利用学习体验设计:数字媒体方法影响本科生在线课程中支持学生学习行为的动机特征。

IF 4.5 2区 教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH Journal of Computing in Higher Education Pub Date : 2022-10-11 DOI:10.1007/s12528-022-09342-1
Joseph T Wong, Bradley S Hughes
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引用次数: 0

摘要

在考虑学生的社会认知动机特征的同时,调查其他基于证据的在线学习方法以了解学生的学习行为,可能会使高等教育受益匪浅。研究人员开展了一项基于设计的现场研究(DBR),调查学习者体验设计(LXD)方法,采用了异步视频、课程仪表板和增强用户体验等方法。这项混合方法研究(N = 181)评估了学生的社会认知动机特质(自我效能、任务价值、自我调节)与 LXD 所产生的学习行为(参与、阐述、批判性思维)之间的关联。社会认知动机特质对学习行为有积极的预测作用。随着动机因素的增加,学生的课程参与度、阐述的使用率和批判性思维能力都有所提高。自我效能感、任务价值和自我调节解释了参与度方差的 31%,解释了批判性思维能力方差的 47%,解释了详细阐述用法方差的 57%。作为预测因子,任务价值信念显著增加了每个模型的解释变异比例,高于自我效能和自我调节。定性内容分析证实了这些发现,解释了 LXD 如何促进学习动机、学习行为和学习体验。结果表明,LXD 和学生学习行为的基础机制很可能是社会认知动机因素动态催化的结果。讨论最后总结了LXD对学生社会认知动机特征和学习行为产生积极影响的能力,同时也考虑了未来迭代的限制因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Leveraging learning experience design: digital media approaches to influence motivational traits that support student learning behaviors in undergraduate online courses.

Higher education may benefit from investigating alternative evidence-based methods of online learning to understand students' learning behaviors while considering students' social cognitive motivational traits. Researchers conducted an in situ design-based research (DBR) study to investigate learner experience design (LXD) methods, deploying approaches of asynchronous video, course dashboards, and enhanced user experience. This mixed-methods study (N = 181) assessed associations of students' social cognitive motivational traits (self-efficacy, task-value, self-regulation) influencing their learning behaviors (engagement, elaboration, critical thinking) resulting from LXD. Social cognitive motivational traits were positively predictive of learning behaviors. As motivational factors increased, students' course engagement, usage of elaboration, and critical thinking skills increased. Self-efficacy, task-value, and self-regulation explained 31% of the variance of engagement, 47% of the explained variance of critical thinking skills, and 57% of the explained variance in the usage of elaboration. As a predictor, task-value beliefs increased the proportion of explained variance in each model significantly, above self-efficacy and self-regulation. Qualitative content analysis corroborated these findings, explaining how LXD efforts contributed to motivations, learning behaviors, and learning experience. Results suggest that mechanisms underpinning LXD and students' learning behaviors are likely the result of dynamically catalyzing social cognitive motivational factors. The discussion concludes with the LXD affordances that explain the positive influences in students' social cognitive motivational traits and learning behaviors, while also considering constraints for future iterations.

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来源期刊
Journal of Computing in Higher Education
Journal of Computing in Higher Education EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
15.10
自引率
3.60%
发文量
40
期刊介绍: Journal of Computing in Higher Education (JCHE) contributes to our understanding of the design, development, and implementation of instructional processes and technologies in higher education. JCHE publishes original research, literature reviews, implementation and evaluation studies, and theoretical, conceptual, and policy papers that provide perspectives on instructional technology’s role in improving access, affordability, and outcomes of postsecondary education.  Priority is given to well-documented original papers that demonstrate a strong grounding in learning theory and/or rigorous educational research design.
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