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This study analyzed segment differences of student preference for video use in lecture classes and university use of video lecture classes. The authors then conducted novel gap analyses to identify gaps between student segments' preferences for videos versus their level of exposure to in-class videos. Multivariate analysis of variance (MANOVA) was used to identify significant factors that explain the gaps.

Segment differences of student preference for video use in lecture classes and university use of video lecture classes were analyzed. Novel gap analyses were then conducted to identify gaps between student segments' preferences for videos versus their level of exposure to in-class videos. MANOVA was used to identify significant factors that explain the gaps.

Gap analysis of video preference relative to video exposure showed a bimodal distribution, with an approximately even split between students with an overall deficit (44.5%) and surplus (47%) of in-class videos. Deficit means students preferred to see more videos than what the lecturer showed them. Surplus means the lecturer showed students more videos than they preferred to see. Further analyses break down the deficits and surpluses based on the type of videos shown.

Results are useful as an effective diagnostic tool for education managers because they are not at the individual student level but rather by course level. One implication for educational managers is that a one-size-fits-all approach for all courses will benefit some students and annoy others.

This paper extends Alpert and Hodkinson’s (2019) findings by identifying preference clusters and performing segmentation analyses based on finer-grained disaggregated data analysis.

To provide an understanding of how video recording can be used to mediate university level teacher development for language learning in diverse classrooms.

This study draws on cultural historical activity theory (Engeström, 1999) and the subsequent professional development literature to conceptualize video as a tool for self-reflection and critique to further learning. This chapter outlines how video analysis can be used in inservice teacher education to investigate the micro- and macro-interactions with English learners.

We found that utilizing various forms of analysis on a single video from the classroom can help teachers build connections between the micro and macro processes and implications of language in classrooms with English learners. Additionally, by studying videos of classroom activities, teachers learn how linguistic theories and particular instructional and assessment tools can be implemented in their own classrooms.

Videos can be a powerful tool for teacher educators and professional development experts because they allow for the analysis and reflection of a variety of analytic levels. Additionally, this study provides evidence that videos can be used to anchor otherwise discrete university coursework and bring cohesion and collaboration throughout the curriculum.

With the rapid development of short videos in China, the public has become accustomed to using short videos to express their opinions. This paper aims to solve problems such as how to represent the features of different modalities and achieve effective cross-modal feature fusion when analyzing the multi-modal sentiment of Chinese short videos (CSVs).

This paper aims to propose a sentiment analysis model MSCNN-CPL-CAFF using multi-scale convolutional neural network and cross attention fusion mechanism to analyze the CSVs. The audio-visual and textual data of CSVs themed on “COVID-19, catering industry” are collected from CSV platform Douyin first, and then a comparative analysis is conducted with advanced baseline models.

The sample number of the weak negative and neutral sentiment is the largest, and the sample number of the positive and weak positive sentiment is relatively small, accounting for only about 11% of the total samples. The MSCNN-CPL-CAFF model has achieved the Acc-2, Acc-3 and F1 score of 85.01%, 74.16 and 84.84%, respectively, which outperforms the highest value of baseline methods in accuracy and achieves competitive computation speed.

This research offers some implications regarding the impact of COVID-19 on catering industry in China by focusing on multi-modal sentiment of CSVs. The methodology can be utilized to analyze the opinions of the general public on social media platform and to categorize them accordingly.

This paper presents a novel deep-learning multimodal sentiment analysis model, which provides a new perspective for public opinion research on the short video platform.

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.

Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner.

Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words.

The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach.

By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.

In this chapter, we describe how a rubric-style observation instrument for observing classroom writing instruction was used to focus and optimize collaborative video analysis sessions among teachers and researchers spread across six states. As part of a three-year Institute of Education Sciences (IES) development grant, we used videos of classroom instruction both as data for researchers studying the nature and impact of a specific instructional approach, Strategic and Interactive Writing Instruction (SIWI), and as a vehicle for collaborative teacher professional development – for both teachers and teacher leaders.

By tying video analysis to a shared observation instrument, we were able to target video clip selection for discussion and focus our analysis to support teachers across several states and school settings implementing a new approach to writing instruction. After a brief overview of the project for which videos were used, we describe the tools and protocols developed over time to ensure the efficient and powerful use of collaborative video analysis. We also share our experiences on the nature and outcomes of these collaborative sessions both in terms of teachers’ involvement and changes in practice over time.

We argue that the use of a common rubric to guide video clip selection, discussion, and analysis allowed teachers to strategically engage in “data reduction” – that is, not be overwhelmed by the amount of video data – and to use the videos as catalysts for conversations as well as evidence of what works well for individual students. As researchers, these sessions allowed us to ensure collaborative video analysis sessions were focused, efficient, and growth-oriented as well as sources of data for understanding trends in challenges and trajectories of growth for teachers implementing a new approach to instruction.

This work illustrates how researchers can use video for dual purposes – to conduct literacy investigations and to provide teachers with professional development involving video review and reflection.

This chapter serves to synthesize existing literature centered on inservice teacher video-facilitated reflection on literacy pedagogy.

The inservice teacher literature review is focused on: (1) video analysis frameworks and scaffolds used to facilitate inservice teachers’ video reflection; (2) reflection and video discussions; and (3) the use of video for inservice teacher change and development.

From this review we learn that there is a dearth of video reflection research with inservice teachers on literacy pedagogy. Within the field of literacy, we know far less about how, when, and why to use video with inservice teachers than preservice teachers.

The review of literature does not incorporate inservice teacher video reflection in disciplines such as science and mathematics. Expanding this review to all disciplines would present a more comprehensive picture of video reflection with inservice teachers.

The chapter highlights the potential value of using video in inservice professional development and points to the specific needs for studies to identify the most effective uses of video specific to inservice professionals.

This chapter provides significant research-based information for designing and implementing future studies and professional development focused on video reflection with inservice teachers.

The present article encourages urban tourism researchers use pre-recorded (like YouTube, TikTok and security camera footage) and self-recorded video data in research. The author highlighted the benefits of doing so for urban tourism scholars, as well as ways in which these investigators can use the observational video analysis technique to produce convincing findings and advance their field.

To accomplish the purpose, the author reviewed 25 scholarly articles on the topic from several fields.

One benefit of observational video analysis research (OVAR), the author highlighted, was the ability of scholars in different locations to simultaneously observe interactions, the focus of the study, in their natural setting and discuss them. This practice is not possible with traditional ethnographic research. Also, one way the author mentioned researchers can accomplish rigor in their OVAR project is through multimodal transcription. With multimodal transcription, verbal and non-verbal happenings in videos are transliterated and later analyzed.

It is hoped because of the present article, increased use of the technique in urban tourism research will be seen.

The present article is the first, to the best of the author’s knowledge, to detail how rigor can be accomplished in OVAR in urban tourism.

Despite the actual prevalence of diverse types of multimedia information, research on video news is still in an early stage. Improving the accessibility of video news seems worth investigating, therefore, the purpose of this paper is to present a new combination mode of video news text clustering and selection. This method is useful for sorting out and classifying various types of news videos and media texts based on sentiment analysis.

A novel system is proposed, whereby video news are identified and categorized into good or bad ones via the authors' suggested Hidden Markov Model (HMM) and Support Vector Machine (SVM) hybrid learning method. Actually, an exploratory video news sentiment analysis case study, conducted on various news databases, has proven that the feature‐selection‐combining method, encompassing the Information Gain (IG), Mutual Information (MI) and CHI‐statistic (CHI), performs the best classification, which testifies and highlights the designed framework's value.

In fact, the system turns out to be applicable to several areas, especially video news, where annotation and personal perspectives affect the accuracy aspect.

The present work shows the way for further research pertaining to the personal attitudes and the application of different linguistic techniques during the classification.

The achieved results are so promising, encouraging and satisfactory, that they highlight the originality and efficiency of the authors' approach as an effective tool enabling to secure an easy access to video news and multi‐media texts.

Plagiarism can occur in research in various ways, whether intentionally or unintentionally. This study aims to assess the role of YouTube in providing information and creating awareness about plagiarism through videos. It also investigates the topics related to plagiarism covered in YouTube videos and evaluates viewer satisfaction with the content provided.

Using the software Webometric Analyst and various search strings, a total of 526 YouTube videos and 18,050 comments were retrieved on April 23, 2023. The “syuzhet” package has been installed on R-studio to comprehend the sentiment associated with the videos.

The greatest number of videos (184) were submitted describing plagiarism software-related information, followed by 122 videos describing examples of plagiarism. The sentiment analysis of viewers’ associated comments revealed that the majority (111.36 %) were positive, indicating that viewers are satisfied with the available videos.

This study has potential limitations. The number of videos assigned to each content category may be biased to some extent because the videos were not fully viewed and the content categories were assigned manually by analyzing the video title. While analyzing comments for sentiment analysis, some comments are difficult for the software to assign a type of sentiment to, so the percentage of sentiment may be slightly biased.

All data used in this research are original and verified manually. The data for this study was obtained using software (Webometric Analyst) and cross-checked on a regular basis. The titles of the videos were individually inspected and evaluated to analyze the content and emphasis area of previously posted films. This allows the authors to identify the areas of plagiarism when no or few videos have been submitted.