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Direct linkSung, Shannon H.; Li, Chenglu; Chen, Guanhua; Huang, Xudong; Xie, Charles; Massicotte, Joyce; Shen, Ji – Journal of Science Education and Technology, 2021
In this paper, we demonstrate how machine learning could be used to quickly assess a student's multimodal representational thinking. Multimodal representational thinking is the complex construct that encodes how students form conceptual, perceptual, graphical, or mathematical symbols in their mind. The augmented reality (AR) technology is adopted…
Descriptors: Observation, Artificial Intelligence, Knowledge Representation, Grade 9
Say What? Automatic Modeling of Collaborative Problem Solving Skills from Student Speech in the Wild
Download full textPugh, Samuel L.; Subburaj, Shree Krishna; Rao, Arjun Ramesh; Stewart, Angela E. B.; Andrews-Todd, Jessica; D'Mello, Sidney K. – International Educational Data Mining Society, 2021
We investigated the feasibility of using automatic speech recognition (ASR) and natural language processing (NLP) to classify collaborative problem solving (CPS) skills from recorded speech in noisy environments. We analyzed data from 44 dyads of middle and high school students who used videoconferencing to collaboratively solve physics and math…
Descriptors: Problem Solving, Cooperation, Middle School Students, High School Students
Gerard, Libby F.; Ryoo, Kihyun; McElhaney, Kevin W.; Liu, Ou Lydia; Rafferty, Anna N.; Linn, Marcia C. – Journal of Educational Psychology, 2016
In 4 classroom experiments we investigated uses for technologies that automatically score student generated essays, concept diagrams, and drawings in inquiry curricula. We used the automatic scores to assign typical and research-based guidance and studied the impact of the guidance on student progress. Seven teachers and their 897 students…
Descriptors: Automation, Scoring, Guidance, Inquiry
