With the proliferation of mobile devices and online video services, mobile learning has grown both in popularity and complexity. New challenges include the multitude of mobile devices with different characteristics and limitations, as well as the exponential growth in educational multimedia content. Streaming multimedia content to mobile devices is a resource intensive task that requires significant resources such as network bandwidth, with demand expected to increase beyond future networks capacity as users adopt new technologies such as UHD, AR, VR, 3D and 360-degree video. While a number of adaptive m-learning systems have been previously proposed, none of these have thoroughly addressed the adaptation of educational multimedia content. This article proposes the novel DQAMLearn framework that aims to support mobile learner’s seamless access to educational multimedia content from a variety of mobile devices with different characteristics. Moreover, as mobile users are increasingly becoming quality-aware, the framework integrates novel mechanisms for decreasing the video quality in a controlled way, with the aim to support a good learner quality of experience (QoE) even in resource constrained situations. A comprehensive subjective study was conducted to evaluate the proposed framework. The results showed that the framework enables both high learning achievement from educational multimedia clips, with 12% and 83% correct response rates for pre and post-test questionnaires, respectively, and high learner QoE with a mean video quality rating of 79.19 on a 0–100 acceptability scale.

中文翻译：

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DQAMLearn：设备和QoE感知的自适应多媒体移动学习框架

随着移动设备和在线视频服务的激增，移动学习的普及和复杂性都在增长。新的挑战包括具有不同特征和局限性的众多移动设备，以及教育多媒体内容的指数增长。将多媒体内容流式传输到移动设备是一项资源密集型任务，需要大量资源（例如网络带宽），并且随着用户采用UHD，AR，VR，3D和360度视频等新技术，需求将超出未来的网络容量。尽管先前已经提出了许多自适应的m学习系统，但是这些都没有彻底解决教育多媒体内容的改编问题。本文提出了一种新颖的DQAMLearn框架，该框架旨在支持移动学习者从具有不同特征的各种移动设备无缝访问教育性多媒体内容。此外，随着移动用户越来越意识到质量，该框架集成了新颖的机制以受控方式降低视频质量，目的是即使在资源受限的情况下也能支持良好的学习者体验质量（QoE）。进行了全面的主观研究，以评估建议的框架。结果表明，该框架既可以通过教育性多媒体剪辑实现较高的学习成绩，其对测试前和测试后问卷的正确回答率分别为12％和83％，以及在0时平均视频质量等级为79.19的高学习者QoE。 –100可接受等级。