Intelligent Recommendation Model of Distance Education Courses Based on Facial Expression Recognition

Yang, Ying; Li, Dongrui; Xiao-feng, Huang; Shengbin, Wu

doi:10.1007/978-3-030-84383-0_12

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…However, this also brings up an issue: how do you find the most suitable learning resources among the vast amount available? The expectation levels of learning energy efficiency serve as a bridge that connects the anticipated impact and intrinsic motivation of students with the tangible benefits of learning resources [12][13][14][15][16]. By conducting in-depth research on the expectations and learning needs of students majoring in finance, we can gain a more accurate understanding and help them select learning resources in a more targeted manner.…”

Section: Introductionmentioning

confidence: 99%

Students’ Decisions in the Context of Social Network Learning Interaction

2023

Int. J. Emerg. Technol. Learn.

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In the context of globalization and technology-driven advancements in the 21st century, learning methods have undergone significant changes. The development of informatization education and social networking technology has facilitated the integration of learning into every aspect of daily life, extending beyond the confines of traditional classrooms. Students interact and cooperate with other learners on social media platforms, which provide them with rich learning resources. However, making the best choice from them has become a core issue. Although the evaluation and selection of learning resources on social networks have been extensively studied in the academic community, most of the studies have focused on the observable attributes of resources while neglecting the subjective perception and experiential attributes of students. This study aimed to investigate how students make learning decisions based on their expectations of learning energy efficiency in social network learning environments. The study also considered observable and experiential attributes of learning resources to provide more comprehensive and accurate references for learning decisions.

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Section: Introductionmentioning

confidence: 99%

Students’ Decisions in the Context of Social Network Learning Interaction

2023

Int. J. Emerg. Technol. Learn.

View full text Add to dashboard Cite

show abstract

Intelligent Recommendation Model of Distance Education Courses Based on Facial Expression Recognition

Cited by 1 publication

References 8 publications

Students’ Decisions in the Context of Social Network Learning Interaction

Students’ Decisions in the Context of Social Network Learning Interaction

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