通过短信进行移动学习的学生参与度

René F. Kizilcec, Maximillian Chen
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引用次数: 12

摘要

移动学习由于其可及性和可负担性正在迅速扩大,特别是在世界上资源贫乏的地区。然而,学生如何参与和学习移动学习还没有大规模的系统分析。这项研究调查了肯尼亚6年级、9年级和12年级的93819名学生如何使用一个基于短信的移动学习平台,该平台在撒哈拉以南非洲拥有数百万用户。我们调查了不同年龄组学生在一年时间内参与的纵向变化,并使用学习曲线分析检查学习收益的证据。学生的参与度在学校假期和标准化考试之前是最高的,但随着时间的推移,持久性很低:不到25%的学生在加入该平台后返回。我们根据学生的活动水平将他们分成三组,检查他们在前十天的学习行为和测验表现的变化。高度活跃的学生在完成测试、重复尝试和准确性方面表现出有希望的趋势,但我们在这项研究中没有看到学习收益的证据。研究结果表明,肯尼亚的学生将移动学习作为一种临时资源或低成本的导师来补充正规学校教育并弥补教学差距。
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Student Engagement in Mobile Learning via Text Message
Mobile learning is expanding rapidly due to its accessibility and affordability, especially in resource-poor parts of the world. Yet how students engage and learn with mobile learning has not been systematically analyzed at scale. This study examines how 93,819 Kenyan students in grades 6, 9, and 12 use a text message-based mobile learning platform that has millions of users across Sub-Saharan Africa. We investigate longitudinal variation in engagement over a one-year period for students in different age groups and check for evidence of learning gains using learning curve analysis. Student engagement is highest during school holidays and leading up to standardized exams, but persistence over time is low: under 25% of students return to the platform after joining. Clustering students into three groups based on their level of activity, we examine variation in their learning behaviors and quiz performance over their first ten days. Highly active students exhibit promising trends in terms of quiz completion, reattempts, and accuracy, but we do not see evidence of learning gains in this study. The findings suggest that students in Kenya use mobile learning either as an ad-hoc resource or a low-cost tutor to complement formal schooling and bridge gaps in instruction.
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Trust, Sustainability and [email protected] L@S'22: Ninth ACM Conference on Learning @ Scale, New York City, NY, USA, June 1 - 3, 2022 L@S'21: Eighth ACM Conference on Learning @ Scale, Virtual Event, Germany, June 22-25, 2021 Leveraging Book Indexes for Automatic Extraction of Concepts in MOOCs Evaluating Bayesian Knowledge Tracing for Estimating Learner Proficiency and Guiding Learner Behavior
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