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Download full textYang, Tsung-Yen; Baker, Ryan S.; Studer, Christoph; Heffernan, Neil; Lan, Andrew S. – International Educational Data Mining Society, 2019
"Sensor-free" detectors of student affect that use only student activity data and no physical or physiological sensors are cost-effective and have potential to be applied at large scale in real classrooms. These detectors are trained using student affect labels collected from human observers as they observe students learn within…
Descriptors: Active Learning, Measurement Techniques, Intelligent Tutoring Systems, Educational Technology
Hutt, Stephen; Grafsgaard, Joseph F.; D'Mello, Sidney K. – Grantee Submission, 2019
We developed generalizable affect detectors using 133,966 instances of 18 affective states collected from 69,174 students who interacted with an online math learning platform called Algebra Nation over the entire school year. To enable scalability and generalizability, we used generic interaction features (e.g., viewing a video, taking a quiz),…
Descriptors: Affective Behavior, Online Courses, Educational Technology, Technology Uses in Education
Direct linkStanden, Penelope J.; Brown, David J.; Taheri, Mohammad; Galvez Trigo, Maria J.; Boulton, Helen; Burton, Andrew; Hallewell, Madeline J.; Lathe, James G.; Shopland, Nicholas; Blanco Gonzalez, Maria A.; Kwiatkowska, Gosia M.; Milli, Elena; Cobello, Stefano; Mazzucato, Annaleda; Traversi, Marco; Hortal, Enrique – British Journal of Educational Technology, 2020
Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student's affective state, to move them out of negative states that inhibit learning, into…
Descriptors: Artificial Intelligence, Technology Uses in Education, Educational Technology, Identification
Azevedo, Roger; Mudrick, Nicholas; Taub, Michelle; Wortha, Franz – Teachers College Record, 2017
Metacognition and emotions play a critical role in learners' ability to monitor and regulate their learning about 21st-century skills related to science, technology, engineering, and mathematics (STEM) content while using advanced learning technologies (ALTs; e.g., intelligent tutoring systems, serious games, hypermedia, augmented reality). In…
Descriptors: Metacognition, Psychological Patterns, STEM Education, Educational Technology
Lin, Hao-Chiang Koong; Wang, Cheng-Hung; Chao, Ching-Ju; Chien, Ming-Kuan – Turkish Online Journal of Educational Technology - TOJET, 2012
Emotional expression in Artificial Intelligence has gained lots of attention in recent years, people applied its affective computing not only in enhancing and realizing the interaction between computers and human, it also makes computer more humane. In this study, emotional expressions were applied into intelligent tutoring system, where learners'…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Artificial Intelligence, Focus Groups
Moridis, Christos N.; Economides, Anastasios A. – Computers & Education, 2009
Building computerized mechanisms that will accurately, immediately and continually recognize a learner's affective state and activate an appropriate response based on integrated pedagogical models is becoming one of the main aims of artificial intelligence in education. The goal of this paper is to demonstrate how the various kinds of evidence…
Descriptors: Prediction, Artificial Intelligence, Inferences, Psychological Patterns
Ma, Zongmin – Information Science Publishing, 2006
Collecting and presenting the latest research and development results from the leading researchers in the field of e-learning systems, Web-Based Intelligent E-Learning Systems: Technologies and Applications provides a single record of current research and practical applications in Web-based intelligent e-learning systems. This book includes major…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Web Based Instruction, Distance Education
