A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

Kirschner, P. A., Kirschner, F. C., & Paas, F. (2009). Cognitive load theory. In E. M. Anderman & L. H. Anderman (Eds.). Psychology of classroom learning: An encyclopedia, Volume 1, a-j (pp. 205-209). Detroit, MI: Macmillan Reference.

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Cognitive Load Theory

The conditions of learning

Changing network conditions pose severe problems to video streaming in the Internet. HTTP adaptive streaming (HAS) is a technology employed by numerous video services that relieves these issues by adapting the video to the current network conditions. It enables service providers to improve resource utilization and Quality of Experience (QoE) by incorporating information from different layers in order to deliver and adapt a video in its best possible quality. Thereby, it allows taking into account end user device capabilities, available video quality levels, current network conditions, and current server load. For end users, the major benefits of HAS compared to classical HTTP video streaming are reduced interruptions of the video playback and higher bandwidth utilization, which both generally result in a higher QoE. Adaptation is possible by changing the frame rate, resolution, or quantization of the video, which can be done with various adaptation strategies and related client- and server-side actions. The technical development of HAS, existing open standardized solutions, but also proprietary solutions are reviewed in this paper as fundamental to derive the QoE influence factors that emerge as a result of adaptation. The main contribution is a comprehensive survey of QoE related works from human computer interaction and networking domains, which are structured according to the QoE impact of video adaptation. To be more precise, subjective studies that cover QoE aspects of adaptation dimensions and strategies are revisited. As a result, QoE influence factors of HAS and corresponding QoE models are identified, but also open issues and conflicting results are discussed. Furthermore, technical influence factors, which are often ignored in the context of HAS, affect perceptual QoE influence factors and are consequently analyzed. This survey gives the reader an overview of the current state of the art and recent developments. At the same time, it targets networking researchers who develop new solutions for HTTP video streaming or assess video streaming from a user centric point of view. Therefore, this paper is a major step toward truly improving HAS.

A Survey on Quality of Experience of HTTP Adaptive Streaming

The interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures have been intensively investigated within a cognitive load framework since mid-90s. This line of research has become known as the expertise reversal effect. Apart from their cognitive load theory-based prediction and explanation, patterns of empirical findings on the effect fit well those in studies of Aptitude Treatment Interactions (ATI) that were originally initiated in mid-60s. This paper reviews recent empirical findings associated with the expertise reversal effect, their interpretation within cognitive load theory, relations to ATI studies, implications for the design of learner-tailored instructional systems, and some recent experimental attempts of implementing these findings into realistic adaptive learning environments.

Expertise Reversal Effect and Its Implications for Learner-Tailored Instruction

Video traffic is widely expected to account for a large portion of the traffic in future wireline and wireless networks, as multimedia applications are becoming increasingly popular. Consequently, the performance evaluation of networking architectures, protocols, and mechanisms for video traffic becomes increasingly important. Video traces, which give the sizes, deadlines, and qualities of the individual video frames in a video sequence, have been emerging as convenient video characterizations for networking studies. In this tutorial we give an introduction to the use of video traces in networking studies. First we give a brief overview of digital video and its encoding and playout. Then we present a library of traces of single- and two-layer encoded video. We discuss the statistical properties of the traces and the resulting implications for the transport of video over networks. Finally we discuss the factors that need to be considered when using video traces in network performance evaluations. In particular, we introduce performance metrics that quantify the quality of the delivered video. We outline a procedure for generating video load for network simulations from the traces, and discuss how to meaningfully analyze the outcomes of these simulations.

Network performance evaluation using frame size and quality traces of single-layer and two-layer video: A tutorial

The performance evaluation of video transport mechanisms becomes increasingly important as encoded video accounts for growing portions of the network traffic. Compared to the widely studied MPEG-4 encoded video, the recently adopted H.264 video coding standards include novel mechanisms, such as hierarchical B frame prediction structures and highly efficient quality scalable coding, that have important implications for network transport. This tutorial introduces a trace-based evaluation methodology for the network transport of H.264 encoded video. We first give an overview of H.264 video coding, and then present the trace structures for capturing the characteristics of H.264 encoded video. We give an overview of the typical video traffic and quality characteristics of H.264 encoded video. Finally, we explain how to account for the H.264 specific coding mechanisms, such as hierarchical B frames, in networking studies.

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Video Transport Evaluation With H.264 Video Traces

Video Traces for Network Performance Evaluation

Encountering the expertise reversal effect with a computer-based environment on electrical circuit analysis

Video encoding for multimedia services over communication networks has significantly advanced in recent years with the development of the highly efficient and flexible H.264/AVC video coding standard and its SVC extension. The emerging H.265/HEVC video coding standard as well as 3D video coding further advance video coding for multimedia communications. This paper first gives an overview of these new video coding standards and then examines their implications for multimedia communications by studying the traffic characteristics of long videos encoded with the new coding standards. We review video coding advances from MPEG-2 and MPEG-4 Part 2 to H.264/AVC and its SVC and MVC extensions as well as H.265/HEVC. For single-layer (nonscalable) video, we compare H.265/HEVC and H.264/AVC in terms of video traffic and statistical multiplexing characteristics. Our study is the first to examine the H.265/HEVC traffic variability for long videos. We also illustrate the video traffic characteristics and statistical multiplexing of scalable video encoded with the SVC extension of H.264/AVC as well as 3D video encoded with the MVC extension of H.264/AVC.

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Patrick Seeling

Papers

Network performance evaluation using frame size and quality traces of single-layer and two-layer video: A tutorial

Video Transport Evaluation With H.264 Video Traces

Video Traces for Network Performance Evaluation

Encountering the expertise reversal effect with a computer-based environment on electrical circuit analysis

Video traffic characteristics of modern encoding standards: H.264/AVC with SVC and MVC extensions and H.265/HEVC.