Open educational resources for computer networking

Bonaventure, O.; Coninck, Quentin De; Duchene, Fabien; Gégo, Anthony; Jadin, Mathieu; François, Michel; Piraux, Maxime; Poncin, Chantal; Tilmans, Olivier

doi:10.1145/3411740.3411746

Cited by 3 publications

(1 citation statement)

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“…This technology primarily generates recommendation lists for target users based on the preferences of nearby users and makes recommendations based on shared interests or projects. The most popular personalized recommendation algorithms with high recommendation efficiency among CF based on memory are CF based on users and CF based on items [ 7 ]. It is necessary to identify a group of users who share this user's interests in order to recommend to this user the interesting preferences of this group of users.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Intelligent Integration of Online Environmental Education Resources for English Language and Literature Majors Based on Collaborative Filtering Algorithm

Meng

2022

Journal of Environmental and Public Health

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There are currently many different types and dispersed online educational resources, which inconvenience users and result in a low utilisation rate of resources. As a result, a new approach is required to realise the integration and recommendation of educational resources. This paper examines the intelligent integration and recommendation of online learning resources for English language and literature majors based on CF. The development of online English language and literature education resources is currently in the process of being discussed, and some flaws in the process are being examined in this paper. The creation and incorporation of a network education resource database are proposed as some strategies and recommendations. The information entropy method is employed to address the cold start problem brought on by the data sparseness of new users and new projects in CF. While this is happening, the recommendation process’s similarity algorithm is being enhanced. This algorithm’s decision support accuracy has been found to be 96.01% after extensive testing. Its accuracy is roughly 8% better than that of conventional CF, which has a precision of 8%. The results demonstrated a degree of accuracy in the improved algorithm.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Intelligent Integration of Online Environmental Education Resources for English Language and Literature Majors Based on Collaborative Filtering Algorithm

Meng

2022

Journal of Environmental and Public Health

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Reusable educational resources for developing complex thinking on open platforms

Sanabria-Z,

Alfaro-Ponce,

González-Pérez

et al. 2023

Educ Inf Technol

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In the context of rapid technological advancements and the increasing availability of Open Educational Resources (OER), there is a growing need to foster complex thinking skills in higher education. The study aims to characterize OER platforms that facilitate the development of such skills, grounded in UNESCO’s recommendations and the evolving definitions of complex thinking. Utilizing a Systematic Literature Review (SLR), the research focuses on three key questions: the type of open license allowing 5R activities, the technical options based on the ALMS Framework, and the sub-competencies of complex thinking that can be nurtured through OER. The study identifies and categorizes key platforms conducive to the development of complex thinking skills, namely innovative, critical, scientific, and systemic thinking. These platforms are analyzed based on their licensing types and technical capabilities. Findings indicate that most of the platforms studied support 5R activities under Creative Commons licenses, require advanced editing skills, and more than half address multiple subcompetencies of complex thinking. The study concludes that the adaptation, reuse, and redistribution of OER can significantly contribute to fostering complex thinking skills, aligning educational practices with 21st-century demands. Recommendations include the development of frameworks by higher education institutions to normalize OER platforms and meet students’ needs, as well as future research to assess user behavior and sustainability of these platforms.

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