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Zhao, Siyuan; Heffernan, Neil – International Educational Data Mining Society, 2017
Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student. Making the inference about causal effects of studies interventions is a central problem. In this paper we propose the Residual Counterfactual Networks (RCN) for answering counterfactual inference questions, such as…
Descriptors: Computation, Outcomes of Treatment, Networks, Randomized Controlled Trials
Wang, Yan; Ostrow, Korinn; Beck, Joseph; Heffernan, Neil – Grantee Submission, 2016
The focus of the learning analytics community bridges the gap between controlled educational research and data mining. Online learning platforms can be used to conduct randomized controlled trials to assist in the development of interventions that increase learning gains; datasets from such research can act as a treasure trove for inquisitive data…
Descriptors: Learning Analytics, Educational Research, Randomized Controlled Trials, Information Retrieval
Selent, Douglas; Patikorn, Thanaporn; Heffernan, Neil – Grantee Submission, 2016
In this paper, we present a dataset consisting of data generated from 22 previously and currently running randomized controlled experiments inside the ASSISTments online learning platform. This dataset provides data mining opportunities for researchers to analyze ASSISTments data in a convenient format across multiple experiments at the same time.…
Descriptors: Intelligent Tutoring Systems, Data, Randomized Controlled Trials, Electronic Learning
Ostrow, Korinn; Heffernan, Neil; Williams, Joseph Jay – Grantee Submission, 2017
Background/Context: Large-scale randomized controlled experiments conducted in authentic learning environments are commonly high stakes, carrying extensive costs and requiring lengthy commitments for all-or-nothing results amidst many potential obstacles. Educational technologies harbor an untapped potential to provide researchers with access to…
Descriptors: Educational Technology, Authentic Learning, Technology Uses in Education, Cooperation
Williams, Joseph Jay; Kim, Juho; Rafferty, Anna; Heffernan, Neil; Maldonado, Samuel; Gajos, Krzysztof Z.; Lasecki, Walter S.; Heffernan, Neil – Online Submission, 2016
While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement System), a system for obtaining explanations. AXIS asks learners to generate, revise, and evaluate explanations as they solve…
Descriptors: Computer Uses in Education, Artificial Intelligence, Problem Solving, Electronic Learning
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Feng, Mingyu; Roschelle, Jeremy; Heffernan, Neil; Fairman, Janet; Murphy, Robert – Grantee Submission, 2014
Much research has been done on the development of an intelligent tutoring system (ITS), and small empirical studies have demonstrated the effectiveness of ITS at promoting student learning. However, large-scale implementation of ITS in school settings has not been researched thoroughly. In this paper, we describe an ongoing randomized controlled…
Descriptors: Intelligent Tutoring Systems, Homework, Educational Technology, Technology Uses in Education