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Educative Sensemaking on Social Media: An Empirical Investigation of Informal Learning on YouTube
No full textEducational videos on social media are widely used in informal learning. However, empirical studies hardly look into sensemaking, a key aspect in the construction of meaning and knowledge, of educational videos on social media in informal learning, despite the growing interest and practice in educative sensemaking. This study addresses this research gap. We draw upon sensemaking theories and investigate how the physical properties of educational videos affect sensemaking. Our research shows how information control, anchor, and noise are associated with committed interpretation in the learning communities to understand the scientific inquiry at hand with data from YouTube educational videos. This study makes timely contributions to the literature on the educative sensemaking in informal learning on social media. It also offers insights into the better design of educational videos to facilitate sensemaking and informal learning
Privacy Assessment Breakthrough: A Design Science Approach to Creating a Unified Methodology
Get PDFRecent changes have increased the need for and awareness of privacy assessments. Organizations focus primarily on Privacy Impact Assessments (PIA) and Data Protection Impact Assessments (DPIA) but rarely take a comprehensive approach to assessments or integrate the results into a privacy risk program. There are numerous industry standards and regulations for privacy assessments, but the industry lacks a simple unified methodology with steps to perform privacy assessments. The objectives of this research project are to create a new privacy assessment methodology model using the design science methodology, update industry standards and present training for conducting privacy assessments that can be adapted by organizations of any shape, size, industry, or geography.
The purpose of this project is to create a unified privacy assessment methodology that will assist organizations with privacy and compliance obligations by simplifying the assessment process with steps that are repeatable and can be adopted by organizations of any shape, size, industry, or geography. The project will address three research questions. What steps are needed to conduct a unified privacy assessment? What inputs and outputs are needed to complete each step in the assessment? What variables are needed as it relates to assessments? The research project was conducted using design science methodology following the engineering lifecycle for a technical action research project. The project created a new privacy assessment methodology model with five steps. The privacy assessment methodology was evaluated with a use case at an organization based in the US with offices globally.
The research project created a new unified privacy assessment methodology as set forth in the beginning of the project. The model was evaluated and validated through realworld business use case of a global healthcare organization and a dozen training sessions presenting this research. This work will not stop with this project, it merely sets the path for additional innovative and industry impacting solutions
Survey of IoT Users
Get PDFThe rising popularity of IoT (internet of things) devices in the home have helped the IoT industry succeed but IoT’s target demographic have not been defined. An IoT device is anything that can connect to the internet while also sharing data without human intervention. Normal household appliances do not fall into the category of an IoT device by default, they need to have internet connection to be an IoT device. In the home, this includes things such as smart fridges or even computer-interfaced clothes washers. These IoT Devices can be all over the home and can even include cameras, speakers, oven appliances, toilets, and more. The terms IoT devices and smart devices will be used interchangeably. The best way to accurately test for who the average consumer in IoT devices is to gather IoT user feedback. Although not every consumer is looking for the same thing from an IoT device, most consumers do not fully understand what an IoT device does for them. The survey includes people across all education levels and income levels and all of the respondents were from the United States. The ages ranged from 18 years old to 65 years old. With a focus on those within the age range of 25-45 as this was the most popular age range within the study. To that end this study seeks to understand who the average consumer of smart devices and what they think about smart devices by surveying people in the U.S. aged 18-65 and analyzing the response data to measure what smart devices they own, what ages they are, and what their household income is
Comparative Analysis of Forensic Tool Testing Frameworks to Use With Mobile Forensic Tools
Get PDFMobile forensics is a well-established, constantly evolving field in digital forensics. The proliferation of mobile devices requires the constant development of mobile forensics tools. With the rapid development of these tools, software vulnerabilities can become commonplace. Software vulnerabilities reside within the forensic tool’s software, meaning the tool should be tested and verified before field use. We aimed to propose a recommended tool testing framework for mobile forensic tools. To recommend a tool testing framework, we analyze the frameworks by utilizing a high-level comparative analysis methodology. The methodology allowed us to identify, analyze, and compare tool testing frameworks with the goal of recommending one. The results of the comparative analysis indicate that the validation verification framework is recommended for testing mobile forensic tools. We found that the framework is adaptative for forensic labs to perform their own testing in a scientifically verifiable manner. Furthermore, the recommended framework accounts for the time cost value of testing a forensic tool for software vulnerabilities. The findings show that current testing frameworks cannot support a broad range of tool testing scenarios due to the time cost value encountered when testing tools. Further research on testing the recommended framework would be required to verify the validation framework quantitively
Identification and Characterization of Pythium Species Isolated From Commercial Alfalfa Fields in South Dakota
Get PDFAlfalfa (Medicaga sativa) seeds are often planted when environmental conditions are optimal for Pythium seed rot and damping-off, which is caused by various different Pythium species. Pythium seed rot can devastate alfalfa stands requiring them to be completely replanted. South Dakota plants the second most acres of alfalfa in the United States, and surveys describing Pythium spp. causing disease on alfalfa In the state were lacking. In this study, putative Pythium isolates were baited from soil samples collected In eastern South Dakota using the rolled•towel technique. Pure cultures were obtained, and Pythium spp. were identified using ITS and cox1 sequences. The Pythium isolates were assessed for pathogenicity on alfalfa using a culture plate method. P. sylvaticum was the most frequently Isolated alfalfa pathogen. Several commercial fungicides were evaluated against Pythium spp. using agar plate-based assays. CruiserMaxx (mefenoxam, fludioxonil, thiamethoxam) demonstrated strong activity against Pythium isolates. This study establishes the presence of a diverse array of Pythium spp. that are alfalfa pathogens in South Dakota, and the fungicide sensitivity tests provide growers with localized information to select effective fungicide treatments. Increased grower awareness to Pythium diseases of alfalfa may lead to better disease management and increased alfalfa yields in South Dakota.dshttps://scholar.dsu.edu/erposters/1009/thumbnail.jp
The role of age in organizational knowledge sharing: A systematic literature review
Get PDFMany organizations emphasize the importance of sharing knowledge to create a supportive learning culture. However, motivating employees to share knowledge can be challenging, especially in an age-diverse organization. The purpose of this systematic literature review is to explore recent studies that focus on age and knowledge sharing to identify gaps in the literature and guide future research directions. With the recent increase in studies exploring the influence of age on knowledge sharing, this review categorizes articles from the Web of Science, IEEE, Science Direct, and the ACM Digital Library that focus on age and knowledge sharing into key elements of process improvement: people, process, and technology. Findings from this search process reveal most studies focus on people with a secondary focus on motivation, skills, and self-efficacy, as well as processes relating to knowledge sharing. Few articles have technology as a central focus. While age undoubtedly influences people and knowledge sharing processes, more research is needed to understand the influence of other factors, including technology and other age-related concepts
How Attractive is Artificial Intelligence? An Empirical Study of User Adoption of AI- Enabled Applications
Get PDFPrevailing AI technologies such as machine learning (ML), natural language processing (NLP), speech recognition, image recognition, etc., are being incorporated into a wide variety of existing and new applications. As an emerging technology, these AI applications (AI apps) have their own unique characteristics, such as machine learning capability, human-like interacting capability, knowledge representing and reasoning capability, and relative autonomy. Such characteristics of AI apps can help users complete their tasks effectively and efficiently. Further, AI apps combined with personal devices such as smartphones, tablets, laptops, and IoTs, provide users with utmost accessibility and pervasiveness. However, even though AI apps are often conveniently accessed and are free to use, people may not use them regularly. A recent survey showed that while 98% of iPhone users had used Siri, only 30% used it regularly and 70% rarely or only occasionally used it (Cowan et al., 2017). How attractive are AI apps to individual users? Why do people tend to or refuse to use AI apps? Research has been conducted on AI technology, its advantages, side effects, limitations, and its forthcoming impact on society. However, very little research has focused on individuals’ adoption of AI apps and how AI apps’ unique characteristics impact an individual’s intention to use AI apps. Accordingly, this study conducts an empirical study examining factors affecting an individual’s intention to use AI apps with a proposed theoretical framework based on the Task-technology Fit (TTF) model. These factors include characteristics of task, characteristics of AI apps, the match between tasks and AI apps, users’ self-efficacy in using AI apps, and users’ perceived risks when using AI apps. An online survey is conducted on users who have installed AI apps on their computers or mobile devices to test the research model. SPSS and SmartPLS are employed to analyze the collected data and test the hypotheses. This study addresses the AI acceptance issue from an individual perspective. Theoretically, this study focuses on how the unique characteristics of AI apps influence the task-technology fit and in turn influence the intention of use. So far, it has seldom been empirically studied in the literature and thus enriches the general TTF model and its relevance to emerging technology. Practically, the findings are also expected to help AI application developers better understand individual users’ behavior regarding using their applications. Most notably, the findings can help researchers and developers evaluate the relative importance of AI app features, providing insights into the technology characteristics and identifying priorities for further research and development
Privacy and Online Social Networks: A Systematic Literature Review of Concerns, Preservation, and Policies
Get PDFBackground: Social media usage is one of the most popular online activities, but with it comes privacy concerns due to how personal data are handled by these social networking sites. Prior literature aimed at identifying users’ privacy concerns as well as user behavior associated with privacy mitigation strategies and policies. However, OSN users continue to divulge private information online and privacy remains an issue. Accordingly, this review aims to present extant research on this topic, and to highlight any potential research gaps. Method: The paper presents a systematic literature review for the period 2006 - 2021, in which 33 full papers that explored privacy concerns in online social networks (OSN), users’ behavior associated with privacy preservation strategies and OSN privacy policies were examined. Results: The findings indicate that users are concerned about their identity being stolen, the disclosure of sensitive information by third-party applications and through data leakage and the degree of control users have over their data. Strategies such as encryption, authentication, and privacy settings configuration, can be used to address users’ concerns. Users generally do not leverage privacy settings available to them, or read the privacy policies, but will opt to share information based on the benefits to be derived from OSNs. Conclusion: OSN users have specific privacy concerns due primarily to the inherent way in which personal data are handled. Different preservation strategies are available to be used by OSN users. Policies are provided to inform users, however, these policies at times are difficult to read and understand, but studies show that there is no direct effect on the behavior of OSN users. Further research is needed to elucidate the correlation between the relative effectiveness of different privacy preservation strategies and the privacy concerns exhibited by users. Extending the research to comparatively assess different social media sites could help with better awareness of the true influence of privacy policies on user behavior
