Publication Date
| In 2026 | 0 |
| Since 2025 | 1 |
| Since 2022 (last 5 years) | 2 |
| Since 2017 (last 10 years) | 8 |
| Since 2007 (last 20 years) | 9 |
Descriptor
Source
| International Educational… | 9 |
Author
| Barnes, Tiffany, Ed. | 2 |
| Feng, Mingyu, Ed. | 2 |
| Lester, James | 2 |
| Rowe, Jonathan | 2 |
| Azevedo, Roger | 1 |
| Barnes, Tiffany | 1 |
| Boyer, Kristy Elizabeth, Ed. | 1 |
| Can Sarpkaya | 1 |
| Chi, Min | 1 |
| Chi, Min, Ed. | 1 |
| Efe Bozkir | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 5 |
| Speeches/Meeting Papers | 5 |
| Collected Works - Proceedings | 4 |
Education Level
| Higher Education | 5 |
| Postsecondary Education | 5 |
| Secondary Education | 3 |
| Junior High Schools | 2 |
| Middle Schools | 2 |
| Adult Education | 1 |
| Early Childhood Education | 1 |
| High Schools | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textSüleyman Özdel; Can Sarpkaya; Efe Bozkir; Hong Gao; Enkelejda Kasneci – International Educational Data Mining Society, 2025
Transforming educational technologies through the integration of large language models (LLMs) and virtual reality (VR) offers the potential for immersive and interactive learning experiences. However, the effects of LLMs on user engagement and attention in educational environments remain open questions. In this study, we utilized a fully…
Descriptors: Technology Uses in Education, Educational Technology, Artificial Intelligence, Computer Simulation
Li, ZhaoBin; Yee, Luna; Sauerberg, Nathaniel; Sakson, Irene; Williams, Joseph Jay; Rafferty, Anna N. – International Educational Data Mining Society, 2020
Digital educational technologies offer the potential to customize students' experiences and learn what works for which students, enhancing the technology as more students interact with it. We consider whether and when attempting to discover how to personalize has a cost, such as if the adaptation to personal information can delay the adoption of…
Descriptors: Educational Technology, Technology Uses in Education, Student Needs, Student Characteristics
Ju, Song; Zhou, Guojing; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Identifying critical decisions is one of the most challenging decision-making problems in real-world applications. In this work, we propose a novel Reinforcement Learning (RL) based Long-Short Term Rewards (LSTR) framework for critical decisions identification. RL is a machine learning area concerning with inducing effective decision-making…
Descriptors: Decision Making, Reinforcement, Artificial Intelligence, Man Machine Systems
Henderson, Nathan; Min, Wookhee; Emerson, Andrew; Rowe, Jonathan; Lee, Seung; Minogue, James; Lester, James – International Educational Data Mining Society, 2021
Recent years have seen significant interest in multimodal frameworks for modeling learner engagement in educational settings. Multimodal frameworks hold particular promise for predicting visitor engagement in interactive science museum exhibits. Multimodal models often utilize video data to capture learner behavior, but video cameras are not…
Descriptors: Museums, Audiences, Participation, Exhibits
Sawyer, Robert; Rowe, Jonathan; Azevedo, Roger; Lester, James – International Educational Data Mining Society, 2018
Student interactions with game-based learning environments produce a wide range of in-game problem-solving sequences. These sequences can be viewed as trajectories through a game's problem-solving space. In this paper, we present a general framework for analyzing students' problem-solving behavior in game-based learning environments by filtering…
Descriptors: Educational Games, Teaching Methods, Educational Technology, Technology Uses in Education
Feng, Mingyu, Ed.; Käser, Tanja, Ed.; Talukdar, Partha, Ed. – International Educational Data Mining Society, 2023
The Indian Institute of Science is proud to host the fully in-person sixteenth iteration of the International Conference on Educational Data Mining (EDM) during July 11-14, 2023. EDM is the annual flagship conference of the International Educational Data Mining Society. The theme of this year's conference is "Educational data mining for…
Descriptors: Information Retrieval, Data Analysis, Computer Assisted Testing, Cheating
Boyer, Kristy Elizabeth, Ed.; Yudelson, Michael, Ed. – International Educational Data Mining Society, 2018
The 11th International Conference on Educational Data Mining (EDM 2018) is held under the auspices of the International Educational Data Mining Society at the Templeton Landing in Buffalo, New York. This year's EDM conference was highly competitive, with 145 long and short paper submissions. Of these, 23 were accepted as full papers and 37…
Descriptors: Data Collection, Data Analysis, Computer Science Education, Program Proposals
Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
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
