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A Data Repository for the EDM Community: The PSLC DataShop
- K. Koedinger, R. Baker, Kyle Cunningham, A. Skogsholm, B. Leber, John C. Stamper
- 25 October 2010
In recent years, educational data mining has emerged as a burgeoning new area for scientific investigation because of the increasing availability of fine-grained, extensive, and longitudinal data on student learning.
Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data
The feasibility of this approach to automatically generate hints for an intelligent tutor that learns is demonstrated by extracting MDPs from four semesters of student solutions in a logic proof tutor, and the probability that they will be able to generate hints at any point in a given problem is calculated.
New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization
- K. Koedinger, Emma Brunskill, R. Baker, Elizabeth A. McLaughlin, John C. Stamper
- Computer ScienceAI Mag.
- 15 September 2013
Examples of use of a variety of techniques to develop or optimize the select, evaluate, suggest, and update functions of intelligent tutors, including probabilistic grammar learning, rule induction, Markov decision process, classification, and integrations of symbolic search and statistical inference are provided.
Experimental Evaluation of Automatic Hint Generation for a Logic Tutor
- John C. Stamper, Michael Eagle, T. Barnes, M. Croy
- Computer ScienceInt. J. Artif. Intell. Educ.
- 28 June 2011
This work augmented the Deep Thought logic tutor with a Hint Factory that generates data-driven, context-specific hints for an existing computer aided instructional tool, and shows that hints help students persist in a deductive logic proofs tutor.
The Rise of the Super Experiment
- John C. Stamper, D. Lomas, Dixie Ching, Steven Ritter, K. Koedinger, Jonathan E. Steinhart
- Physics, Computer ScienceEDM
The Super Experiment Framework is introduced, which describes how internet-scale experiments can inform and be informed by classroom and lab experiments, and is applied to a research project implementing learning games for mathematics that is collecting hundreds of thousands of data trials weekly.
Human-Machine Student Model Discovery and Improvement Using DataShop
It is shown how data visualization and modeling tools can be used with human input to improve student models and how such student model changes can be use to modify a tutoring system not only in terms of the usual student model effects on the tutor's problem selection, but also in driving the creation of new problems and hint messages.
Automated Student Model Improvement
This work presents a technique for automated improvement of student models that leverages the DataShop repository, crowd sourcing, and a version of the Learning Factors Analysis algorithm to discover improved models based on better test-set prediction in cross validation.
Towards improving programming habits to create better computer science course outcomes
It is found that students who start earlier tend to earn better scores, which is consistent with the findings of other researchers, and how students use release tokens is evaluated, a novel mechanism that provides feedback to students without giving away the code for the test cases used for grading.
Using Data-Driven Discovery of Better Student Models to Improve Student Learning
- K. Koedinger, John C. Stamper, Elizabeth A. McLaughlin, Tristan Nixon
- Computer Science, EducationAIED
- 9 July 2013
It is demonstrated that a tutor unit, redesigned based on data-driven cognitive model improvements, helped students reach mastery more efficiently and produced better learning on the problem-decomposition planning skills that were the focus of the Cognitive model improvements.
Automatic Hint Generation for Logic Proof Tutoring Using Historical Data
The feasibility of a novel application of Markov decision processes to automatically generate hints for an intelligent tutor that learns is demonstrated by extracting MDPs from four semesters of student solutions in a logic proof tutor, and calculating the probability that they will be able to generated hints for students at any point in a given problem.