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An ideal scenario for educational research is to perform an experiment, report and publish results, make the results and data available for verification, and finally allow the data to be used in follow up experiments or for secondary analyses. Unfortunately, this scenario often fails after the results are published. Researchers move on to new data and the old data may linger on a legacy server for...
We show how data visualization and modeling tools can be used with human input to improve student models. We present strategies for discovering potential flaws in existing student models and use them to identify improvements in a Geometry model. A key discovery was that the student model should distinguish problem steps requiring problem decomposition planning and execution from problem steps requiring...
The Pittsburgh Science of Learning Center’s DataShop is an open data repository and set of associated visualization and analysis tools. DataShop has data from thousands of students deriving from interactions with on-line course materials and intelligent tutoring systems. The data is fine-grained, with student actions recorded roughly every 20 seconds, and it is longitudinal, spanning semester or yearlong...
In our prior work we showed it was feasible to augment a logic tutor with a data-driven Hint Factory that uses data to automatically generate context-specific hints for an existing computer aided instructional tool. Here we investigate the impact of automatically generated hints on educational outcomes in a robust experiment that shows that hints help students persist in deductive logic courses. Three...
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