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Towards the Automatic Risk of Bias Assessment on Randomized Controlled Trials: A Comparison of RobotReviewer and Humans
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Yuan Tian; Xi Yang; Suhail A. Doi; Luis Furuya-Kanamori; Lifeng Lin; Joey S. W. Kwong; Chang Xu – Research Synthesis Methods, 2024
RobotReviewer is a tool for automatically assessing the risk of bias in randomized controlled trials, but there is limited evidence of its reliability. We evaluated the agreement between RobotReviewer and humans regarding the risk of bias assessment based on 1955 randomized controlled trials. The risk of bias in these trials was assessed via two…
Descriptors: Risk, Randomized Controlled Trials, Classification, Robotics
Using Machine Learning Techniques to Improving Average Treatment Effect Estimates in Small-Scale Randomized Controlled Trials. Working Paper. WR-A1004-1
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Opper, Isaac M. – RAND Corporation, 2020
Researchers often include covariates when they analyze the results of randomized controlled trials (RCTs), valuing the increased precision of the estimates over the potential of inducing small-sample bias when doing so. In this paper, we develop a sufficient condition which ensures that the inclusion of covariates does not induce small-sample bias…
Descriptors: Artificial Intelligence, Man Machine Systems, Educational Technology, Technology Uses in Education
The Future of Adaptive Learning: Does the Crowd Hold the Key?
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Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay – International Journal of Artificial Intelligence in Education, 2016
Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students--explanations, feedback, and other pedagogical interactions. Considering the…
Descriptors: Artificial Intelligence, Educational Technology, Student Needs, Electronic Publishing
The Future of Adaptive Learning: Does the Crowd Hold the Key?
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Heffernan, Neil T.; Ostrow, Korinn S.; Kelly, Kim; Selent, Douglas; Van Inwegen, Eric G.; Xiong, Xiaolu; Williams, Joseph Jay – Grantee Submission, 2016
Due to substantial scientific and practical progress, learning technologies can effectively adapt to the characteristics and needs of students. This article considers how learning technologies can adapt over time by crowdsourcing contributions from teachers and students -- explanations, feedback, and other pedagogical interactions. Considering the…
Descriptors: Artificial Intelligence, Educational Technology, Student Needs, Electronic Publishing