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Download full textAghababyan, Ani; Martin, Taylor; Janisiewicz, Philip; Close, Kevin – Journal of Learning Analytics, 2016
Learning analytics is an emerging discipline and, as such, benefits from new tools and methodological approaches. This work reviews and summarizes our workshop on microgenetic data analysis techniques using R, held at the second annual Learning Analytics Summer Institute in Cambridge, Massachusetts, on 30 June 2014. Specifically, this paper…
Descriptors: Educational Research, Data Collection, Data Analysis, Workshops
Direct linkStoddard, Jeremy; Banks, Angela M.; Nemacheck, Christine; Wenska, Elizabeth – Democracy & Education, 2016
Video games are the most recent technological advancement to be viewed as an educational panacea and a force for democracy. However, this medium has particular affordances and constraints as a tool for democratic education in educational environments. This paper presents results from a study of the design and content of four iCivics games and…
Descriptors: Video Games, Technology Uses in Education, Educational Technology, Democratic Values
Paquette, Luc; Rowe, Jonathan; Baker, Ryan; Mott, Bradford; Lester, James; DeFalco, Jeanine; Brawner, Keith; Sottilare, Robert; Georgoulas, Vasiliki – International Educational Data Mining Society, 2016
Computational models that automatically detect learners' affective states are powerful tools for investigating the interplay of affect and learning. Over the past decade, affect detectors--which recognize learners' affective states at run-time using behavior logs and sensor data--have advanced substantially across a range of K-12 and postsecondary…
Descriptors: Models, Affective Behavior, Intelligent Tutoring Systems, Games
Kai, Shiming; Paquette, Luc; Baker, Ryan S.; Bosch, Nigel; D'Mello, Sidney; Ocumpaugh, Jaclyn; Shute, Valerie; Ventura, Matthew – International Educational Data Mining Society, 2015
Increased attention to the relationships between affect and learning has led to the development of machine-learned models that are able to identify students' affective states in computerized learning environments. Data for these affect detectors have been collected from multiple modalities including physical sensors, dialogue logs, and logs of…
Descriptors: Video Technology, Interaction, Physics, Affective Behavior
Pardos, Zachary A.; Baker, Ryan S. J. D.; San Pedro, Maria O. C. Z.; Gowda, Sujith M.; Gowda, Supreeth M. – Journal of Learning Analytics, 2014
In this paper, we investigate the correspondence between student affect and behavioural engagement in a web-based tutoring platform throughout the school year and learning outcomes at the end of the year on a high-stakes mathematics exam in a manner that is both longitudinal and fine-grained. Affect and behaviour detectors are used to estimate…
Descriptors: Affective Behavior, Student Behavior, Learner Engagement, Web Based Instruction
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
