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Applying learning analytics for improving students engagement and learning outcomes in an MOOCs enabled collaborative programming course

机译:应用学习分析以提高学生在MOOC中启用的协作编程课程的参与度和学习成果

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As information technology continues to evolve rapidly, programming skills become increasingly crucial. To be able to construct superb programming skills, the training must begin before college or even senior high school. However, when developing comprehensive training programmers, the learning and teaching processes must be considered. In order to improve the students' learning outcome and engagement in programming course, this study applied learning analytics into the proposed massive online open courses (MOOCs) enabled collaborative programming course. Through the proposed learning activity, instructors receive a monthly report that explains which students are at risk and in need of timely intervention. This study conducted an experiment to evaluate the effectiveness of the proposed learning activity. Students in the experimental group received learning interventions from an instructor according to the result of learning analytics, and students in the control group received interventions according to the instructor's observation. The data for this study were collected over 10 weeks at a university in Taiwan. The result indicated that the proposed programming course with learning analytics improved students' learning outcomes and levels of engagement.
机译:随着信息技术的迅速发展,编程技能变得越来越重要。为了培养精湛的编程技能,培训必须在大学甚至高中之前开始。但是,在开发综合培训程序员时,必须考虑学习和教学过程。为了提高学生的学习成果并参与编程课程,本研究将学习分析应用于拟议的大规模在线公开课程(MOOC)支持的协作编程课程。通过拟议的学习活动,讲师将收到一份月度报告,其中说明了哪些学生有风险并需要及时干预。这项研究进行了一项实验,以评估所提议的学习活动的有效性。实验组的学生根据学习分析的结果接受了讲师的学习干预,对照组的学生根据讲师的观察接受了干预。这项研究的数据是在台湾一所大学的10周内收集的。结果表明,拟议的编程课程带有学习分析功能,可以提高学生的学习成果和参与水平。

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