Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 6 |
| Since 2006 (last 20 years) | 7 |
Descriptor
| Data Analysis | 7 |
| Markov Processes | 7 |
| Student Behavior | 7 |
| Intelligent Tutoring Systems | 5 |
| Models | 5 |
| Problem Solving | 4 |
| Reinforcement | 4 |
| Artificial Intelligence | 3 |
| Automation | 3 |
| College Students | 3 |
| Cooperation | 3 |
| More ▼ | |
Author
| Feng, Mingyu, Ed. | 2 |
| Andrade, Alejandro | 1 |
| Barnes, Tiffany | 1 |
| Barnes, Tiffany, Ed. | 1 |
| Chi, Min | 1 |
| Chi, Min, Ed. | 1 |
| Danish, Joshua A. | 1 |
| Delandshere, Ginette | 1 |
| Desmarais, Michel, Ed. | 1 |
| Galyardt, April | 1 |
| Goldin, Ilya | 1 |
| More ▼ | |
Publication Type
| Reports - Research | 4 |
| Collected Works - Proceedings | 3 |
| Journal Articles | 3 |
| Speeches/Meeting Papers | 1 |
Education Level
| Elementary Education | 3 |
| Higher Education | 3 |
| Postsecondary Education | 3 |
| Secondary Education | 3 |
| High Schools | 2 |
| Junior High Schools | 2 |
| Middle Schools | 2 |
| Early Childhood Education | 1 |
| Grade 1 | 1 |
| Grade 2 | 1 |
| Grade 8 | 1 |
| More ▼ | |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Download full textShen, Shitian; Mostafavi, Behrooz; Barnes, Tiffany; Chi, Min – Journal of Educational Data Mining, 2018
An important goal in the design and development of Intelligent Tutoring Systems (ITSs) is to have a system that adaptively reacts to students' behavior in the short term and effectively improves their learning performance in the long term. Inducing effective pedagogical strategies that accomplish this goal is an essential challenge. To address…
Descriptors: Teaching Methods, Markov Processes, Decision Making, Rewards
Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus – International Educational Data Mining Society, 2016
The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally…
Descriptors: Student Behavior, Data Analysis, Behavior Patterns, Multivariate Analysis
Andrade, Alejandro; Delandshere, Ginette; Danish, Joshua A. – Journal of Learning Analytics, 2016
One of the challenges many learning scientists face is the laborious task of coding large amounts of video data and consistently identifying social actions, which is time consuming and difficult to accomplish in a systematic and consistent manner. It is easier to catalog observable behaviours (e.g., body motions or gaze) without explicitly…
Descriptors: Student Behavior, Data Analysis, Models, Video Technology
Galyardt, April; Goldin, Ilya – Journal of Educational Data Mining, 2015
In educational technology and learning sciences, there are multiple uses for a predictive model of whether a student will perform a task correctly or not. For example, an intelligent tutoring system may use such a model to estimate whether or not a student has mastered a skill. We analyze the significance of data recency in making such…
Descriptors: Achievement Rating, Performance Based Assessment, Bayesian Statistics, Data Analysis
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
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
Lynch, Collin F., Ed.; Merceron, Agathe, Ed.; Desmarais, Michel, Ed.; Nkambou, Roger, Ed. – International Educational Data Mining Society, 2019
The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…
Descriptors: Data Collection, Data Analysis, Information Retrieval, Content Analysis
