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Download full textZhou, Yiqiu; Kang, Jina – International Educational Data Mining Society, 2022
The complex and dynamic nature of collaboration makes it challenging to find indicators of productive learning and quality collaboration. This exploratory study developed a collaboration metric to capture temporal patterns of joint attention (JA) based on log files generated as students interacted with an immersive astronomy simulation using…
Descriptors: Astronomy, Problem Solving, Science Instruction, Cooperative Learning
Hao, Jiangang; Chen, Lei; Flor, Michael; Liu, Lei; von Davier, Alina A. – ETS Research Report Series, 2017
Conversations in collaborative problem-solving activities can be used to probe the collaboration skills of the team members. Annotating the conversations into different collaboration skills by human raters is laborious and time consuming. In this report, we report our work on developing an automated annotation system, CPS-rater, for conversational…
Descriptors: Problem Solving, Cooperative Learning, Teamwork, Documentation
Direct linkZiaeehezarjeribi, Yadi – ProQuest LLC, 2010
This study, based on experiential play methodology was used to explore student engagement while playing "Medal of Honor (2002)" and "Call of Duty (2003)". It identifies some of the key issues related to the use of video games and simulations during the training phase of game play. Research into the effects of gaming in education has been extremely…
Descriptors: Learner Engagement, Play, Video Games, Learning Strategies
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
Peer reviewedKearsley, Greg – Machine-Mediated Learning, 1985
Describes three principal types of embedded training: help systems, simulations, and intelligent tutors. Implications of designing and implementing embedded training are discussed in terms of a set of propositions about the use of computers for learning. (Author/MBR)
Descriptors: Artificial Intelligence, Classification, Computer Assisted Instruction, Computer Simulation
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction
Biswas, Gautam; And Others – 1994
This report characterizes human problem solving in digital circuit design. Protocols of 11 different designers with varying degrees of training were analyzed by identifying the designers' problem solving strategies and discussing activity patterns that differentiate the designers. These methods are proposed as a tentative basis for assessing…
Descriptors: Classification, Cognitive Processes, Computer Simulation, Computer Software Development
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers
