RETRACTED ARTICLE: Study on ethical dilemmas faced by teaching professionals in rural environments

Chen, Yuling; Lin, Tianhai; Muthu, BalaAnand; Cb, Sivaparthipan

doi:10.1007/s12144-020-00845-7

Cited by 21 publications

(1 citation statement)

References 21 publications

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“…along with their location on the Internet or in the real world [11], [12]. Besides, it can provide learners with different personalization support, including supervision on learning, input, and resources based on their needs [13][14][15][16].It allows students to access digital resources and communicate anywhere and at any given time with learning systems. It delivers them actively with the required tools, learning guidance, tips, or suggestions at the right location, at the right period, and in the right form [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based efficient smart learning framework for education platform

Cao

Wang

Sbeih³

et al. 2020

INT ARTIF

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A smart learning environment is equipped with personal digital devices, wireless communication, learning platforms, and sensors that associate to provide input into Artificial intelligence systems. Artificial intelligence makes decisions about regulating the physical aspects of the environment or learning systems. These requirements may be identified by analyzing learning performance, behaviors, and the real-world and online settings in which students are situated. There are several challenges in implementing smart learning environments that are highly cost-effective, connectivity issues (Internet), impairing students' problem-solving capacity, technical challenges, e.g., malfunctioning of electronic gadgets. Hence, in this paper, Artificial Intelligence based Efficient Smart Learning Framework (AI-ESLF) has been proposed to overcome the challenges faced by a smart learning environment. This study aims to designate the smart learning environment's current concept based on AI application and examine its fundamental criteria and demonstrate how tests can be performed in this smart learning environment by case studies. The experimental results show that the suggested system enhances the prediction ratio in terms of students' learning behavior compared to other existing approaches.

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