2,824 research outputs found
International Summerschool Computer Science 2014: Proceedings of Summerschool 7.7. - 13.7.2014
Get PDFProceedings of International Summerschool Computer Science 201
Analysis of Sign Language Facial Expressions and Deaf Students\u27 Retention Using Machine Learning and Agent-based Modeling
Get PDFThere are currently about 466 million people worldwide who have a hearing disability, and that number is expected to increase to 900 million by 2050. About 15% of adult Americans have hearing disabilities and about every three in 1,000 U.S. children are born with hearing loss in one or both ears. The World Health Organization (WHO) estimates that unaddressed hearing loss poses an annual global cost of $980 billion, including cost of educational support, loss of productivity, and societal costs. These are all evident that people with hearing loss are experiencing several kinds and levels of difficulties. In this dissertation, we are addressing two main challenges of hearing impaired people; sign language recognition and post-secondary education. Both sign language recognition and reliable education systems that properly support the deaf community are essential needs of the globe and in this dissertation we aim to attack these exact problems. For the first part, we introduce novel dataset and methodology using machine learning while for the second part, a novel agent-based model framework is proposed. Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this dissertation, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of sad , surprise , fear , angry , neutral , disgust , and happy . We also considered the None class if the image\u27s facial expression could not be described by any of the emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems. In addition, post-secondary education persistence is the likelihood of a student remaining in post-secondary education. Although statistics show that post-secondary persistence for deaf students has increased recently, there are still many obstacles obstructing students from completing their post-secondary degree goals. Therefore, increasing the persistence rate is crucial to increase education and work goals for deaf students. In this work, we present an agent-based model using NetLogo software for the persistence phenomena of deaf students. We consider four non-cognitive factors: having clear goals, social integration, social skills, and academic experience, which influence the departure decision of deaf students. Progress and results of this work suggest that agent-based modeling approaches promise to give better understanding of what will increase persistence
2015-2016 Lindenwood University Graduate Course Catalog
Get PDFLindenwood University Graduate Course Cataloghttps://digitalcommons.lindenwood.edu/catalogs/1174/thumbnail.jp
The Determinant Factor of Learning Motivation on High School Students' Learning Outcomes in Covid-19 Outbreak in Indonesia
Get PDFDuring the pandemic, students are at home and learning from home online. Learning through online methods requires teachers and students to adapt and motivate themselves to live it. This study aimed to determine the determinants of learning motivation factors on high school students’ achievement in the era of the Covid-19 pandemic in Indonesia. The research method was quantitative research conducted on 60 students of class XI SMA class 2 of high school in three private high schools in Surabaya, Indonesia. By selecting purposive sampling based on the researcher's inclusion criteria and the data collection using google form. Then the collected data were processed using statistical methods using the correlation approach (correlational research) and linear regression. This study was to test the hypothesis related to the correlation between learning motivation variables and learning achievement during online learning in the Covid-19 pandemic era. Based on the correlation analysis of motivation on learning achievement, the correlation coefficient (r) = 0.458 with a significant value of 0.000 <0.05. Because with a significance value of 0.000 <0.05, Ho was rejected, and H1 was accepted. There was a very significant relationship between the two variables between learning motivation and learning achievement. Meanwhile, from the coefficient value of 0.458 (in the range 0.41 - 0.6), it could be concluded that the correlation between learning motivation scores and learning achievement was moderate. In other words, the higher the motivation to learn, the higher the learning achievement of students. Then according to the t-test, it could be seen that the significance value of 0.000 was smaller than 0.05; it could be concluded that Ho was rejected and H1 was accepted, which meant that there was a significant influence between learning motivation and students achievement. Keywords: learning motivation, learning outcomes, students, Covid-19 DOI: 10.7176/JEP/12-18-02 Publication date:June 30th 202
2016-2017 Lindenwood University Graduate Course Catalog
Get PDFLindenwood University Graduate Course Cataloghttps://digitalcommons.lindenwood.edu/catalogs/1177/thumbnail.jp
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