Publication Date
| In 2015 | 0 |
| Since 2014 | 0 |
| Since 2011 (last 5 years) | 6 |
| Since 2006 (last 10 years) | 6 |
| Since 1996 (last 20 years) | 6 |
Descriptor
| Pretests Posttests | 6 |
| Intelligent Tutoring Systems | 4 |
| Data | 3 |
| Scores | 3 |
| Academic Achievement | 2 |
| College Students | 2 |
| Comparative Analysis | 2 |
| Data Analysis | 2 |
| Learning | 2 |
| Mathematics | 2 |
| More ▼ | |
Source
| International Educational… | 6 |
Author
| Pardos, Zachary A. | 2 |
| Rau, Martina A. | 2 |
| Azevedo, Roger | 1 |
| Biswas, Gautam | 1 |
| Bouchet, Francois | 1 |
| Brunskill, Emma | 1 |
| Butler, Heather | 1 |
| Cai, Zhiqiang | 1 |
| Dolan, Robert P. | 1 |
| Forsyth, Carol | 1 |
| More ▼ | |
Publication Type
| Speeches/Meeting Papers | 6 |
| Reports - Evaluative | 3 |
| Reports - Research | 2 |
| Reports - Descriptive | 1 |
Education Level
| Higher Education | 2 |
| Elementary Education | 1 |
| Grade 4 | 1 |
| Grade 5 | 1 |
| Grade 6 | 1 |
| Grade 7 | 1 |
| Middle Schools | 1 |
| Postsecondary Education | 1 |
Audience
Showing all 6 results
Download full textBouchet, Francois; Azevedo, Roger; Kinnebrew, John S.; Biswas, Gautam – International Educational Data Mining Society, 2012
Identification of student learning behaviors, especially those that characterize or distinguish students, can yield important insights for the design of adaptation and feedback mechanisms in Intelligent Tutoring Systems (ITS). In this paper, we analyze trace data to identify distinguishing patterns of behavior in a study of 51 college students…
Descriptors: Tutoring, Feedback (Response), Intelligent Tutoring Systems, Academic Achievement
Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T. – International Educational Data Mining Society, 2012
Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…
Descriptors: Homogeneous Grouping, Prediction, Tutors, Cluster Grouping
Rau, Martina A.; Scheines, Richard – International Educational Data Mining Society, 2012
Although learning from multiple representations has been shown to be effective in a variety of domains, little is known about the mechanisms by which it occurs. We analyzed log data on error-rate, hint-use, and time-spent obtained from two experiments with a Cognitive Tutor for fractions. The goal of the experiments was to compare learning from…
Descriptors: Experiments, Mathematics, Comparative Analysis, Outcomes of Education
Interleaved Practice with Multiple Representations: Analyses with Knowledge Tracing Based Techniques
Rau, Martina A.; Pardos, Zachary A. – International Educational Data Mining Society, 2012
The goal of this paper is to use Knowledge Tracing to augment the results obtained from an experiment that investigated the effects of practice schedules using an intelligent tutoring system for fractions. Specifically, this experiment compared different practice schedules of multiple representations of fractions: representations were presented to…
Descriptors: Intelligent Tutoring Systems, Mathematics, Knowledge Level, Scheduling
Yudelson, Michael V.; Brunskill, Emma – International Educational Data Mining Society, 2012
In this paper we combine a logistic regression student model with an exercise selection procedure. As opposed to the body of prior work on strategies for selecting practice opportunities, we are working on an assumption of a finite amount of opportunities to teach the student. Our goal is to prescribe activities that would maximize the amount…
Descriptors: Scores, Tests, Regression (Statistics), Pretests Posttests
Forsyth, Carol; Pavlik, Philip, Jr.; Graesser, Arthur C.; Cai, Zhiqiang; Germany, Mae-lynn; Millis, Keith; Dolan, Robert P.; Butler, Heather; Halpern, Diane – International Educational Data Mining Society, 2012
"OperationARIES!" is an Intelligent Tutoring System that teaches scientific inquiry skills in a game-like atmosphere. Students complete three different training modules, each with natural language conversations, in order to acquire deep-level knowledge of 21 core concepts of research methodology (e.g., correlation does not mean causation). The…
Descriptors: Learning, Cognitive Processes, Logical Thinking, Scientific Methodology
