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Walter L. Leite; Samrat Roy; Nilanjana Chakraborty; George Michailidis; A. Corinne Huggins-Manley; Sidney K. D'Mello; Mohamad Kazem Shirani Faradonbeh; Emily Jensen; Huan Kuang; Zeyuan Jing – Grantee Submission, 2022
This study presents a novel video recommendation system for an algebra virtual learning environment (VLE) that leverages ideas and methods from engagement measurement, item response theory, and reinforcement learning. Following Vygotsky's Zone of Proximal Development (ZPD) theory, but considering low affect and high affect students separately, we…
Descriptors: Artificial Intelligence, Video Technology, Technology Uses in Education, Program Effectiveness
Chen, Zhanwen; Li, Shiyao; Rashedi, Roxanne; Zi, Xiaoman; Elrod-Erickson, Morgan; Hollis, Bryan; Maliakal, Angela; Shen, Xinyu; Zhao, Simeng; Kunda, Maithilee – Grantee Submission, 2020
Modern social intelligence includes the ability to watch videos and answer questions about social and theory-of-mind-related content, e.g., for a scene in "Harry Potter," "Is the father really upset about the boys flying the car?" Social visual question answering (social VQA) is emerging as a valuable methodology for studying…
Descriptors: Visual Stimuli, Questioning Techniques, Social Cognition, Video Technology
Mongkhonvanit, Kritphong; Kanopka, Klint; Lang, David – Grantee Submission, 2019
MOOCs and online courses have notoriously high attrition [1]. One challenge is that it can be difficult to tell if students fail to complete because of disinterest or because of course difficulty. Utilizing a Deep Knowledge Tracing framework, we account for student engagement by including course interaction covariates. With these, we find that we…
Descriptors: Online Courses, Large Group Instruction, Knowledge Level, Learner Engagement
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Lawrence Angrave; Jiaxi Li; Ninghan Zhong – Grantee Submission, 2022
To efficiently create books and other instructional content from videos and further improve accessibility of our course content we needed to solve the scene detection (SD) problem for engineering educational content. We present the pedagogical applications of extracting video images for the purposes of digital book generation and other shareable…
Descriptors: Instructional Materials, Material Development, Video Technology, Course Content
Schussler, Deborah; Frank, Jennifer; Lee, Tsan-Kuang; Mahfouz, Julia – Grantee Submission, 2017
Nearly one in three students in the United States today is negatively impacted by bullying. Teachers can play a critical role in stopping bullying-related violence, but many struggle with how to engage students in difficult conversations. Traditional classroom-based pedagogy used to teach communication skills (e.g., modeling & role-play) is…
Descriptors: Computer Simulation, Artificial Intelligence, Role Playing, Teaching Methods
Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay – Grantee Submission, 2016
Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students -- explanations, feedback, and other pedagogical interactions. Considering the…
Descriptors: Artificial Intelligence, Educational Technology, Student Needs, Electronic Publishing