Cultivating deep learning in field-based tourism courses: finding purpose in “trouble”

Miller, Maggie C.; Clausen, Helene Balslev; Cater, Carl

doi:10.1080/15313220.2021.2015739

Cited by 3 publications

(1 citation statement)

References 41 publications

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“…(1) MC Migrant children groups refer to the transient population of China's children aged 0-17. Floating population refers to residents who have left their place of domicile for more than half a year, which has increased since the Reform and Opening Up policy in the 1980s (Toker, 2021;Miller et al, 2022).…”

Section: Education Policy Evaluation Modelingmentioning

confidence: 99%

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Chen

Song²,

Yuan³

et al. 2022

Front. Psychol.

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This work intends to solve the problem that the traditional education system cannot reasonably adjust the educational integration of children with the arrival of labor force in a short time, and support the education of migrant children (MC) in the education policy (EP) to integrate them into the local educational environment as soon as possible. Firstly, this work defines the surplus labor force and MC. Secondly, the principles of Artificial Intelligence (AI) and Deep Learning (DL) are introduced. Thirdly, it analyzes the education of MC and relevant policies, and the data of the education effect of MC are collected and the evaluation effect model is built. Finally, the evaluation model of MC’s education effect is applied to test the effect of EP. The results show that using AI technology combined with DL technology to model the education effect of MC can establish an effective and accurate evaluation model of the education effect of MC, effectively evaluate the impact of local education policies on the education of MC, and give an effective effect analysis of relevant education policies in each period. The result of Adaptive Resonance Theory (ART)–Back Propagation algorithm is 65 ∼ 96%, which is much higher than the efficiency of traditional algorithms. This shows that the education integration evaluation model of MC based on AI technology and DL technology can effectively and accurately evaluate the integration effect of MC on the local education system, and then provide reference for local and even national adjustment of education policies. The results provide a new idea for the application of new technology in EP.

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Section: Education Policy Evaluation Modelingmentioning

confidence: 99%

Influence Analysis of Education Policy on Migrant Children’s Education Integration Using Artificial Intelligence and Deep Learning

Chen

Song²,

Yuan³

et al. 2022

Front. Psychol.

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Machine learning applied to tourism: A systematic review

Núñez,

Gómez‐Pulido,

Ramírez

2024

WIREs Data Min & Knowl

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The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper understanding of the progress of this field. Thus, different approaches in the field of tourism will be analyzed, such as planning, forecasting, recommendation, prevention, and security, among others. As a result of this analysis, among other findings, the greater impact of supervised learning in the field of tourism, and more specifically those techniques based on neural networks, has been confirmed. The results of this study would allow researchers not only to have the most up‐to‐date and accurate overview of the application of machine learning in tourism, but also to identify the most appropriate techniques to apply to their domain of interest, as well as other similar approaches with which to compare their own solutions.This article is categorized under: Application Areas > Society and Culture Technologies > Machine Learning Application Areas > Business and Industry

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