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Learner satisfaction-based research on the application of artificial intelligence science popularization kits

The application of artificial intelligence science popularization kits in maker courses has promoted the rapid development of maker education. However, there exist few theoretical and empirical studies on the application of artificial intelligence science popularization kits in maker education. The...

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Detalles Bibliográficos
Autores principales: Ling, Yingfei, Jin, Zhou, Li, Yingxin, Huang, Jieya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9343763/
https://www.ncbi.nlm.nih.gov/pubmed/35928423
http://dx.doi.org/10.3389/fpsyg.2022.901191
Descripción
Sumario:The application of artificial intelligence science popularization kits in maker courses has promoted the rapid development of maker education. However, there exist few theoretical and empirical studies on the application of artificial intelligence science popularization kits in maker education. The theory of learner satisfaction can be used to explain learner motivation and outcomes with regard to participation in maker education using the artificial intelligence suite. Therefore, taking advantage of the opportunity the Zhejiang Action Plan for Promoting the Development of New Generation Artificial Intelligence (2019–2022) has provided, this study first conducted semi-structured interviews based on the results of a literature review and a questionnaire survey and then performed Pearson correlation analysis and regression analysis using SPSS 24.0 to explore the influencing factors of students’ satisfaction with the use of artificial intelligence science popularization kits in education. The following results were obtained. (1) The correlation between grades and learners’ satisfaction is not significant. (2) The use of a high-quality artificial intelligence science suite in the classroom will positively impact learners’ satisfaction. (3) The degree of interaction with the artificial intelligence suite is negatively correlated with learners’ satisfaction. (4) Teaching adaptability is significantly positively correlated with learner satisfaction. (5) Learners’ individual characteristics have no significant positive correlation with learner satisfaction. Therefore, this study recommends focusing on suite quality, improving human–computer interaction, adopting a student-centered strategy, and aiming at improving the suitability of the curriculum.