A Video Recommendation System for Complex Topic Learning Based on a Sustainable Design Approach

Meza, Xanat Vargas; Yamanaka, Toshimasa

doi:10.1142/s2196888819500179

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The benefits of attending seminars (Miller et al. , 2021) should not be forgotten to avoid social distancing (Chuang and Liao, 2021) or other individual limitations (cognitive and emotional), social (privacy and ethics) and structural (Xanat and Toshimasa, 2019), which compromise the role of OSNs in generating awareness.…”

Section: Resultsmentioning

confidence: 99%

“…The rapid migration to e-Learning during the lockdown posed numerous challenges that negatively affected the effectiveness and sustainability of awareness activities (Cavus et al, 2021). The benefits of attending seminars (Miller et al, 2021) should not be forgotten to avoid social distancing (Chuang and Liao, 2021) or other individual limitations (cognitive and emotional), social (privacy and ethics) and structural (Xanat and Toshimasa, 2019), which compromise the role of OSNs in generating awareness.…”

Section: Use Of Online Social Networkmentioning

confidence: 99%

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Framework for using online social networks for sustainability awareness

Chalmeta,

Barbeito-Caamaño

2023

OIR

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Purpose This study aims to contribute to the field of computer systems for sustainability research. It proposes a framework for sustainability awareness using online social networks (OSNs) by analyzing major research streams of the current state of knowledge and different bibliometric variables, and identifies a future research agenda in the field.Design/methodology/approach The preferred reporting items for systematic review and meta-analysis (PRISMA) methodology, content analysis and bibliometric tools were employed to identify, select, collect, synthesize, analyze and evaluate all research published on sustainability awareness using OSNs to provide complete insight into this research area.Findings This study proposed a framework comprising four categories for sustainability awareness using OSNs. These four categories are: the key factors to success, analysis of existing tools, proposal of new methods, approaches and theoretical frameworks, and case examples. In addition, this study synthesized the future research challenges for each category of the proposed framework.Originality/valueFostering sustainability awareness and sustainable behavior using OSNs is a growing area of research that seeks cultural change in society to achieve sustainable development. Through OSNs, people can discover and become aware of the consequences of unsustainable practices and habits in society, and learn how to develop sustainable behavior.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/OIR

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Section: Resultsmentioning

confidence: 99%

Section: Use Of Online Social Networkmentioning

confidence: 99%

Framework for using online social networks for sustainability awareness

Chalmeta,

Barbeito-Caamaño

2023

OIR

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“…Collaborative filtering recommendation needs to find rules according to the behavior characteristics and interests of users, and the first task is to collect user preference data. According to the recommendation requirements of sports online video teaching resources [18], it analyzes some user behaviors which accurately represent user preferences, and finally these data are obtained and saved for future analysis. The specific process is as follows.…”

Section: Algorithm Design Of Resource Recommendation Software Based O...mentioning

confidence: 99%

Precise Recommendation Algorithm for Online Sports Video Teaching Resources

Zhu

Zhang

2022

EAI Endorsed Scal Inf Syst

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INTRODUCTION: With the development of the epidemic, online teaching has gradually become a hot topic. However, unlike traditional teaching programs, there are many types of physical education resources, and the recommendation of related content has always been a difficulty in online teaching. OBJECTIVES: Therefore, this paper designs an accurate recommendation algorithm for online video teaching resources of sports to meet the personalized needs of online learning of sports majors. The data layer of the entire recommendation algorithm stores the video in the database and transmits it to the service processing layer after receiving the data. METHODS: This study was conducted using techniques from social network analysis. After receiving the data, the data layer of the recommendation algorithm stores the video in the database and transmits it to the business processing layer at the same time. The business processing layer uses the designed collaborative filtering resource recommendation algorithm to formulate recommendation results for different users, and push the recommended results to the user display interface of the user layer. RESULTS: The test results of the algorithm show that the designed system has a high recommendation success rate, and the system can still maintain stable running performance when the concurrent users are 500. The average precision of resource recommendation of this method is 98.21%, the average recall rate is 98.35%, and the average F1 value is 95.37%. CONCLUSION: The proposed resource recommendation algorithm realizes accurate recommendation of sports online video teaching resources through efficient recommendation algorithms.

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“…However, the disadvantage is that the difference of users' interests is not reflected enough, and the accuracy of recommendation is difficult to ensure. Xanat et al [18] put forward an online video content recommendation system following the principle of sustainable design and application, increased the classes and methods of recommended items, and considerably enriched the extracurricular English reading resources. However, this sys-tem encountered problems like low content matching accuracy.…”

Section: State Of the Artmentioning

confidence: 99%

Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

Yang¹

2021

Int. J. Emerg. Technol. Learn.

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With the emergence of computers and networks, the social needs for English competence have presented a diversified and professionalized trend. The current single teaching model can no longer satisfy students’ needs. To cater to different demands of students and improve their level of satisfaction with personalized and automatically recommended teaching videos, an automatic recommendation sys-tem of college English teaching videos, which consists of interface layer, busi-ness logic layer, and data layer, was designed. The quality of recommended teaching videos was ensured through the strict management of English teaching videos within the system. The degree of interest in videos was calculated accord-ing to students’ browsing history of teaching videos. The content of teaching vid-eos that meet students’ personalized demands was established on the basis of the degree of interest. The Naïve Bayesian classification method was used to precise-ly, rapidly, and stably divide the teaching videos into two classes—interest and disinterest—according to the abovementioned information. Results show that the recall ratio and precision ratio of this system reach as high as 95.18% and 97.2%, respectively. The system recommendations averagely rank top, the recommenda-tion precision is high, and the recommended video contents are abundant, with an applause rate of 97.79%. This designed system can establish student-centered college English teaching methods, create a favorable language environment, and better promote the teaching of English among college students.

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A Video Recommendation System for Complex Topic Learning Based on a Sustainable Design Approach

Cited by 6 publications

References 25 publications

Framework for using online social networks for sustainability awareness

Framework for using online social networks for sustainability awareness

Precise Recommendation Algorithm for Online Sports Video Teaching Resources

Automatic Recommendation System of College English Teaching Videos Based on Students’ Personalized Demands

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