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The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

Our purpose in this chapter is to provide researchers and educators with a model of how the Gradual Release of Responsibility (GRR) can be used with inservice and preservice teachers for professional development when teachers engage in reflective processes through the use of video reflection.

In this chapter we provide a brief review of the literature related to video as a learning tool for reflection and a discussion of the Gradual Release of Responsibility and emphasize the role of a teacher educator or more knowledgeable other who scaffolds inservice and preservice teacher reflection across various contexts. Several versions of the GRR model are included. We introduce and explain examples from two class sessions where a combination of inservice and preservice teachers engaged in reflection through video with support from a teacher educator.

We demonstrate that the teacher educator followed the GRR model as she guided preservice and inservice teachers to reflect on video. Through a contrastive analysis of two different class sessions, we show how the instructor released responsibility to the students and how students began to take up this responsibility to reflect more deeply on their own teaching practices.

The examples within this chapter are from a graduate level teacher education course affiliated with a university literacy center. The course was comprised of both preservice and inservice teachers. The model is applicable in a variety of settings and for teachers who are novices as well as those who are experienced teachers.

This is a valuable model for teacher educators and others in professional development to use with teachers. Many teachers are familiar with the use of the GRR model in considering how to guide children’s literacy practices, and the GRR can easily be introduced to teachers to assist them in video reflection on their own teaching.

This chapter provides significant research-based examples of the GRR model and foregrounds the role of a teacher educator in video reflection. The chapter provides a unique framing for research and teaching related to video reflection. The chapter explicitly links the GRR to teacher reflection and video in contexts of professional development or teacher education.

The purpose of the paper is to construct a model that considers video purchase and then identifies the logical relationships implied by the parameters to explore video platform operation mechanisms.

The authors analyzed the video platform system using a mathematical modeling approach and numerical optimization techniques. Through pricing decisions, the authors obtained equilibrium results for the profitability of the video platforms and analyzed the favorable market factors. The authors then extended the model by analyzing the competitive strategies of the two video platforms in the market.

The authors find that advertiser profitability, ad nuisance, video sensitivity and video creator network effects are important factors influencing the pricing strategy of video platforms. During positive market conditions, video platforms tend to lower their prices until they absorb enough users. As market conditions change, the price adjustment strategies of video platforms are affected by parameter changes and inter-parameter relationships.

The study considers the network effects of video creators, which provides a realistic reference for scholars and managers. In addition, the authors consider the bargaining power of platforms when purchasing content. The authors provide a fresh perspective for scholars while filling a gap in the field as video platforms can acquire a portion of the content on the market by setting a purchase price.

Students with extensive supports needs (ESN) often require pervasive and intensive supports to access the full benefits of educational programming. In this chapter, the authors describe the application of both established and innovative technologies for promoting equitable access and opportunity for these students. They provide guidance for the use of technology across the areas of academic instruction, social communication, behavior supports, daily living, and employment.

This chapter provides evidence-based supports for special education transition services for students with autism. Visual supports, video modeling, and other related strategies used in the transition from school to adult living are included. Discussion includes using evidence-based transition strategies with students who have autism, fostering self-determination in students with autism during the transition process, and clear examples of how technology can support the transition process. Practitioners may combine the transition supports presented in this chapter because each support addresses more than one characteristic or need.

This study aims to investigate the factors that affect an audience's purchase decisions on a new type of social media, namely live video streaming platforms.

This study is based on data from an online survey providing 488 valid responses. These responses are used to test the research model by employing partial least squares (PLS) modeling.

Three antecedents (consumer competitive arousal, gift design aesthetics and broadcaster's image) influence the audience's purchase decisions (impulse buying and continuous buying intention). Chinese impression management (mianzi) acts as a moderator. Self-mianzi, mutual mianzi and other mianzi (i.e. three subtypes of mianzi) moderate the effects of consumer competitive arousal, gift design aesthetics and broadcaster's image on impulse buying.

The findings encourage practitioners developing marketing strategies for live video streaming platforms in the Chinese cultural context to consider peer influence, gift appearance, broadcaster's image and mianzi.

Drawing on the community gift-giving model and face-negotiation theory, this study provides an integrated research model to investigate a new type of social media (live video streaming). It offers insight into virtual gifting behaviors by confirming the effects of three antecedents on the audience's purchase decisions, with mianzi acting as a moderator.

This chapter presents ways in which educators can use technology to enhance learning for students with autism spectrum disorder (ASD). The chapter begins with an overview of the communicative and social challenges that students with ASD typically experience. Next, it defines technology-aided instruction and intervention (TAII) and offers suggestions for educators planning to incorporate TAII into their classroom. The third section offers specific supports on using TAII to enhance communication while the final section offers specific supports on using TAII to enhance affective and social learning. Case studies and tables with clearly defined supports are included throughout the chapter. Educators may combine the supports presented because each support addresses more than one characteristic or need. In addition, educators will find that these supports benefit students with neurodiverse learning needs who may not be identified with ASD.

This chapter provides evidence-based supports for communication, social skills, and for using restricted patterns of interests and activities educationally for students with autism. Supports for receptive language, expressive language, Picture Exchange Communication System (PECS), visual supports, social narratives, and augmentative and alternative communication are included. Discussion on evidence-based social supports include using peer-mediated instruction and intervention, video modeling, and support for inclusive education. Supports for restricted patterns of behavior, interests, and activities cover how to include special interests, rituals, and routines in educational planning and how to recognize when restricted patterns of behavior are detrimental to education. Repetitive behaviors associated with anxiety and self-injury are also discussed. All supports can be combined and address more than one characteristic or need.

With the continuous growth of online education, the quality issue of online educational videos has become increasingly prominent, causing students in online learning to face the dilemma of knowledge confusion. The existing mechanisms for controlling the quality of online educational videos suffer from subjectivity and low timeliness. Monitoring the quality of online educational videos involves analyzing metadata features and log data, which is an important aspect. With the development of artificial intelligence technology, deep learning techniques with strong predictive capabilities can provide new methods for predicting the quality of online educational videos, effectively overcoming the shortcomings of existing methods. The purpose of this study is to find a deep neural network that can model the dynamic and static features of the video itself, as well as the relationships between videos, to achieve dynamic monitoring of the quality of online educational videos.

The quality of a video cannot be directly measured. According to previous research, the authors use engagement to represent the level of video quality. Engagement is the normalized participation time, which represents the degree to which learners tend to participate in the video. Based on existing public data sets, this study designs an online educational video engagement prediction model based on dynamic graph neural networks (DGNNs). The model is trained based on the video’s static features and dynamic features generated after its release by constructing dynamic graph data. The model includes a spatiotemporal feature extraction layer composed of DGNNs, which can effectively extract the time and space features contained in the video's dynamic graph data. The trained model is used to predict the engagement level of learners with the video on day T after its release, thereby achieving dynamic monitoring of video quality.

Models with spatiotemporal feature extraction layers consisting of four types of DGNNs can accurately predict the engagement level of online educational videos. Of these, the model using the temporal graph convolutional neural network has the smallest prediction error. In dynamic graph construction, using cosine similarity and Euclidean distance functions with reasonable threshold settings can construct a structurally appropriate dynamic graph. In the training of this model, the amount of historical time series data used will affect the model’s predictive performance. The more historical time series data used, the smaller the prediction error of the trained model.

A limitation of this study is that not all video data in the data set was used to construct the dynamic graph due to memory constraints. In addition, the DGNNs used in the spatiotemporal feature extraction layer are relatively conventional.

In this study, the authors propose an online educational video engagement prediction model based on DGNNs, which can achieve the dynamic monitoring of video quality. The model can be applied as part of a video quality monitoring mechanism for various online educational resource platforms.

To explain how an innovative practice-based approach to teacher preparation called the Adaptive Cycles of Teaching utilizes video reflection as part of multiple cycles of teaching across high impact literacy practices.

The faculty research team adopted a design-based research approach to develop and test the ACT model through iterations of design, implementation, analysis, and redesign. The chapter outlines the curriculum and findings from the initial iteration of design.

Teacher candidates experiencing the ACT model developed a strong knowledge of core literacy practices and were able to implement them with children. They continued to need additional scaffolding with respect to the quality of their instructional discourse and the gradual release of responsibility.

Continued research on the ACT model will allow us to refine the ways in which video use can enable preservice teachers to reflect and analyze their teaching and learning.