Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 5 |
Descriptor
Data Processing | 5 |
Intelligent Tutoring Systems | 4 |
Models | 3 |
Tutoring | 3 |
Artificial Intelligence | 2 |
Automation | 2 |
Chemistry | 2 |
Computer System Design | 2 |
Data Analysis | 2 |
Decision Making | 2 |
Educational Technology | 2 |
More ▼ |
Source
Educational Technology &… | 1 |
Grantee Submission | 1 |
International Journal of… | 1 |
International Working Group… | 1 |
Journal of Learning Analytics | 1 |
Author
Stamper, John | 5 |
Barnes, Tiffany | 2 |
Davenport, Jodi | 2 |
Liu, Ran | 2 |
Calders, Toon | 1 |
Conati, Cristina | 1 |
Croy, Marvin | 1 |
Pechenizkiy, Mykola | 1 |
Romero, Cristobal | 1 |
Ventura, Sebastian | 1 |
Publication Type
Journal Articles | 4 |
Reports - Research | 3 |
Collected Works - Proceedings | 1 |
Reports - Descriptive | 1 |
Audience
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Liu, Ran; Stamper, John; Davenport, Jodi – Journal of Learning Analytics, 2018
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Learning, Data Analysis, Student Development
Liu, Ran; Stamper, John; Davenport, Jodi – Grantee Submission, 2018
Temporal analyses are critical to understanding learning processes, yet understudied in education research. Data from different sources are often collected at different grain sizes, which are difficult to integrate. Making sense of data at many levels of analysis, including the most detailed levels, is highly time-consuming. In this paper, we…
Descriptors: Intelligent Tutoring Systems, Learning, Data Analysis, Student Development
Stamper, John; Barnes, Tiffany; Croy, Marvin – International Journal of Artificial Intelligence in Education, 2011
The Hint Factory is an implementation of our novel method to automatically generate hints using past student data for a logic tutor. One disadvantage of the Hint Factory is the time needed to gather enough data on new problems in order to provide hints. In this paper we describe the use of expert sample solutions to "seed" the hint generation…
Descriptors: Cues, Prompting, Learning Strategies, Teaching Methods
Barnes, Tiffany; Stamper, John – Educational Technology & Society, 2010
In building intelligent tutoring systems, it is critical to be able to understand and diagnose student responses in interactive problem solving. However, building this understanding into a computer-based intelligent tutor is a time-intensive process usually conducted by subject experts. Much of this time is spent in building production rules that…
Descriptors: Intelligent Tutoring Systems, Logical Thinking, Tutors, Probability
Pechenizkiy, Mykola; Calders, Toon; Conati, Cristina; Ventura, Sebastian; Romero, Cristobal; Stamper, John – International Working Group on Educational Data Mining, 2011
The 4th International Conference on Educational Data Mining (EDM 2011) brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large datasets to answer educational research questions. The conference, held in Eindhoven, The Netherlands, July 6-9, 2011, follows the three previous editions…
Descriptors: Academic Achievement, Logical Thinking, Profiles, Tutoring