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Hai Li; Wanli Xing; Chenglu Li; Wangda Zhu; Hyunju Oh – British Journal of Educational Technology, 2025
Mathematical stories can enhance students' motivation and interest in learning mathematics, thereby positively impacting their academic performance. However, due to resource constraints faced by the creators, generative artificial intelligence (GAI) is employed to create mathematical stories accompanied by images. This study introduces a method…
Descriptors: Mathematics Education, Story Telling, Readability, Artificial Intelligence
Diego G. Campos; Tim Fütterer; Thomas Gfrörer; Rosa Lavelle-Hill; Kou Murayama; Lars König; Martin Hecht; Steffen Zitzmann; Ronny Scherer – Educational Psychology Review, 2024
Systematic reviews and meta-analyses are crucial for advancing research, yet they are time-consuming and resource-demanding. Although machine learning and natural language processing algorithms may reduce this time and these resources, their performance has not been tested in education and educational psychology, and there is a lack of clear…
Descriptors: Artificial Intelligence, Algorithms, Computer System Design, Natural Language Processing
Qi Zhou; Wannapon Suraworachet; Mutlu Cukurova – Education and Information Technologies, 2024
Collaboration is argued to be an important skill, not only in schools and higher education contexts but also in the workspace and other aspects of life. However, simply asking students to work together as a group on a task does not guarantee success in collaboration. Effective collaborative learning requires meaningful interactions among…
Descriptors: Learning Analytics, Cooperative Learning, Nonverbal Communication, Speech Communication
Bryan R. Drost; Char Shryock – Phi Delta Kappan, 2025
Creating assessment questions aligned to standards is a time-consuming task for teachers, but large language models such as ChatGPT can help. Bryan Drost & Char Shryock describe a three-step process for using ChatGPT to create assessments: 1) Ask ChatGPT to break standards into measurable targets. 2) Determine how much time to spend on each…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, Teaching Methods
Trakunphutthirak, Ruangsak; Lee, Vincent C. S. – Journal of Educational Computing Research, 2022
Educators in higher education institutes often use statistical results obtained from their online Learning Management System (LMS) dataset, which has limitations, to evaluate student academic performance. This study differs from the current body of literature by including an additional dataset that advances the knowledge about factors affecting…
Descriptors: Information Retrieval, Pattern Recognition, Data Analysis, Information Technology
Moreno-Estevaa, Enrique Garcia; White, Sonia L. J.; Wood, Joanne M.; Black, Alex A. – Frontline Learning Research, 2018
In this research, we aimed to investigate the visual-cognitive behaviours of a sample of 106 children in Year 3 (8.8 ± 0.3 years) while completing a mathematics bar-graph task. Eye movements were recorded while children completed the task and the patterns of eye movements were explored using machine learning approaches. Two different techniques of…
Descriptors: Artificial Intelligence, Man Machine Systems, Mathematics Education, Eye Movements
Bozak, Ali; Aybek, Eren Can – International Journal of Contemporary Educational Research, 2020
The present study aims to determine which analysis technique-Artificial Neural Networks (ANNs) or Logistic Regression (LR) Analysis-is better at predicting the science literacy success of the 15-year Turkish students who participated in PISA research carried out in 2015 by using learning time spent on science, test anxiety, environmental…
Descriptors: Artificial Intelligence, Networks, Regression (Statistics), Achievement Tests
Vu, Phu; Fredrickson, Scott; Meyer, Richard – Online Journal of Distance Learning Administration, 2016
With a dearth of research on human-robot interaction in education and relatively high non-completion rates of online students, this study was conducted to determine the feasibility of using a virtual assistant (VA) to respond to questions and concerns of students and provide 24/7 online course content support. During a 16 week-long academic…
Descriptors: Electronic Learning, Robotics, Online Courses, Feasibility Studies
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction