This study focuses on visual creativity and how it can be supported with computer technologies and thereby be used to support learning and instruction. However, studies related to computer-enabled visual creativity have not been frequently explored. As such, the current research proposes a model consisting of four major factors: (a) computer-aided visual art self-efficacy, (b) computer self-efficacy, (c) general creative self-efficacy, and (d) visual creativity. The aim is to explore the causal relationships among these factors so that they can then be used to support creativity, especially in the context of learning and instruction. To test the proposed model, this study firstly collected a total of 736 responses from an American public university to construct a scale using exploratory factor analyses and confirmatory factor analyses for three factors: (a) computer self-efficacy, (b) computer-aided visual art self-efficacy, and (c) general creative self-efficacy. Later, 164 responses were collected to analyze those hypothesized predictors of visual creativity and their relationships using structural equation modeling with Mplus. The results of the study indicate that computer self-efficacy was a significant predictor of computer-aided visual art self-efficacy, which in turn was a significant predictor of general creative self-efficacy. General creative self-efficacy, in turn, was a significant predictor of visual creativity. Finally, the study yielded a significant indirect effect of computer-aided visual art self-efficacy on visual creativity as mediated by general creative self-efficacy. Implications for learning and instruction are discussed as well as future studies to further research to develop relevant models of visual creativity in support of learning.

中文翻译：

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计算机视觉创造力：一种基于经验的模型，对学习和指导有影响

这项研究的重点是视觉创造力，以及如何通过计算机技术来支持视觉创造力，从而将其用于支持学习和指导。但是，有关计算机视觉创造力的研究尚未得到频繁探索。因此，当前的研究提出了一个模型，该模型包括四个主要因素：（a）计算机辅助视觉艺术的自我效能感；（b）计算机自我效能感；（c）一般创造性自我效能感；以及（d）视觉创造性。目的是探索这些因素之间的因果关系，以便可以将它们用于支持创造力，特别是在学习和指导的背景下。为了测试建议的模型，这项研究首先从美国一所公立大学收集了总共736个回应，以探索性因素分析和确认性因素分析为三个因素建立了量表：（a）计算机自我效能感，（b）计算机辅助视觉艺术自我效能感， （c）一般的创作自我效能感。后来，使用M的结构方程模型，收集了164个响应，以分析那些假设的视觉创造力预测指标及其关系。加。研究结果表明，计算机自我效能感是计算机辅助视觉艺术自我效能感的重要预测指标，而计算机辅助视觉艺术自我效能感又是一般创造性自我效能感的重要预测指标。反过来，一般的创造性自我效能感是视觉创造力的重要预测指标。最后，该研究产生了计算机辅助视觉艺术自我效能对视觉创造力的显着间接影响，这是由一般创造性自我效能介导的。讨论了对学习和指导的含义以及未来的研究，以进行进一步的研究以开发相关的视觉创造力模型来支持学习。