The Collaborative Learning of the Analysis and Modeling of Software with the Use of Facebook

Cedeño, Dunia María Colomé; Ortega, Arlenys Palmero; Dihigo, Ailec Granda; Rodríguez, Taire Faife

doi:10.1007/978-3-030-52575-0_59

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Several studies have identified the benefits of incorporating social media in engineering education and found that the use of social media can improve student engagement and increase collaboration among students [16][17][18]. Similarly, [19] found that using social media in engineering courses can improve student participation, enhance the quality of discussions, and promote self-directed learning.…”

Section: Background and Related Workmentioning

confidence: 99%

Exploring the Use of Social Media in Engineering Education—Preliminary Findings from a Systematic Literature Review

Momin,

Kittur,

Rouf

et al.

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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Section: Background and Related Workmentioning

confidence: 99%

Exploring the Use of Social Media in Engineering Education—Preliminary Findings from a Systematic Literature Review

Momin,

Kittur,

Rouf

et al.

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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LRDC: Learning Resource Diffusion Based on Credibility for Computer‐Supported Collaborative Learning

Cui

Liu

Ren

et al. 2021

Wireless Communications and Mobile Computing

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Computer-supported collaborative learning (CSCL) is a learning strategy that gathers students together on campus through mobile application software on intelligent handheld devices to carry out creative exploration learning activities and social interaction learning activities. Learning resource diffusion is a very important constituent part of CSCL mobile software. However, learners will receive or forward a large number of learning resources such that short video, images, or short audio which will increase the energy consumption of forwarding nodes and reduce the message delivery success rate. How to improve the message delivery success rate is an urgent problem to be solved. To solve the aforementioned problem, this paper mainly studies the diffusion of learning resources in campus opportunistic networks based on credibility for CSCL. In campus opportunistic networks, learners who participate in collaborative learning can obtain the desired learning resources through the distribution and sharing of learning resources. Learning resource diffusion depends on the credibility of learners who participate in collaborative learning. However, the existing classical algorithms do not take into account the credibility between learners. Firstly, the concept of credibility in campus opportunistic networks is proposed, and the calculation method of credibility is also presented. Next, the problem of node initialization starvation is solved in this paper. The node initialization starvation phase of collaborative learning is defined and resolved in campus opportunistic networks. Based on the information of familiarity and activity between nodes formed in the process of continuous interaction, a learning resource diffusion mechanism based on node credibility is proposed. Finally, the paper proposes a complete learning resource diffusion algorithm based on credibility for computer-supported collaborative learning (LRDC for short) to improve the delivery success rate of learning resources on the campus. Extensive simulation results show that the average message diffusion success rate of LDRC is higher than that of classical algorithms such as DirectDeliver, Epidemic, FirstContact, and SprayAndWait under the different transmission speed, buffer size, and initial energy, which is averagely improved by 46.83%, 44.43%, and 45.6%, respectively. The scores of LRDC in other aspects are also significantly better than these classical algorithms.

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Identifying Educational Communication Patterns through Social Media Interactions: The Case of Engineering Education in Oklahoma

Sadri,

Momin

et al.

2024 ASEE Annual Conference &Amp; Exposition Proceedings

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is an experienced Senior Executive specializes in Supply Chain Management. His interdisciplinary journey blends academic achievement with professional leadership. He began with a Bachelor of Science in Mechanical Engineering, laying a strong foundation for his problem-solving skills. Recognizing the importance of understanding diverse aspects of business, he pursued a Master of Development Studies, diving into socio-economic complexities. Further enhancing his data science expertise, he completed a Master of Business Intelligence and Analytics, refining his skills in data-driven decision-making. His research interests include equitable engineering systems design, data analytics and artificial intelligence in engineering education, and broadening participation in engineering.

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The Collaborative Learning of the Analysis and Modeling of Software with the Use of Facebook

Cited by 3 publications

References 6 publications

Exploring the Use of Social Media in Engineering Education—Preliminary Findings from a Systematic Literature Review

Exploring the Use of Social Media in Engineering Education—Preliminary Findings from a Systematic Literature Review

LRDC: Learning Resource Diffusion Based on Credibility for Computer‐Supported Collaborative Learning

Identifying Educational Communication Patterns through Social Media Interactions: The Case of Engineering Education in Oklahoma

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