Latent Profiles of Online Self-Regulated Learning: Relationships with Predicted and Final Course Grades

Diana Mindrila, Li Cao
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Abstract

This study used a combined person- and variable-centered approach to identify self-regulated online learning latent profiles and examine their relationships with the predicted and earned course grades. College students (N=177) at a Southeastern U.S. university responded to the Online Self-Regulated Learning Questionnaire. Exploratory structural equation modeling revealed four self-regulation factors: goal setting, environment management, peer help-seeking, and task strategies. Latent profile analysis yielded four latent profiles: Below Average Self-Regulation (BASR), Average Self-Regulation (ASR), Above Average Self-Regulation (AASR), and Low Peer Help-Seeking (LPHS). Compared with the AASR group, when students anticipated obtaining a higher course grade, they were less likely to engage in peer help-seeking and task strategies and more likely to adopt the LPHS self-regulation profile. Relating to LPHS, membership to all other groups predicted significantly lower course grades. AASR and LPHS predicted their performance most accurately, with non-significant differences between the predicted and the final course grades.
在线自主学习的潜在特征:与预期成绩和最终成绩的关系
本研究采用以人为中心和以变量为中心相结合的方法来识别自我调节的在线学习潜在特征,并检查其与预测和获得的课程成绩的关系。美国东南部一所大学的177名大学生回答了“在线自主学习问卷”。探索性结构方程模型揭示了四个自我调节因子:目标设定、环境管理、同伴求助和任务策略。潜在特征分析得到四种潜在特征:低于平均自我调节(BASR)、平均自我调节(ASR)、高于平均自我调节(AASR)和低同伴寻求帮助(LPHS)。与AASR组相比,当学生期望获得更高的课程成绩时,他们不太可能参与同伴寻求帮助和任务策略,更可能采用LPHS自我调节档案。与LPHS相关,所有其他组的成员都预示着课程成绩显著降低。AASR和LPHS最准确地预测了他们的表现,预测和最终课程成绩之间没有显著差异。
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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