Date of Award
Spring 5-2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Education
First Advisor
Carlos Henderson
Second Advisor
Douglas Havard
Third Advisor
Keith Howard
Abstract
Recognizing the existing research gaps concerning learner characteristics in the realm of personalized learning in Chinese higher art education, this study initially analyzed prevailing patterns in personalized learning research and its current implementation in higher education through an extensive literature review. Subsequently, a quantitative investigation was carried out at S University in Shanghai, aiming to delve into their learner characteristics, investigate the interrelationships among these characteristics, and propose customizable personalized learning designs. The research included a comprehensive quantitative study using a learner characteristics questionnaire survey involving 455 art students at S University, employing various statistical methods, including ANOVA, factor analysis, cluster analysis, and multiple regression. The study extensively explored eight distinct learner characteristic factors and successfully identified three learner clusters with statistically significant differences, providing detailed descriptions of the characteristics within each cluster to support personalized learning. Furthermore, the paper, through multiple regression analysis, revealed the direct impacts of self-efficacy and spatial orientation ability on learning behavior, while also elucidating the moderating role of learning anxiety in this relationship. Ultimately, personalized learning recommendations for higher art education were formulated based on the identified learner characteristics in distinct groups of art students in higher education.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Shao, J. (2024). Personalized learning for art major students based on learner characteristics [Doctoral dissertation, Chapman University]. Chapman University Digital Commons. https://doi.org/10.36837/chapman.000560