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Jionghao Lin; Wei Tan; Lan Du; Wray Buntine; David Lang; Dragan Gasevic; Guanliang Chen – IEEE Transactions on Learning Technologies, 2024
Automating the classification of instructional strategies from a large-scale online tutorial dialogue corpus is indispensable to the design of dialogue-based intelligent tutoring systems. Despite many existing studies employing supervised machine learning (ML) models to automate the classification process, they concluded that building a…
Descriptors: Classification, Dialogs (Language), Teaching Methods, Computer Assisted Instruction
Hoel, Tore; Chen, Weiqin; Pawlowski, Jan M. – Research and Practice in Technology Enhanced Learning, 2020
There is a gap between people's online sharing of personal data and their concerns about privacy. Till now, this gap is addressed by attempting to match individual privacy preferences with service providers' options for data handling. This approach has ignored the role different contexts play in data sharing. This paper aims at giving privacy…
Descriptors: Privacy, Engineering, Context Effect, Information Security
Shafee Mohammed – ProQuest LLC, 2020
Predicting learning and human behavior in general is a challenging endeavor. Machine learning driven predictive modeling have been an increasingly popular means to understand disparities in student performance. With more than a handful of approaches to predictive modeling, the current literature of predicting learning is plagued with issues such…
Descriptors: Prediction, Short Term Memory, Blended Learning, Student Behavior
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Clavié, Benjamin; Gal, Kobi – International Educational Data Mining Society, 2020
We introduce DeepPerfEmb, or DPE, a new deep-learning model that captures dense representations of students' online behaviour and meta-data about students and educational content. The model uses these representations to predict student performance. We evaluate DPE on standard datasets from the literature, showing superior performance to the…
Descriptors: Student Behavior, Electronic Learning, Metadata, Prediction
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Denby, Thomas; Schecter, Jeffrey; Arn, Sean; Dimov, Svetlin; Goldrick, Matthew – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2018
Phonotactics--constraints on the position and combination of speech sounds within syllables--are subject to statistical differences that gradiently affect speaker and listener behavior (e.g., Vitevitch & Luce, 1999). What statistical properties drive the acquisition of such constraints? Because they are naturally highly correlated, previous…
Descriptors: Phonology, Probability, Learning Processes, Syllables
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