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Effect of uninterrupted time-on-task on students' success in Massive Open Online Courses (MOOCs)

机译:大规模开放在线课程(MOOC)中不间断的上课时间对学生成功的影响

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This study investigated the relationship between uninterrupted time-on-task and academic success of students enrolled in a Massive Open Online Course (MOOC). The variables representing uninterrupted time-on-task, such as number and duration of uninterrupted consecutive learning activities, were mined from the log files capturing how 4286 students tried to learn Newtonian mechanics concepts in a MOOC. These variables were used as predictors in the logistic regression model estimating the likelihood of students getting a course certificate at the end of the semester. The analysis results indicate that the predictive power of the logistic regression model, which was assessed by Area Under the Precision-Recall Curve (AUPRC), depends on the value of off-task activity threshold time, and the likelihood of students getting a course certificate increases as students were doing more uninterrupted learning activities over a longer period of time. The findings from this study suggest that a simple count of learning activities, which has been used as a proxy for time-on-task in previous studies, may not accurately describe student learning in the computer-based learning environment because it does not reflect the quality, such as uninterrupted durations, of those learning activities. (C) 2018 Elsevier Ltd. All rights reserved.
机译:这项研究调查了不间断的任务时间与参加大规模开放在线课程(MOOC)的学生的学业成就之间的关系。从日志文件中提取了代表不间断的任务时间的变量,例如不间断的连续学习活动的数量和持续时间,这些日志文件捕获了4286名学生如何尝试在MOOC中学习牛顿力学概念的过程。这些变量在逻辑回归模型中用作预测变量,用于估计学生在学期末获得课程证书的可能性。分析结果表明,由“精确召回曲线下的面积”(AUPRC)评估的逻辑回归模型的预测能力取决于任务外活动阈值时间的值以及学生获得课程证书的可能性随着学生在更长的时间内进行更多不间断的学习活动,学习次数会增加。这项研究的发现表明,在先前的研究中,简单的学习活动数量已被用作完成任务的时间,但由于它不能反映学生的学习状况,因此可能无法准确地描述基于计算机的学习环境中的学生学习情况。学习活动的质量,例如持续时间。 (C)2018 Elsevier Ltd.保留所有权利。

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