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Direct linkYongtian Cheng; K. V. Petrides – Educational and Psychological Measurement, 2025
Psychologists are emphasizing the importance of predictive conclusions. Machine learning methods, such as supervised neural networks, have been used in psychological studies as they naturally fit prediction tasks. However, we are concerned about whether neural networks fitted with random datasets (i.e., datasets where there is no relationship…
Descriptors: Psychological Studies, Artificial Intelligence, Cognitive Processes, Predictive Validity
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
Utkarsh Upadhyay; Graham Lancashire; Christoph Moser; Manuel Gomez-Rodriguez – npj Science of Learning, 2021
We perform a large-scale randomized controlled trial to evaluate the potential of machine learning-based instruction sequencing to improve memorization while allowing the learners the freedom to choose their review times. After controlling for the length and frequency of study, we find that learners for whom a machine learning algorithm determines…
Descriptors: Randomized Controlled Trials, Teaching Methods, Memorization, Algorithms
Download full textNdubuisi Friday Ugwu; Raphael Ezamenyi Ochiaka; Ugochukwu Simeon Asogwa; Adewumi Segun Igbinlade; Kamorudeen Taiwo Sanni; Toyin Segun Onayinka; Obinna Iroegbu; Michael Olayinka Irewole; Jacob Kehinde Opele; Abiodun Oyetunde Oloyede; Ndidi Christiana Ibenyenwa; Oladipo Adeyeye Olubodun – Higher Learning Research Communications, 2025
Objective: Our study aimed to compare the efficacy of artificial intelligence (AI)-based immersive training with human-led workshops to improve the English language skills of non-English early career researchers (NEECRs) in a Nigerian public university. Methods: Our study employed a randomized pretest/posttest control group design. A total of 124…
Descriptors: Artificial Intelligence, Technology Uses in Education, Workshops, Researchers
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
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
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
