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Mbouzao, Boniface; Desmarais, Michel C.; Shrier, Ian – International Educational Data Mining Society, 2020
Massive online Open Courses (MOOCs) make extensive use of videos. Students interact with them by pausing, seeking forward or backward, replaying segments, etc. We can reasonably assume that students have different patterns of video interactions, but it remains hard to compare student video interactions. Some methods were developed, such as Markov…
Descriptors: Comparative Analysis, Video Technology, Interaction, Measurement Techniques
Halpern, David; Tubridy, Shannon; Wang, Hong Yu; Gasser, Camille; Popp, Pamela Osborn; Davachi, Lila; Gureckis, Todd M. – International Educational Data Mining Society, 2018
Knowledge tracing is a popular and successful approach to modeling student learning. In this paper we investigate whether the addition of neuroimaging observations to a knowledge tracing model enables accurate prediction of memory performance in held-out data. We propose a Hidden Markov Model of memory acquisition related to Bayesian Knowledge…
Descriptors: Learning Processes, Memory, Prediction, Second Language Learning
Rafferty, Anna N.; LaMar, Michelle M.; Griffiths, Thomas L. – Cognitive Science, 2015
Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we…
Descriptors: Inferences, Knowledge Level, Educational Games, Computer Simulation
Ezen-Can, Aysu; Boyer, Kristy Elizabeth – Journal of Educational Data Mining, 2015
Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language…
Descriptors: Classification, Dialogs (Language), Computational Linguistics, Information Retrieval