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A New Web-Based Personalized Learning System Improves Student Learning Outcomes
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Sancenon, Vicente; Wijaya, Kharisma; Wen, Xavier Yue Shu; Utama, Diaz Adi; Ashworth, Mark; Ng, Kelvin Hongrui; Cheong, Alicia; Neo, Zhizhong – International Journal of Virtual and Personal Learning Environments, 2022
Although there is increasing acceptance that personalization improves learning outcomes, there is still limited experimental evidence supporting this claim. The aim of this study was to implement and evaluate the effectiveness of an adaptive recommendation system for Singapore primary and secondary education. The system leverages users trace data…
Descriptors: Academic Achievement, Electronic Learning, Learning Analytics, Learning Processes
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