online courses (MOOCs). In this study, to explore the factors that influence the effective teaching of MOOCs, a large number of relevant studies are analyzed. Based on grounded theory, semi-structured interviews were conducted with 30 students and teachers who used MOOCs for online teaching. The interview data were subjected to four research processes –open coding, axial coding, selective coding, and saturation testing– to explore the factors influencing MOOCs’ effective teaching and the interactions between them. The results demonstrate that: (1) Effective teachers, effective tuition, effective communication, active online learning, social support guarantees, and online course design have important positive effects on effective teaching, while only certain online learning behaviors will seriously affect the teaching effectiveness of MOOC, resulting in negative effects. (2) Effective communication is essential for effective teaching in MOOCs; effective teachers are the leading factor, thus teachers should take the initiative to study and understand the students to understand their various learning needs and difficulties. (3) Reasonable and effective classroom teaching design is key to improving MOOCs’ teaching efficiency. (4) E-learning is respected, cared for, and valued by society, including cognition, emotion, and learning platform support from family, school, teachers, and classmates, and has an important impact on students’ motivation and the effects of online learning. The results of this study further clarify factors influencing effective teaching of MOOCs, thus helping to enrich and supplement the theory of effective teaching and evaluation and providing theoretical guidance for teachers to effectively implement MOOC teaching.

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Research on the mechanism of knowledge diffusion in the MOOC learning forum using ERGMs

Cited by 30 publications

References 38 publications

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

Energy Consumption and QoS Optimization Coverage Mechanism in Wireless Sensor Networks Based on Swarm Sensing Algorithm

IT Monitoring and Management of Learning Quality of Online Courses for College Students Based on Machine Learning

Influencing factors for effective teaching evaluation of massively open online courses in the COVID-19 epidemics: An exploratory study based on grounded theory

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