@inproceedings{cabf9d25adb944f394b04aa799d495b2,
title = "Learning, moment-by-moment and over the long term",
abstract = "The development of moment-by-moment learning graphs (MBMLGs), which plot predictions about the probability that a student learned a skill at a specific time, has already helped to improve our understanding of how student performance during the learning process relates to robust learning [1]. In this study, we extend this work to study year-end learning outcomes and to account for differences in learning on original questions and within knowledge-construction scaffolds. We discuss which quantitative features of moment-by-moment learning in these two contexts are predictive of the longerterm outcomes, and conclude with potential implications for instruction.",
keywords = "Educational data mining, Intelligent tutoring system, Moment-by-moment learning, Scaffolding",
author = "Yang Jiang and Baker, {Ryan S.} and Luc Paquette and Pedro, {Maria San} and Heffernan, {Neil T.}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 17th International Conference on Artificial Intelligence in Education, AIED 2015 ; Conference date: 22-06-2015 Through 26-06-2015",
year = "2015",
doi = "10.1007/978-3-319-19773-9_84",
language = "English (US)",
isbn = "9783319197722",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "654--657",
editor = "Cristina Conati and Neil Heffernan and Antonija Mitrovic and {Felisa Verdejo}, M.",
booktitle = "Artificial Intelligence in Education - 17th International Conference, AIED 2015, Proceedings",
address = "Germany",
}