第二语言学习动机模式的测量及其与学习成绩的关系——以混合式学习课程为例

IF 0.6 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zahra AZIZAH, Tomoya OHYAMA, Xiumin ZHAO, Yuichi OHKAWA, Takashi MITSUISHI
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引用次数: 0

摘要

学习分析(LA)已经成为许多学习环境中提高教育质量的一种技术,包括混合式学习(BL)课程。大量研究表明,学生的学习成绩显著地受到其自主学习能力的影响。在本研究中,我们探讨了在第二语言学习过程中表现出的SRL和动机行为。使用移动语言学习应用程序(m-learning app)的在线跟踪数据作为BL实现的一部分。观察到的动机分为两类:高水平动机(及时学习、再学习和早期学习)和低水平动机(死记硬背和迎头赶上)。因此,表现好的学生往往会有高水平的动机。而表现不佳的学生则倾向于低级动机。回归模型表明,无论是春季学期还是秋季学期,及时学习后早期学习对BL课程的学习成绩都有显著影响。利用有限的移动学习应用日志数据资源,本研究可以解释整体的BL性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring Motivational Pattern on Second Language Learning and its Relationships to Academic Performance: A Case Study of Blended Learning Course
Learning analytics (LA) has emerged as a technique for educational quality improvement in many learning contexts, including blended learning (BL) courses. Numerous studies show that students' academic performance is significantly impacted by their ability to engage in self-regulated learning (SRL). In this study, learning behaviors indicating SRL and motivation are elucidated during a BL course on second language learning. Online trace data of a mobile language learning application (m-learning app) is used as a part of BL implementation. The observed motivation were of two categories: high-level motivation (study in time, study again, and early learning) and low-level motivation (cramming and catch up). As a result, students who perform well tend to engage in high-level motivation. While low performance students tend to engage in clow-level motivation. Those findings are supported by regression models showing that study in time followed by early learning significantly influences the academic performance of BL courses, both in the spring and fall semesters. Using limited resource of m-learning app log data, this BL study could explain the overall BL performance.
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来源期刊
IEICE Transactions on Information and Systems
IEICE Transactions on Information and Systems 工程技术-计算机:软件工程
CiteScore
1.80
自引率
0.00%
发文量
238
审稿时长
5.0 months
期刊介绍: Published by The Institute of Electronics, Information and Communication Engineers Subject Area: Mathematics Physics Biology, Life Sciences and Basic Medicine General Medicine, Social Medicine, and Nursing Sciences Clinical Medicine Engineering in General Nanosciences and Materials Sciences Mechanical Engineering Electrical and Electronic Engineering Information Sciences Economics, Business & Management Psychology, Education.
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