Elizabeth A McLaughlin
Elizabeth A McLaughlin
Verified email at cs.cmu.edu
Cited by
Cited by
Learning is not a spectator sport: Doing is better than watching for learning from a MOOC
KR Koedinger, J Kim, JZ Jia, EA McLaughlin, NL Bier
Proceedings of the second (2015) ACM conference on learning@ scale, 111-120, 2015
New potentials for data-driven intelligent tutoring system development and optimization
KR Koedinger, E Brunskill, RSJ Baker, EA McLaughlin, J Stamper
AI Magazine 34 (3), 27-41, 2013
Instruction based on adaptive learning technologies
V Aleven, EA McLaughlin, RA Glenn, KR Koedinger
Handbook of research on learning and instruction 2, 522-560, 2016
A quasi-experimental evaluation of an on-line formative assessment and tutoring system
KR Koedinger, EA McLaughlin, NT Heffernan
Journal of Educational Computing Research 43 (4), 489-510, 2010
Data mining and education
KR Koedinger, S D'Mello, EA McLaughlin, ZA Pardos, CP Rose
Wiley Interdisciplinary Reviews: Cognitive Science 6 (4), 333-353, 2015
Automated Student Model Improvement.
KR Koedinger, EA McLaughlin, JC Stamper
International Educational Data Mining Society, 2012
Using data-driven discovery of better student models to improve student learning
KR Koedinger, JC Stamper, EA McLaughlin, T Nixon
International conference on artificial intelligence in education, 421-430, 2013
Seeing language learning inside the math: Cognitive analysis yields transfer
K Koedinger, E McLaughlin
Proceedings of the annual meeting of the cognitive science society 32 (32), 2010
Explanatory learner models: Why machine learning (alone) is not the answer
CP Rosé, EA McLaughlin, R Liu, KR Koedinger
British Journal of Educational Technology 50 (6), 2943-2958, 2019
Is the doer effect a causal relationship? How can we tell and why it's important
KR Koedinger, EA McLaughlin, JZ Jia, NL Bier
Proceedings of the sixth international conference on learning analytics …, 2016
Interpreting model discovery and testing generalization to a new dataset
R Liu, EA McLaughlin, KR Koedinger
Educational Data Mining 2014, 2014
A comparison of model selection metrics in datashop
J Stamper, K Koedinger, E Mclaughlin
Educational Data Mining 2013, 2013
Closing the Loop with Quantitative Cognitive Task Analysis.
KR Koedinger, EA McLaughlin
International Educational Data Mining Society, 2016
Data-driven Learner Modeling to Understand and Improve Online Learning: MOOCs and technology to advance learning and learning research (Ubiquity symposium)
KR Koedinger, EA McLaughlin, JC Stamper
Ubiquity 2014 (May), 1-13, 2014
MOOCs and technology to advance learning and learning research: Data-driven learner modeling to understand and improve online learning
KR Koedinger, EA McLaughlin, JC Stamper
Ubiquity 3, 1-13, 2014
A general multi-method approach to design-loop adaptivity in intelligent tutoring systems
Y Huang, V Aleven, E McLaughlin, K Koedinger
International Conference on Artificial Intelligence in Education, 124-129, 2020
Methods for Evaluating Simulated Learners: Examples from SimStudent.
KR Koedinger, N Matsuda, CJ MacLellan, EA McLaughlin
AIED Workshops, 2015
Is there an explicit learning bias? Students beliefs, behaviors and learning outcomes.
PF Carvalho, EA McLaughlin, K Koedinger
CogSci, 2017
A general multi-method approach to data-driven redesign of tutoring systems
Y Huang, NG Lobczowski, JE Richey, EA McLaughlin, MW Asher, ...
LAK21: 11th International Learning Analytics and Knowledge Conference, 161-172, 2021
The knowledge-learning-instruction (KLI) dependency: how the domain-specific and domain-general interact in STEM learning
KR Koedinger, EA McLaughlin
Washington University Libraries, 2014
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