KEYWORDS: COVID 19, Data analysis, Data conversion, Web 2.0 technologies, Visualization, Medicine, Data processing, Data modeling, Data visualization, Data acquisition
In this paper, we determine associations between social media use and beliefs in conspiracy theories and misinformation among African American communities in Tuskegee County. We will study a high community with significant social problems. The primary goal of this work is to visualize how information (both false and accurate) flows through social media, traditional media, and social networks to influence decision-making in rural areas. The second goal is to examine how various other factors moderate this influence. We will examine the impacts of education, age, and other demographics, as well as measure Gigerenzer’s concept of “risk literacy” which examines the accuracy of people’s perceived notions of risk. We will develop our model based on data collected from in-person meetings and town halls, questionnaires, and other information collected to measure peoples’ social media use, social networks, and their beliefs about issues such as the efficacy of COVID vaccines, their trust in the health care system, their beliefs about mental health.
This paper is concerned with the correlation in the African American community between their social media usage and their degree of Covid vaccine hesitancy and other general health attributes. In the past, various studies have found associations between social media use and beliefs in conspiracy theories and misinformation, however, most of these studies focus on large data sets which lack in accuracy or too general and lack of sufficient quantitative methodologies such as machine learning techniques. In this work, we experimented a pilot study with a small number of African American community regarding COVID-19 vaccine and their social beliefs. In other words, this pilot study is important for the improvement of the quality and efficiency of the main study in the future. In addition, it helps to understand the pattern of a certain community regarding certain views.
Measuring classroom engagement is an important but challenging task in education. In this paper, we present an automated method for the assessment of the degree of classroom engagement using computer vision techniques that integrate data from multiple sensors, including the front and back of the student's seating arrangement. The students' engagement is evaluated based on attributes such as facial expression, gesture, head position, and distractions visible from the frontal view of the students. Moreover, using the videos from the back of the classroom, the professor's teaching content as well as their alignment with student engagement, are calculated. We leverage deep learning methods to extract emotion and behavior features to aid in the evaluation of engagement. These AI methods will quantify the classroom engagement process.
Classroom engagement is one important factor that determines whether students learn from lectures. Most of the traditional classroom assessment tools are based on summary judgements of students in the form of student surveys filled out in class or online once a semester. Such ratings are often bias and do not capture the real time teaching of professors. In addition, they fail for the most part to capture the locus of teaching and learning difficulties. They cannot differentiate whether ongoing poor performance of students is a function of the instructor's lack of teaching skill or the student's lack of engagement in the class. So, in order to streamline and improve the evaluation of classroom engagement, we introduce human gestures as additional tool to improve teaching evaluation along with other techniques. In this paper we report the results of using a novel technique that uses a semi-automatic computer vision based approach to obtain accurate prediction of classroom engagement in classes where students do not have digital devices like laptops and, cellphones during lectures. We conducted our experiment in various class room sizes at different times of the day. We computed the engagement through a semi- automatic process (using Azure, and manual observation). We combined our initial computational algorithms with human judgment to build confidence the validity of the results. Application of the technique in the presence of distractors like laptops and cellphones is also discussed.
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