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Jain, Puneet, Justin Manweiler, Arup Acharya, and Romit Roy Choudhury. "Scalable Social Analytics for Live Viral Event Prediction." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (May 16, 2014): 226–35. http://dx.doi.org/10.1609/icwsm.v8i1.14504.
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Large-scale, predictive social analytics have proven effective. Over the last decade, research and industrial efforts have understood the potential value of inferences based on online behavior analysis, sentiment mining, influence analysis, epidemic spread, etc. The majority of these efforts, however, are not yet designed with realtime responsiveness as a first-order requirement. Typical systems perform a post-mortem analysis on volumes of historical data and validate their “predictions” against already-occurred events.We observe that in many applications, real-time predictions are critical and delays of hours (and even minutes) can reduce their utility. As examples: political campaigns could react very quickly to a scandal spreading on Facebook; content distribution networks (CDNs) could prefetch videos that are predicted to soon go viral; online advertisement campaigns can be corrected to enhance consumer reception. This paper proposes CrowdCast, a cloud-based framework to enable real-time analysis and prediction from streaming social data. As an instantiation of this framework, we tune CrowdCast to observe Twitter tweets, and predict which YouTube videos are most likely to “go viral” in the near future. To this end, CrowdCast first applies online machine learning to map natural language tweets to a specific YouTube video. Then, tweets that indeed refer to videos are weighted by the perceived “influence” of the sender. Finally, the video’s spread is predicted through a sociological model, derived from the emerging structure of the graph over which the video-related tweets are (still) spreading. Combining metrics of influence and live structure, CrowdCast outputs sets of candidate videos, identified as likely to become viral in the next few hours. We monitor Twitter for more than 30 days, and find that CrowdCast’s real-time predictions demonstrate encouraging correlation with actual YouTube viewership in the near future.
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Arulraj, Joy. "Accelerating Video Analytics." ACM SIGMOD Record 50, no. 4 (January 31, 2022): 39–40. http://dx.doi.org/10.1145/3516431.3516442.
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MOTIVATION. The advent of inexpensive, high-quality cameras has led to a rapid increase in the volume of generated video data [19, 16]. It is now feasible to automatically analyze these video datasets at scale due to two developments over the last decade. First, researchers have designed complex, computationally-intensive deep learning (DL) models that capture the contents of a given set of video frames (e.g., objects present in a particular frame [11]) [15]. Second, the computational capabilities of hardware accelerators for evaluating these DL models have increased over the last decade (e.g., TPUs) [8]. We anticipate that automated analysis of videos will reduce the labor cost of analyzing video
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Wang, Han, Shangyu Xie, and Yuan Hong. "VideoDP: A Flexible Platform for Video Analytics with Differential Privacy." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (October 1, 2020): 277–96. http://dx.doi.org/10.2478/popets-2020-0073.
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AbstractMassive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.
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Dolores, Maria, and Jorge Mañana-Rodriguez. "Exploring Engagement in Online Videos for Language Learning through YouTube’s Learning Analytics." EDEN Conference Proceedings, no. 1 (September 21, 2021): 49–58. http://dx.doi.org/10.38069/edenconf-2021-ac0005.
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Until a few years ago, video analytics were not accessible to learning stakeholders, mainly because online video platforms did not share the users’ interactions on the system with stakeholders. However, this scenario has changed, and currently YouTube, the world’s largest media sharing site, offers these data. YouTube is also the main tool for transmitting audio-visual content in Language MOOCs (massive open online courses), and its video engagement data can be monitored through the YouTube Studio channel, which provides free and open access to video analytics. In this paper we present our research based on the analysis of viewers’ engagement with 35 videos of the Language MOOC entitled Alemán para hispanohablantes: basic principles (German for Spanish-speakers). The data provided by the YouTube Studio Learning Analytics platform has enabled new insights related to participants’ watching of these videos in Language MOOCs (LMOOCs). The results of our study provide pedagogical implications for Foreign Language instructors concerning the use of videos in language learning.
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Zhang, Jingjing, Yicheng Huang, and Ming Gao. "Video Features, Engagement, and Patterns of Collective Attention Allocation." Journal of Learning Analytics 9, no. 1 (March 11, 2022): 32–52. http://dx.doi.org/10.18608/jla.2022.7421.
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Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.
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Carpenter, Chris. "Computer Vision Analytics Enables Determination of Rig State." Journal of Petroleum Technology 74, no. 01 (January 1, 2022): 96–98. http://dx.doi.org/10.2118/0122-0096-jpt.
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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204086, “Determining Rig State From Computer Vision Analytics,” by Crispin Chatar, SPE, and Suhas Suresha, Schlumberger, and Laetitia Shao, Stanford University, et al. The paper has not been peer reviewed. While companies cannot agree on a standard definition of “rig state,” they can agree that, as further use is made of remote operations and automation, rig-state calculation is mandatory in some form. By use of a machine-learning model that relies exclusively on videos collected on the rig floor to infer rig states, overcoming the limitations of existing methods is possible as the industry moves into a future of rigs featuring advanced technologies. Introduction The complete paper presents a machine-learning pipeline implemented to determine rig state from videos captured on the floor of an operating rig. The pipeline is composed of two parts. First, the annotation pipeline matches each frame of the video data set to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Second, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from the videos. The models are trained on a data set of 3 million frames on a cloud platform using graphics processing units. Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional 3D (C3D) model, and a two-stream model that uses optical flow to capture temporal information.
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Liu, Ying, Dickson K. W. Chiu, and Kevin K. W. Ho. "Short-Form Videos for Public Library Marketing: Performance Analytics of Douyin in China." Applied Sciences 13, no. 6 (March 7, 2023): 3386. http://dx.doi.org/10.3390/app13063386.
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Short-form video platforms have become an important marketing channel for library resources and services. However, such promotions’ actual performance is not as good as expected. This research examined the performance of library marketing on the dominant short-form video platform in China, Douyin (aka TikTok worldwide), with social media analytics, including topic and correlation analysis. Results indicated that the number of fans of an account is positively correlated with the number of likes (p < 0.001) and independent of the number of videos (p > 0.05). Libraries post videos most often on the topic of “Reading Promotion”(31%), but the marketing performance on this topic is average (Mean DMI = 90.27). The most popular topics for patrons are “Hot Topics” and “Knowledge Quiz” (Mean DMI = 207.00). Using short-form videos for library marketing is a new practice, and scant studies have evaluated such performance, especially in Asia. Our results strengthen library practitioners’ awareness and reflections on conducting new media services and short-form video promotion, especially for the newer generation.
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Luo, Zhiming, Pierre-Marc Jodoin, Song-Zhi Su, Shao-Zi Li, and Hugo Larochelle. "Traffic Analytics With Low-Frame-Rate Videos." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 4 (April 2018): 878–91. http://dx.doi.org/10.1109/tcsvt.2016.2632439.
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DAUN, Felipe, and Ana Maria Dianezi GAMBARDELLA. "Educational videos with nutritional approach in YouTube." Revista de Nutrição 31, no. 3 (May 2018): 339–49. http://dx.doi.org/10.1590/1678-98652018000300007.
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ABSTRACT Objective Produce food and nutrition education videos, post these on YouTube and evaluate their reception over a two-year period. Methods Afterward bibliographic searches, sixteen different themes were developed and explored. An educational objective was defined for each video, took into account food and nutrition aspects in Brazil. The reception of the videos was evaluated using the “YouTube Analytics” tool, which allows analysis of the number of times videos were played, average playing time, and profile of the viewers. Results Sixteen videos were produced from November 2013 to July 2015. Views for each video within two years of posting were calculated individually, giving a total of 78,546 views for all videos. Most of the videos delivered their educational message before the audience lost interest. Conclusion Videos successfully reached the YouTube users and delivered the food and nutrition education messages. Therefore, this pioneering work showed YouTube as a new setting for health promotion in Brazil, paving the way for further initiatives with this platform.
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Xiao, Sihan. "More than Data: A Multivocal Inquiry into Video-Based Research on Learning and Teaching." ECNU Review of Education 1, no. 3 (September 2018): 23–35. http://dx.doi.org/10.30926/ecnuroe2018010302.
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Purpose This commentary aims to echo Wilkinson, Bailey, and Maher's (this volume) arguments about the affordances of videos and video databases in studying learning and teaching. Design/Approach/Methods This article illustrates a multivocal approach to the videos from the Video Mosaic Collaborative (VMC). In particular, three mathematics teachers in Shanghai were invited to watch and discuss a set of VMC videos. Two recurring themes concerning mathematics learning and teaching were identified in this video-cued interview and discussed in relation to the VMC Analytics. Findings The VMC videos played a mediating and facilitating role in the interview, helping the teachers notice and reflect on the mundane, implicit culture practices. Based upon this analysis, I argue that to tap into the potential of video in educational research, we need to see videos as more than data and look for more possibilities of using them. Originality/Value To open and further research dialogues, this article discusses future directions of using videos in educational research and serves as an invitation to creative explorations, in-depth conversations, ethical reflections, and cross-cultural collaborations on the use of videos in education.
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Geri, Nitza, Amir Winer, and Beni Zaks. "Challenging the six-minute myth of online video lectures: Can interactivity expand the attention span of learners?" Online Journal of Applied Knowledge Management 5, no. 1 (May 5, 2017): 101–11. http://dx.doi.org/10.36965/ojakm.2017.5(1)101-111.
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Keeping learners engaged in viewing online video lectures is a challenge, which is considered harder as the length of the video is longer. Although it is a known obstacle, in practice, many videos are lengthy and do not contain interactive elements. This study takes an attention economy perspective, and examines if interactivity may enable effective use of longer online video lectures. Google Analytics data was used to measure average online video lecture viewing completion percentage for two ‘long’ and ‘short’ video lecture groups, before and after the addition of interactive components. Preliminary results show that addition of interactivity significantly improved completion percentage as well as average viewing time for both ‘long’ and ‘short’ video lecture groups by more than 20%. Furthermore, the average viewing time of the ‘long’ group grew to over 10 minutes. The contributions of this study are twofold: it demonstrates the potential of learning analytics to identify ways to improve learning processes, and it provides empirical support for the potential of adding interactive elements to the videos to expand the attention span of learners.
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Osztián, Pálma Rozália, Zoltán Kátai, Ágnes Sántha, and Erika Osztián. "Investigating the AlgoRythmics YouTube channel: the Comment Term Frequency Comparison social media analytics method." Acta Universitatis Sapientiae, Informatica 14, no. 2 (December 1, 2022): 273–301. http://dx.doi.org/10.2478/ausi-2022-0016.
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Abstract In this paper we investigate the comments from the AlgoRythmics YouTube channel using the Comment Term Frequency Comparison social media analytics method. Comment Term Frequency Comparison can be a useful tool to understand how a social media platform, such as a Youtube channel is being discussed by users and to identify opportunities to engage with the audience. Understanding viewer opinions and reactions to a video, identifying trends and patterns in the way people are discussing a particular topic, and measuring the effectiveness of a video in achieving its intended goals is one of the most important points of view for a channel to develop. Youtube comment analytics can be a valuable tool looking to understand how the AlgoRythmics channel videos are being received by viewers and to identify opportunities for improvement. Our study focuses on the importance of user feedback based on ten algorithm visualization videos from the AlgoRythmics channel. In order to find evidence how our channel works and new ideas to improve we used the so-called comment term frequency comparison social media analytics method to investigate the main characteristics of user feedback. We analyzed the comments using both Youtube Studio Analytics and Mozdeh Big Data Analysis tool.
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Van Nuland, Sonya E., and Natalie R. Langley. "‘Must See’ Videos? Why Educators Need Better Video Analytics to Measure Learning." FASEB Journal 34, S1 (April 2020): 1. http://dx.doi.org/10.1096/fasebj.2020.34.s1.02981.
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C, Rahul, and Merin Meleet. "Irregular Events Detection in Videos using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 4268–72. http://dx.doi.org/10.22214/ijraset.2022.45921.
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Abstract: Video analytics for detecting events using machine learning is designed and developed to analyze and detect patterns in the videos. Especially in the field of criminal forensics where a video needs to be analyzed to find out what abnormal events are happening in it and who caused it and how it was caused. This is an easy task for a human as they can recognize criminal events easily but not machines. The objective is to automatically detect the irregular events in videos like burglary, fighting, arson and explosion using a CNN model by preprocessing the videos into frames and extracting information from these frames and also to train the model so that it can also detect normal events. The results of this work shows that the model detects irregular events in videos with high accuracy
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Pan, Yaohua, Zhibin Niu, Jing Wu, and Jiawan Zhang. "InSocialNet: Interactive visual analytics for role—event videos." Computational Visual Media 5, no. 4 (December 2019): 375–90. http://dx.doi.org/10.1007/s41095-019-0157-9.
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Larson, Benjamin, Jeffrey A Bohler, and Anand Krishnamoorthy. "Innovative Pedagogical Strategies of Streaming, Just-in-Time Teaching, and Scaffolding: A Case Study of Using Videos to Add Business Analytics Instruction Across a Curriculum." Journal of Information Technology Education: Innovations in Practice 20 (2021): 001–19. http://dx.doi.org/10.28945/4694.
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Aim/Purpose: Business analytics is a cross-functional field that is important to implement for a college and has emerged as a critically important core component of the business curriculum. It is a difficult task due to scheduling concerns and limits to faculty and student resources. This paper describes the process of creating a central video repository to serve as a platform for just in time teaching and the impact on student learning outcomes. Background: Industry demand for employees with analytical knowledge, skills, and abilities requires additional analytical content throughout the college of business curriculum. This demand needs other content to be added to ensure that students have the prerequisite skills to complete assignments. Two pedagogical approaches to address this issue are Just-in-Time Teaching (JiTT) and scaffolding, grounded in the Vygoskian concept of “Zone of Proximal Development. Methodology: This paper presents a case study that applies scaffolding and JiTT teaching to create a video repository to add business analytics instruction to a curriculum. The California Critical Thinking Skills Test (CCTST) and Major Field Test (MFT) scores were analyzed to assess learning outcomes. Student and faculty comments were considered to inform the results of the review. Contribution: This paper demonstrates a practical application of scaffolding and JiTT theory by outlining the process of using a video library to provide valuable instructional resources that support meaningful learning, promote student academic achievement, and improve program flexibility. Findings: A centrally created library is a simple and inexpensive way to provide business analytics course content, augmenting standard content delivery. Assessment of learning scores showed an improvement, and a summary of lessons learned is provided to guide implications. Recommendations for Practitioners: Pedagogical implications of this research include the observation that producing a central library of instructor created videos and assignments can help address knowledge and skills gaps, augment the learning of business analytics content, and provide a valuable educational resource throughout the college of business curriculum. Recommendation for Researchers: This paper examines the use of scaffolding and JiTT theories. Additional examination of these theories may improve the understanding and limits of these concepts as higher education evolves due to the combination of market forces changing the execution of course delivery. Impact on Society: Universities are tasked with providing new and increasing skills to students while controlling the costs. A centrally created library of instructional videos provides a means of delivering meaningful content while controlling costs. Future Research: Future research may examine student success, including the immediate impact of videos and longitudinally using video repositories throughout the curriculum. Studies examining the approach across multiple institutions may help to evaluate the success of video repositories. Faculty acceptance of centrally created video libraries and assignments should be considered for the value of faculty recruiting and use in the classroom. The economic impact on both the university and students should be evaluated.
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Walsh, John N., Michael P. O'Brien, and Darina M. Slattery. "Video Viewing Patterns Using Different Teaching Treatments: A Case Study Using YouTube Analytics." Research in Education and Learning Innovation Archives, no. 22 (June 24, 2019): 78. http://dx.doi.org/10.7203/realia.22.15389.
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This study explores the viewing patterns of 17 instructional videos in both a traditional and flipped classroom environment by 732 business students taking an IT-related module. While previous work has concentrated mainly on outputs(e.g. student satisfaction/results), this study focuses on how the nature of students’ interactions with videos can be determined through a deep analysis of analytics data. The main findings show that there were less interactions with the instructional videos in the flipped classroom environment compared to the traditional environment, and that videos were used more as a revision aid prior to exams (in both environments) than as an ongoing support to develop skills during term. Implications of this study include the need for regular monitoring of how instructional videos are being used during termand the importance of undertaking a deeper analysis of analytics data as the initial summary data may be misleading.
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Kumar, Lokesh, Pramod Kumar, and Parag Jain. "Object ID Tracking in Videos: A Review." International Transactions in Mathematical Sciences and Computer 15, no. 02 (2022): 157–66. http://dx.doi.org/10.58517/itmsc.2022.15204.
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Assigning and monitoring distinct identifiers to objects or entities in a video stream is known as object ID tracking in videos. For the purpose of tracking and analyzing object movement over time, computer vision, video analytics, and surveillance systems employ this technology extensively. Object detection is the first step in the process, wherein computer vision algorithms locate and identify things within individual video frames. This may entail methods like region-based Convolutional Neural Network (Faster R-CNN) or YOLO (You Only Look Once), which are object detection models based on deep learning. Based on the object's past trajectory, the tracking algorithm assists in predicting the object's position in the frame.
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Manu, Y. M., and G. K. Ravikumar. "Survey on Machine Learning Based Video Analytics Techniques." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 4989–95. http://dx.doi.org/10.1166/jctn.2020.9000.
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Video information has turned into the biggest wellspring of information expended all inclusive. Because of the fast development of applications which are related to video applications and requests of boosting for greater surpassing video administrations, video information volume has expanding violently around the world, which is the serious challenge for media processing, capacity and transmission. Video coding by packing recordings into a lot littler size is also key arrangements; in any case, its advancement has turned out to be soaked somewhat while the pressure proportion consistently develops over the most recent three decades. Machine inclining calculations, particularly those utilizing profound realizing, which are equipped for finding learning from unstructured huge information and giving information driven forecasts, give new chances to further updating video coding advancements. In this survey, we try to express an audit on AI based video encoding streamlining, expecting to furnish specialists with a solid establishment and rouse future improvements for information driven video coding. Initially, we investigate the portrayals furthermore, information about redundant videos. Besides, we audit the improvement of video coding models and key prerequisites. Hence, we exhibit a foundational review on the ongoing advances also difficulties related regarding AI based video coding enhancements from following key viewpoints, such as high effectiveness, low unpredictability and also high visual quality. Their work processes, delegate plans, exhibitions, focal points and disservices are broke down in detail. At long last, the difficulties and openings are recognized, which may furnish the scholastic and mechanical networks with preparation and potential bearings for eventual research.
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Chalkias, Ilias, Katerina Tzafilkou, Dimitrios Karapiperis, and Christos Tjortjis. "Learning Analytics on YouTube Educational Videos: Exploring Sentiment Analysis Methods and Topic Clustering." Electronics 12, no. 18 (September 19, 2023): 3949. http://dx.doi.org/10.3390/electronics12183949.
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The popularity of social media is continuously growing, as it endeavors to bridge the gap in communication between individuals. YouTube, one of the most well-known social media platforms with millions of users, stands out due to its remarkable ability to facilitate communication through the exchange of video content. Despite its primary purpose being entertainment, YouTube also offers individuals the valuable opportunity to learn from its vast array of educational content. The primary objective of this study is to explore the sentiments of YouTube learners by analyzing their comments on educational YouTube videos. A total of 167,987 comments were extracted and processed from educational YouTube channels through the YouTube Data API and Google Sheets. Lexicon-based sentiment analysis was conducted using two different methods, VADER and TextBlob, with the aim of detecting the prevailing sentiment. The sentiment analysis results revealed that the dominant sentiment expressed in the comments was neutral, followed by positive sentiment, while negative sentiment was the least common. VADER and TextBlob algorithms produced comparable results. Nevertheless, TextBlob yielded higher scores in both positive and negative sentiments, whereas VADER detected a greater number of neutral statements. Furthermore, the Latent Dirichlet Allocation (LDA) topic clustering outcomes shed light on various video attributes that potentially influence viewers’ experiences. These attributes included animation, music, and the conveyed messages within the videos. These findings make a significant contribution to ongoing research efforts aimed at understanding the educational advantages of YouTube and discerning viewers’ preferences regarding video components and educational topics.
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Córcoles, César, Germán Cobo, and Ana-Elena Guerrero-Roldán. "The Usefulness of Video Learning Analytics in Small Scale E-Learning Scenarios." Applied Sciences 11, no. 21 (November 4, 2021): 10366. http://dx.doi.org/10.3390/app112110366.
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A variety of tools are available to collect, process and analyse learning data obtained from the clickstream generated by students watching learning resources in video format. There is also some literature on the uses of such data in order to better understand and improve the teaching-learning process. Most of the literature focuses on large scale learning scenarios, such as MOOCs, where videos are watched hundreds or thousands of times. We have developed a solution to collect clickstream analytics data applicable to smaller scenarios, much more common in primary, secondary and higher education, where videos are watched tens or hundreds of times, and to analyse whether the solution is useful to teachers to improve the learning process. We have deployed it in a real scenario and collected real data. Furthermore, we have processed and presented the data visually to teachers for those scenarios and have collected and analysed their perception of their usefulness. We conclude that the collected data are perceived as useful by teachers to improve the teaching and learning process.
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Cruz-Oliver, Dulce M., Martha Abshire, Chakra Budhathoki, Melissa deCardi Hladek, Angelo Volandes, Lucas Jorgensen, and Debra Parker Oliver. "Comparison of Traditional Videos With Telenovelas for Hospice Family Caregivers Education." American Journal of Hospice and Palliative Medicine® 38, no. 10 (February 8, 2021): 1230–37. http://dx.doi.org/10.1177/1049909121991524.
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Background: While research has shown that hospice family caregivers (HFCG) seek additional information related to patient care, pain and symptom management, and self-care, it is unknown how the use of telenovela videos for education in hospice would be received by HFCG. Objective: To explore HFCG perceived benefits and challenges with the use of telenovelas as compared to traditional educational videos during online support group. Methods: A mixed methods study with a concurrent triangulated design that analyzed qualitative interviews and YouTube analytics report to identify how viewers responded (number of views and their feedback) to telenovela videos as compared to traditional educational videos. Results: Among 39 (n = 39) HFCGs, most participants were female (80%) of White/Caucasian race, with more than high school education (85%) and they were adult children of hospice cancer patient (49%). Comparing HFCG that viewed traditional videos with HFCG that viewed telenovela videos, the telenovela video was watched more (12% longer viewing duration) and caregivers reported better content recall with informative benefits, more follow up actions and reflection about their own hospice experience. Conclusion: Caregiver feedback indicated that watching the telenovela was engaging, acceptable and produced more conversations about patient care, than watching a non-telenovela format video. Further research is needed to test telenovela efficacy in enhancing HFCG outcomes.
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Mukherjee, Prerana, and Brejesh Lall. "Pedestrian Behavior Analytics on Dashcam Videos in Chaotic Environments." IEEE Sensors Journal 21, no. 14 (July 15, 2021): 15660–69. http://dx.doi.org/10.1109/jsen.2021.3062762.
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Tackett, Sean, David Green, Michael Dyal, Erin O'Keefe, Tanya Emmanuelle Thomas, Tiffany Nguyen, Duyen Vo, et al. "Use of Commercially Produced Medical Education Videos in a Cardiovascular Curriculum: Multiple Cohort Study." JMIR Medical Education 7, no. 4 (October 7, 2021): e27441. http://dx.doi.org/10.2196/27441.
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Background Short instructional videos can make learning more efficient through the application of multimedia principles, and video animations can illustrate the complex concepts and dynamic processes that are common in health sciences education. Commercially produced videos are commonly used by medical students but are rarely integrated into curricula. Objective Our goal was to examine student engagement with medical education videos incorporated into a preclinical Cardiovascular Systems course. Methods Students who took the first-year 8-week Cardiovascular Systems course in 2019 and 2020 were included in the study. Videos from Osmosis were recommended to be watched before live sessions throughout the course. Video use was monitored through dashboards, and course credit was given for watching videos. All students were emailed electronic surveys after the final exam asking about the course’s blended learning experience and use of videos. Osmosis usage data for number of video views, multiple choice questions, and flashcards were extracted from Osmosis dashboards. Results Overall, 232/359 (64.6%) students completed surveys, with rates by class of 81/154 (52.6%) for MD Class of 2022, 39/50 (78%) for MD/MPH Class of 2022, and 112/155 (72.3%) for MD Class of 2023. Osmosis dashboard data were available for all 359 students. All students received the full credit offered for Osmosis engagement, and learning analytics demonstrated regular usage of videos and other digital platform features. Survey responses indicated that most students found Osmosis videos to be helpful for learning (204/232, 87.9%; P=.001) and preferred Osmosis videos to the traditional lecture format (134/232, 57.8%; P<.001). Conclusions Commercial medical education videos may enhance curriculum with low faculty effort and improve students’ learning experiences. Findings from our experience at one medical school can guide the effective use of supplemental digital resources for learning, and related evaluation and research.
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Ge, Yongming, Vanessa Lin, Maureen Daum, Brandon Haynes, Alvin Cheung, and Magdalena Balazinska. "Demonstration of apperception." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 2767–70. http://dx.doi.org/10.14778/3476311.3476340.
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Many recent video applications---including traffic monitoring, drone analytics, autonomous driving, and virtual reality---require piecing together, combining, and operating over many related video streams. Despite the massive data volumes involved and the need to jointly reason (both spatially and temporally) about these videos, current techniques to store and manipulate such data are often limited to file systems and simple video processing frameworks that reason about a single video in isolation. We present Apperception, a new type of database management system optimized for geospatial video applications. Apperception comes with an easy to use data model to reason about multiple geospatial video data streams, and a programming interface for developers to collectively reason about the entities observed in those videos. Our demo will let users write queries over video using Apperception and retrieve (in real-time) both metadata and rendered video data. Users can also compare results and observe speedups achieved by using Apperception.
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Kadakia, Shevali, Catherine Stratton, Yinfei Wu, Josemari Feliciano, and Yetsa A. Tuakli-Wosornu. "The Accessibility of YouTube Fitness Videos for Individuals Who Are Disabled Before and During the COVID-19 Pandemic: Preliminary Application of a Text Analytics Approach." JMIR Formative Research 6, no. 2 (February 15, 2022): e34176. http://dx.doi.org/10.2196/34176.
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Background People with disabilities face barriers to in-person physical activity (PA), including a lack of adaptive equipment and knowledgeable instructors. Given this and the increased need for digital resources due to widespread COVID-19 lockdowns, it is necessary to assess the accessibility of digital fitness resources for people with disabilities. To investigate whether YouTube fitness content creators have made videos accessible to people with disabilities would be informative about access to PA during COVID-19 and could also provide insight into the feasibility of individuals who are disabled relying on YouTube for PA in a post–COVID-19 world. Objective This study aims to ascertain if disability-friendly PA videos on YouTube are accessible through searching general fitness terms and whether a change in the availability of accessible fitness resources for people with disabilities occurred on YouTube between before and during the COVID-19 pandemic on “Hospital/Medical Institutions,” “Individual(s),” and “Other(s)” channels. Secondary aims are to investigate if different categories of YouTube channels produce more accessible fitness content and highlight any disparities in disability-friendly PA content on YouTube. Methods A cross-sectional text analysis of exercise-related YouTube videos was conducted. The authors used Python (version 3.0) to access the YouTube database via its data application programming interface. Terms pertaining to PA that were searched on YouTube were at-home exercise, exercise at home, exercise no equipment, home exercise, home-based exercise, no equipment workout, and workout no equipment. Various elements (eg, view count and content generation) of the videos published between January 1 and June 30, 2019 (n=700), were compared to the elements of videos published between January 1 and June 30, 2020 (n=700). To capture a broad idea of disability-friendly videos on YouTube, videos were labeled “accessible” if they were found in the first 100 video results and if their title, description, or tags contained the following terms: para, paralympic, adaptive, adapted, disabled, disability, differently abled, disability-friendly, wheelchair accessible, and inclusive. Each video and channel were categorized as “Hospitals/Medical Institutions,” “Individuals,” or “Other(s).” Results The analysis revealed a statistically significant increase in viewership of fitness content on YouTube (P=.001) and in fitness content generated by Hospitals/Medical Institutions (P=.004). Accessible terms applicable to people with disabilities had minimal appearances in 2019 (21 videos) and 2020 (19 videos). None of the top viewed fitness videos that populated on YouTube from 2019 or 2020 were accessible. Conclusions The proportion of accessible disability-friendly videos remains diminutive relative to the prevalence of disability in the general population, revealing that disability-friendly videos are seldom findable on YouTube. Thus, the need for disability-friendly fitness content to be easily searched and found remains urgent if access to digital fitness resources is to improve.
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Saikrishnan, Venkatesan, and Mani Karthikeyan. "Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification on Surveillance Videos." Engineering, Technology & Applied Science Research 13, no. 5 (October 13, 2023): 11747–52. http://dx.doi.org/10.48084/etasr.6231.
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Surveillance videos are recordings captured by video recording devices for monitoring and securing an area or property. These videos are frequently used in applications, involving law enforcement, security systems, retail analytics, and traffic monitoring. Surveillance videos can provide valuable visual information for analyzing patterns, identifying individuals or objects of interest, and detecting and investigating incidents. Object detection and classification on video surveillance involves the usage of computer vision techniques to identify and categorize objects within the video footage. Object detection algorithms are employed to locate and identify objects within each frame. These algorithms use various techniques, namely bounding box regression, Convolutional Neural Networks (CNNs), and feature extraction to detect objects of interest. This study presents the Mayfly Optimization with Deep Learning-based Robust Object Detection and Classification (MFODL-RODC) method on surveillance videos. The main aim of the MFODL-RODC technique lies in the accurate classification and recognition of objects in surveillance videos. To accomplish this, the MFODL-RODC method follows a two-step process, consisting of object detection and object classification. The MFODL-RODC method uses the EfficientDet object detector for the object detection process. Besides, the classification of detected objects takes place using the Variational Autoencoder (VAE) model. The MFO algorithm is employed to enrich the performance of the VAE model. The simulation examination of the MFODL-RODC technique is performed on benchmark datasets. The extensive results accentuated the improved performance of the MFODL-RODC method over other existing algorithms with an output of 98.89%.
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Dontu, A. I., L. Gaiginschi, and A. Sachelarie. "Automatically collecting data traffic in intersections by using video analytics software for vehicle counting." IOP Conference Series: Materials Science and Engineering 1262, no. 1 (October 1, 2022): 012063. http://dx.doi.org/10.1088/1757-899x/1262/1/012063.
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Traffic volume and composition data are important for infrastructure planning and increase traffic safety. The actual survey techniques depend on the goals of the survey, the amount of traffic data and the human and financial resources available. The purpose of this research is to improve classical collecting data by creating an automatically collecting data traffic with the help of a video analytics software. Authors present in this paper a new method for automatically collecting data traffic using Camlytics, a video analytics software, which is automatic processing the videos. This method is less time consuming, don’t need a significant human resource and it is easy to apply. Also, this new method for counting the traffic may be used by the municipality for making a better fluidisation of traffic in the city by corelating the data traffic with the Intelligent Transportation Systems (I.T.S.).
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Nguyen, Dien Van, and Jaehyuk Choi. "Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge." Applied Sciences 10, no. 18 (September 14, 2020): 6391. http://dx.doi.org/10.3390/app10186391.
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Intelligent video analytics systems have come to play an essential role in many fields, including public safety, transportation safety, and many other industrial areas, such as automated tools for data extraction, and analyzing huge datasets, such as multiple live video streams transmitted from a large number of cameras. A key characteristic of such systems is that it is critical to perform real-time analytics so as to provide timely actionable alerts on various tasks, activities, and conditions. Due to the computation-intensive and bandwidth-intensive nature of these operations, however, video analytics servers may not fulfill the requirements when serving a large number of cameras simultaneously. To handle these challenges, we present an edge computing-based system that minimizes the transfer of video data from the surveillance camera feeds on a cloud video analytics server. Based on a novel approach of utilizing the information from the encoded bitstream, the edge can achieve low processing complexity of object tracking in surveillance videos and filter non-motion frames from the list of data that will be forwarded to the cloud server. To demonstrate the effectiveness of our approach, we implemented a video surveillance prototype consisting of edge devices with low computational capacity and a GPU-enabled server. The evaluation results show that our method can efficiently catch the characteristics of the frame and is compatible with the edge-to-cloud platform in terms of accuracy and delay sensitivity. The average processing time of this method is approximately 39 ms/frame with high definition resolution video, which outperforms most of the state-of-the-art methods. In addition to the scenario implementation of the proposed system, the method helps the cloud server reduce 49% of the load of the GPU, 49% that of the CPU, and 55% of the network traffic while maintaining the accuracy of video analytics event detection.
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Davidson, B., NM Alotaibi, BK Hendricks, and A. Cohen-Gadol. "P.081 Popularity of online multimedia educational resources in neurosurgery: Insights from The Neurosurgical Atlas project." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 45, s2 (June 2018): S37. http://dx.doi.org/10.1017/cjn.2018.183.
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Background:The Neurosurgical Atlas is a neurosurgical website with informative chapters and videos to promote excellence and safety in neurosurgical techniques. Here, we present our analysis of this website’s viewing data and describe how online neurosurgical resources are being utilized. We hope this will be a useful guide for neurosurgeons interested in online multimedia education. Methods: We analyzed Google Analytics data from The Neurosurgical Atlas between June 2016 and August 2017 which tracked user demographics, geographical location, and the videos watched. Views were also analyzed categorically by dividing videos into six neurosurgical topics and into basic and advanced levels as per their surgical complexity. Results: There were 246,259 website visits and 143,868 video plays. The most frequent age groups were 25-34 (44%) and 35-44 (24%). 71% of visitors were male. Most visitors were from the US (29.52%) and Brazil (6.43%). Website visits and video plays increased over time, with multiple peaks corresponding to promotional email updates. The six neurosurgical topics were all similarly popular. Conclusions: Our study presents the first piece of evidence demonstrating the feasibility and popularity of a free online resource in neurosurgical education. Our experience highlights the growing demand for free-access online chapters, anatomical illustrations, and operative videos.
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Pierce, David, and Geoffre Sherman. "Using Data Analytics to Create a Digital Strategy That Drives Engagement and Views on Social Media." Case Studies in Sport Management 9, S1 (January 1, 2020): S9—S12. http://dx.doi.org/10.1123/cssm.2019-0028.
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Students are placed into a consulting role with SPT, a sport marketing agency hired to help a sports organization create a new strategy for video content creation on social media. Students are provided a large data set in Tableau with analytics that hold the key to increasing the team’s engagement and views of videos on social media. Can your students find the insights in the data to drive a new video strategy for social media? Can they turn those insights into a creative content plan that will engage and win fans in the future? Students will have the opportunity to demonstrate creativity and innovation, data-based decision making, and digital literacy.
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Gao, Tianhao, Meng Zhang, Yifan Zhu, Youjian Zhang, Xiangsheng Pang, Jing Ying, and Wenming Liu. "Sports Video Classification Method Based on Improved Deep Learning." Applied Sciences 14, no. 2 (January 22, 2024): 948. http://dx.doi.org/10.3390/app14020948.
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Classifying sports videos is complex due to their dynamic nature. Traditional methods, like optical flow and the Histogram of Oriented Gradient (HOG), are limited by their need for expertise and lack of universality. Deep learning, particularly Convolutional Neural Networks (CNNs), offers more effective feature recognition in sports videos, but standard CNNs struggle with fast-paced or low-resolution sports videos. Our novel neural network model addresses these challenges. It begins by selecting important frames from sports footage and applying a fuzzy noise reduction algorithm to enhance video quality. The model then uses a bifurcated neural network to extract detailed features, leading to a densely connected neural network with a specific activation function for categorizing videos. We tested our model on a High-Definition Sports Video Dataset covering over 20 sports and a low-resolution dataset. Our model outperformed established classifiers like DenseNet, VggNet, Inception v3, and ResNet-50. It achieved high precision (0.9718), accuracy (0.9804), F-score (0.9761), and recall (0.9723) on the high-resolution dataset, and significantly better precision (0.8725) on the low-resolution dataset. Correspondingly, the highest values on the matrix of four traditional models are: precision (0.9690), accuracy (0.9781), F-score (0.9670), recall (0.9681) on the high-resolution dataset, and precision (0.8627) on the low-resolution dataset. This demonstrates our model’s superior performance in sports video classification under various conditions, including rapid motion and low resolution. It marks a significant step forward in sports data analytics and content categorization.
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Moon, Nazmun Nessa, Imrus Salehin, Masuma Parvin, Md Mehedi Hasan, Iftakhar Mohammad Talha, Susanta Chandra Debnath, Fernaz Narin Nur, and Mohd Saifuzzaman. "Natural language processing based advanced method of unnecessary video detection." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (December 1, 2021): 5411. http://dx.doi.org/10.11591/ijece.v11i6.pp5411-5419.
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<span>In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions.</span>
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Sumathi, J. k. "Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework." Wasit Journal of Computer and Mathematics Science 1, no. 1 (March 17, 2021): 1–8. http://dx.doi.org/10.31185/wjcm.vol1.iss1.3.
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The key advances in Computer Vision and Optical Image Processing are the emerging technologies nowadays in diverse fields including Facial Recognition, Biometric Verifications, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking and several others. Thus, these applications use image and live video processing for facilitating different applications for analyzing and forecasting." Computer vision is used in tons of activities such as monitoring, face recognition, motion recognition, object detection, among many others. The development of social networking platforms such as Facebook and Instagram led to an increase in the volume of image data that was being generated. Use of image and video processing software is a major concern for Facebook because the photos and videos that people post to the social network are doctored images. These kind of images are frequently cited as fake and used in malevolent ways such as motivating violence and death. You need to authenticate the questionable images before take action. It is very hard to ensure photo authenticity due to the power of photo manipulations. Image formation can be determined by image forensic techniques. The technique of image duplication is used to conceal missing areas.
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Bjarnadottir, Margret V., and Lawrence D. Stone. "Introduction: 2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research." INFORMS Journal on Applied Analytics 53, no. 5 (September 2023): 333–35. http://dx.doi.org/10.1287/inte.2023.intro.v53.n5.
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The judges for the 2022 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the four finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application describes the design and deployment of a generalized synthetic control, a powerful and innovative statistical method for identifying, in a noisy environment, retailing innovations that produce a small percentage improvement in a large volume of sales for Anheuser Busch Inbev. The remaining three papers describe an inverse control approach to allocating lung transplants that best meets targeted outcomes and has been implemented as the national lung allocation policy on March 9, 2023, across the United States; a human-centric, optimized parcel delivery system developed for Deutsche Post that saves money while meeting constraints learned dynamically from driver behavior; and an AI-based system developed for Alibaba that learns supplier behavior to improve replenishment ordering and inventory control. Supplemental Material: Full presentation videos with slides are available in the INFORMS Video Library at https://www.informs.org/Resource-Center/Video-Library and as electronic companions to the INFORMS Journal on Applied Analytics articles.
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Zhong, Chengzhang, Amy R. Reibman, Hansel A. Mina, and Amanda J. Deering. "Multi-View Hand-Hygiene Recognition for Food Safety." Journal of Imaging 6, no. 11 (November 7, 2020): 120. http://dx.doi.org/10.3390/jimaging6110120.
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A majority of foodborne illnesses result from inappropriate food handling practices. One proven practice to reduce pathogens is to perform effective hand-hygiene before all stages of food handling. In this paper, we design a multi-camera system that uses video analytics to recognize hand-hygiene actions, with the goal of improving hand-hygiene effectiveness. Our proposed two-stage system processes untrimmed video from both egocentric and third-person cameras. In the first stage, a low-cost coarse classifier efficiently localizes the hand-hygiene period; in the second stage, more complex refinement classifiers recognize seven specific actions within the hand-hygiene period. We demonstrate that our two-stage system has significantly lower computational requirements without a loss of recognition accuracy. Specifically, the computationally complex refinement classifiers process less than 68% of the untrimmed videos, and we anticipate further computational gains in videos that contain a larger fraction of non-hygiene actions. Our results demonstrate that a carefully designed video action recognition system can play an important role in improving hand hygiene for food safety.
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Rajagopal, Thenmozhi, Amutha Balakrishnan, Sreeram Valsalakumar, Thundil Rajagopal, and Senthilarasu Sundaram. "Application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in Drone Thermal Imaging for Solar Farm Monitoring." Energies 14, no. 24 (December 8, 2021): 8255. http://dx.doi.org/10.3390/en14248255.
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The impact of multimedia in day-to-day life and its applications will be increased greatly with the proposed model (MSVPC)–5G Multicast SDN network eminence video transmission obtained using PSO and cross layer progress in wireless nodes. The drone inspection and analysis in a solar farm requires a very high number of transmissions of various videos, data, animations, along with all sets of audio, text and visuals. Thus, it is necessary to regulate the transmissions of various videos due to a huge amount of bandwidth requirement for videos. A software-defined network (SDN) enables forwarder selection through particle swarm optimization (PSO) mode for streaming video packets through multicast routing transmissions. Transmission delay and packet errors are the main factors in selecting a forwarder. The nodes that transfer the videos with the shortest delay and the lowest errors have been calculated and sent to the destination through the forwarder. This method involves streaming to be increased with the highest throughput and less delay. Here, the achieved throughput is shown as 0.0699412 bits per second for 160 s of simulation time. Also, the achieved packet delivery ratio is 81.9005 percentage for 150 nodes on the network. All these metrics can be changed according to the network design and can have new results. Thus, the application of MSVPC- 5G Multicast SDN Network Eminence Video Transmission in drone thermal imaging helps in monitoring solar farms more effectively, and may lead to the development of certain algorithms in prescriptive analytics which recommends the best practices for solar farm development.
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Baker, Sally, Caitlin Field, Jung-Sook Lee, and Nicole Saintilan. "Supporting students' academic literacies in post-COVID-19 times: Developing digital videos to develop students' critical academic reading practices." Journal of University Teaching and Learning Practice 18, no. 4 (October 1, 2021): 35–49. http://dx.doi.org/10.53761/1.18.4.5.
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While embedding Academic Language and Literacies (ALL) instruction in discipline-specific courses is known to be effective, it is difficult to enact across the siloed university. Moreover, the move to online/ remote delivery during COVID-19 has necessitated greater focus on the development of online supports. This article reports on an effort to embed digital ALL support in a mandatory social research methods course, which we argue is particularly suited to academic literacies instruction. A series of digital videos were created to complement a literature review assignment, and were evaluated using video analytics, end-of-course student surveys, and individual interviews with tutors. Quantitative analysis of viewing patterns demonstrated that the majority of students accessed the videos multiple times, while qualitative data suggest that students generally had positive responses to the videos. However, thematic analysis of interviews with tutors showed that while they considered the content helpful, they also had reservations about the length and use of the videos. These findings clearly demonstrate the extent of the unmet need to integrate these types of approaches into undergraduate courses. We also argue that if universities wish to maintain currency in a shifting, globalised world, they must do more to foster the types of collaborative partnerships that facilitate effective ALL instruction in undergraduate coursework. These findings carry particular relevance for teaching and learning literacies in the wake of COVID-19, because digital video has become even more integral to higher education.
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Choo, Chee-Yan, Hui Poh Goh, and Chiau Ming Long. "Exploring learning analytics and motivated strategies for learning questionnaire (MSLQ) to understand pharmacy students’ learning profiles, motivation and strategies post-COVID." Pharmacy Education 23, no. 1 (October 26, 2023): 656–64. http://dx.doi.org/10.46542/pe.2023.231.656664.
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Background: First-year pharmacy students experienced on-site education after three years of studying online in isolation. Objectives: This study aimed to analyse newly enrolled first-year pharmacy students’ learning profiles using learning analytics from YouTube, and further understand their motivation and learning strategy during the transition period. Method: Learning Analytics (LA) were retrieved from YouTube analytics on instructor-generated videos. Students’ motivation and learning strategies were acquired using the Motivated Strategies for Learning Questionnaire (MSLQ) with a seven-point Likert score distributed online using Google Forms. Data were analysed using SPSS, and interview sessions were conducted with some of the students. Results: The LA showed most students referred to the instructor-generated video during study week. Students avoided the tutorial video with a view ratio lower than 1.0. This result correlated with the lower metacognitive mean compared to the cognitive level in the MSLQ analysis. Dependant on extrinsic components has increased their anxiety level. The peer learning scored higher than the help-seeking and was confirmed through interviews. Conclusion: This study offers insights into students learning motivation and strategies. Well-designed instructional learning activities may help in improving their problem-solving skills to boost their motivation. The teacher-student relationship may need more effort to build.
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Lew, Dong June, Kihyun Yoo, and Kwang Woo Nam. "DeepVQL: Deep Video Queries on PostgreSQL." Proceedings of the VLDB Endowment 16, no. 12 (August 2023): 3910–13. http://dx.doi.org/10.14778/3611540.3611583.
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The recent development of mobile and camera devices has led to the generation, sharing, and usage of massive amounts of video data. As a result, deep learning technology has gained attention as an alternative for video recognition and situation judgment. Recently, new systems supporting SQL-like declarative query languages have emerged, focusing on developing their own systems to support new queries combined with deep learning that are not supported by existing systems. The proposed DeepVQL system in this paper is implemented by expanding the PostgreSQL system. DeepVQL supports video database functions and provides various user-defined functions for object detection, object tracking, and video analytics queries. The advantage of this system is its ability to utilize queries with specific spatial regions or temporal durations as conditions for analyzing moving objects in traffic videos.
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Geri, Nitza, Amir Winer, and Beni Zaks. "A Learning Analytics Approach for Evaluating the Impact of Interactivity in Online Video Lectures on the Attention Span of Students." Interdisciplinary Journal of e-Skills and Lifelong Learning 13 (2017): 215–28. http://dx.doi.org/10.28945/3875.
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Aim/Purpose: As online video lectures rapidly gain popularity in formal and informal learning environments, one of their main challenges is student retention. This study investigates the influence of adding interactivity to online video lectures on students’ attention span. Background: Interactivity is perceived as increasing the attention span of learners and improving the quality of learning. However, interactivity may be regarded as an interruption, which distracts students. Furthermore, adding interactive elements to online video lectures requires additional investment of various resources. Therefore, it is important to investigate the impact of adding interactivity to online video lectures on the attention span of learners. Methodology: This study employed a learning analytics approach, obtained data from Google Analytics, and analyzed data of two Massive Open Online Courses (MOOCs) that were developed by the Open University of Israel in order to make English for academic purposes (EAP) courses freely accessible. Contribution: The paper provides important insights, based on quantitative empirical research, on: integrating interactive elements in online videos; the impact of video length; and differences between two groups of advanced and basic learners. Furthermore, it demonstrates how learning analytics may be used for improving instructional design. Findings: The findings suggest that interactivity may increase the attention span of learners, as measured by the average online video lecture viewing completion percentage, before and after the addition of interactivity. However, when the lecture is longer than about 15 minutes, the completion percentages decrease, even after adding interactive elements. Recommendations for Practitioners: Adding interactivity to online video lectures and controlling their length is expected to increase the attention span of learners. Recommendation for Researchers: Learning analytics is a powerful quantitative methodology for identifying ways to improve learning processes. Impact on Society: Providing practical insights on mechanisms for increasing the attention span of learners is expected to improve social inclusion. Future Research: Discovering further best practices to improve the effectiveness of online video lectures for diverse learners.
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Shoufan, Abdulhadi. "Estimating the cognitive value of YouTube's educational videos: A learning analytics approach." Computers in Human Behavior 92 (March 2019): 450–58. http://dx.doi.org/10.1016/j.chb.2018.03.036.
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Vishaka Gayathri, D., Shrutee Shree, Taru Jain, and K. Sornalakshmi. "Real Time System for Human Identification and Tracking from Surveillance Videos." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 244. http://dx.doi.org/10.14419/ijet.v7i3.12.16034.
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The need for intelligent surveillance systems has raised the concerns of security. A viable system with automated methods for person identification to detect, track and recognize persons in real time is required. The traditional detection techniques have not been able to analyze such a huge amount of live video generated in real-time. So, there is a necessity for live streaming video analytics which includes processing and analyzing large scale visual data such as images or videos to find content that are useful for interpretation. In this work, an automated surveillance system for real-time detection, recognition and tracking of persons in video streams from multiple video inputs is presented. In addition, the current location of an individual can be searched with the tool bar provided. A model is proposed, which uses a messaging queue to receive/transfer video feeds and the frames in the video are analyzed using image processing modules to identify and recognize the person with respect to the training data sets. The main aim of this project is to overcome the challenges faced in integrating the open source tools that build up the system for tagging and searching people.
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Nicolaidou, Despo, and Iolie Nicolaidou. "Learning Analytics: A case study of Adaptive Video Activities." European Conference on e-Learning 21, no. 1 (November 8, 2022): 484–88. http://dx.doi.org/10.34190/ecel.21.1.945.
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Adaptive elements are integrated in activities to facilitate personalization of the learning process and provide learning analytics for each student. Following the digital storytelling trends, integrating adaptive activities in interactive videos facilitates student engagement in crucial topics and personalizes the learning path to each individual student’s pace and learning ability. However, it is considered challenging for teachers to design an effective cluster of adaptive activities and to make sense of the learning analytics that are provided. A literature review was conducted to examine how teachers make use of user analytics in real circumstances. It showed that despite the variety of existing tools that can facilitate teachers in collecting learning analytics, the raw data require further analysis for the teacher to be able to understand the students’ individual paths and more training is required so that teachers can effectively interpret these data. This paper is based on a case study conducted to examine how learning analytics are used and what tools can support teachers in making sense of their students’ data. Moreover, it reveals how students perceive adaptive activities, in relevance to their flow and usability, as part of the overall goal of the activity, which focused on environmental awareness. To understand the processes involved around the implementation of adaptive activities, an interactive video with adaptive activities was designed and implemented in a classroom of 12 students (M=12.5 years old). The methodology followed a quantitative approach. A structured questionnaire was used to understand students’ perspectives regarding the flow and usability of the adaptive activities. Considering students’ perspectives on the flow of the adaptive-interactive video activity, students’ level of absorption and the natural progress of the activities received scores of 3.8/5 and 4/5, respectively. The usability of the activity received an average of 75.4 as a System Usability Score (SUS), which is considered above average. The results reveal that both flow and usability are essential for the effective implementation of adaptive activities. This research study recommends further studies of the topic to understand how learning analytics can become manageable and/or better integrated in software enabling the creation of adaptive activities.
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Ahmed, Zayneb, Abir Jaafar Hussain, Wasiq Khan, Thar Baker, Haya Al-Askar, Janet Lunn, Raghad Al-Shabandar, Dhiya Al-Jumeily, and Panos Liatsis. "Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics." Remote Sensing 12, no. 6 (March 20, 2020): 1004. http://dx.doi.org/10.3390/rs12061004.
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The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search.
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Cremer, Stefan, and Claudia Loebbecke. "Artificial Intelligence Imagery Analysis Fostering Big Data Analytics." Future Internet 11, no. 8 (August 15, 2019): 178. http://dx.doi.org/10.3390/fi11080178.
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In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.
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Surabani, Santorini. "Harnessing Text and Web Analytics to Enhance Decision-Making in Job Opportunity Categorization." International Journal of Information Technology and Computer Science Applications 2, no. 2 (May 10, 2024): 1–8. http://dx.doi.org/10.58776/ijitcsa.v2i2.145.
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Text analytics is defined as a method of analyzing compilations of structured text such as dates, times, locations, semi structured text, such as HTML and JSON as well as unstructured text, such as word documents, videos, and images, to extract and discover trends and relationships without requiring the exact words or terms to convey those concepts. Web analytics on the other hand is the technology that collects, measures, analyses, and provides reports of data on how users use websites and web applications. It is used to track a number of aspects of direct user-website interactions, such as the number of visits, time spent on the site, and click pathway. It also aids in the identification of user interest areas and the enhancement of web application features. We used clustering techniques to categorize the job opportunities that are available for the job seekers. By implementing text analytics, text data may be grouped with the goal of providing outcomes in the form of word frequency distribution, pattern identification, and predictive analytics. Text analytics may create one-of-a-kind values to use in the improvement of decision-making and business processes, as well as the development of new business models.
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Agrawal, Priyanka. "Smart Surveillance System using Face Tracking." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 25, 2021): 2613–17. http://dx.doi.org/10.22214/ijraset.2021.35567.
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The face is seen as a key component of the human body, and humans utilise it to identify one another. Face detection in video refers to the process of detecting a person's face from a video sequence, while face tracking refers to the process of tracking the person's face throughout the video. Face detection and tracking has become a widely researched issue due to applications such as video surveillance systems and identifying criminal activity. However, working with videos is tough due to problems such as bad illumination, low resolution, and atypical posture, among others. It is critical to produce a fair analysis of various tracking and detection strategies in order to fulfil the goal of video tracking and detection. Closed-circuit television (CCTV) technology had a significant impact on how crimes were investigated and solved. The material used to review crime scenes was CCTV footage. CCTV systems, on the other hand, just offer footage and do not have the ability to analyse it. In this research, we propose a system that can be integrated with the CCTV footage or any other video input like webcam to detect, recognise, and track a person of interest. Our system will follow people as they move through a space and will be able to detect and recognise human faces. It enables video analytics, allowing existing cameras to be combined with a system that will recognise individuals and track their activities over time. It may be used for remote surveillance and can be integrated into video analytics software and CCTV security solutions as a component. It may be used on college campuses, in offices, and in shopping malls, among other places.
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Tan, Sabine, Michael Wiebrands, Kay O’Halloran, and Peter Wignell. "Analysing student engagement with 360-degree videos through multimodal data analytics and user annotations." Technology, Pedagogy and Education 29, no. 5 (October 19, 2020): 593–612. http://dx.doi.org/10.1080/1475939x.2020.1835708.
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Quick, Virginia, Kirsten W. Corda, Jennifer Martin-Biggers, Barbara Chamberlin, Donald W. Schaffner, and Carol Byrd-Bredbenner. "Short food safety videos promote peer networking and behavior change." British Food Journal 117, no. 1 (January 5, 2015): 78–93. http://dx.doi.org/10.1108/bfj-09-2013-0270.
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Purpose – The purpose of this paper is to create a series of 30-60-second short videos to promote improved food safety behaviors of middle school youth, determine the feasibility of disseminating the videos through peer networks, and measure their effects on food safety attitudes, perceived social norms, and behaviors of youth. Design/methodology/approach – Food safety content specialists, learning experts, programmers, illustrators, project managers, instructional designers, scriptwriters, and stakeholders were involved in creation of the Don’t Be Gross short videos before evaluation by middle school youth (sixth to eighth grades). The experimental group (n=220) completed the following activities at about one-week intervals: pre-test, viewed videos, post-test, and follow-up test. The control group (n=112) completed the same activities at similar intervals but did not have access to the videos until after the follow-up test. Findings – Controlling for grade and gender, linear mixed-effects models revealed significant time by group effects for participants’ perceived susceptibility to foodborne illness; intentions to perform recommended food safety behaviors approached significance. Additionally, compared to the pre-test, the experimental group perceived their friends as being significantly more confident in performing food safety behaviors at post- and follow-up tests. Google Analytics data revealed that the bounce rate from the home page of the videos was low (38 percent) suggesting that the videos were engaging. Originality/value – The Don’t Be Gross videos were liked by youth and shared among their peers and may have the potential to promote positive food safety behaviors and intentions among youth.