The Data Burden of Digital Learning

Rachel Umoren, Ime Asangansi, Dillon Afenir, Brian W. Bresnahan, Annabelle Kotler, Cailin White, Matt Cook, Casey Lowman, Sara Berkelhamer
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Abstract

The costs of participating in training programs that rely on video conferencing vary by mechanics of use and the specific platform. We proposed practical solutions to limiting costs in low resource settings with the use of video conferencing calls. Scenarios in which facilitators have their video on and expect learners to participate with continuous video result in the greatest data burden, while use of intermittent video by both facilitator and learners can significantly lower data use, and thus costs. The choice of a platform also impacts teleprogramming, with creative options for use of lower cost platforms to reduce participant and training organization costs. These might include sharing educational content or video via chat groups and limiting conference to audio alone. In the context of COVID-19 where virtual meetings have become prevalent, it is critical that data burden is considered by program directors and funders. Looking forward, hybrid training that includes virtual and in-person training will likely become the norm in global health settings, but achieving this model will still require thoughtful consideration of data costs. Further, our findings are relevant to many other fields and advocate for evaluation of costs and data burden along with the growing use of teleprogramming in these settings.
数字化学习的数据负担
参加依赖视频会议的培训项目的成本因使用方法和具体平台而异。我们提出了切实可行的解决方案,以限制在低资源环境下使用视频会议呼叫的成本。在这种情况下,主持人打开视频并期望学员通过连续视频参与,会造成最大的数据负担,而主持人和学员使用间歇性视频则可以大大降低数据使用量,从而降低成本。平台的选择也会影响远程编程,可以创造性地选择使用成本较低的平台,以降低学员和培训组织的成本。这可能包括通过聊天群共享教学内容或视频,以及将会议限制在音频范围内。在 COVID-19 的背景下,虚拟会议已成为一种普遍现象,因此计划负责人和资助者必须考虑到数据负担问题。展望未来,包括虚拟培训和现场培训在内的混合培训很可能成为全球健康领域的常态,但实现这种模式仍需要对数据成本进行深思熟虑。此外,我们的研究结果还与许多其他领域相关,因此,随着远程编程在这些领域的应用越来越广泛,我们主张对成本和数据负担进行评估。
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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