Predicting Knowledge Over Time

Predictive models of student knowledge help us create adaptive educational technology that personalizes learning material based on what a student knows. Many models do not account for forgetting as a factor when predicting student knowledge. This project explored how cognitive models of memory combined with big data techniques may provide a means for more accurate predictions of knowledge over time. In turn this aids the development of automated practice schedules in educational software.


The interactive doc linked below is big so give it some time to load. Note that I prioritized testing the limits of what I could do with R Markdown more than I prioritized a beautiful document! That might help explain some pages that look like I tried to include everything and the kitchen sink. 


In the summer of 2015 I worked as a graduate student intern at Carnegie Mellon University with Dr. Noboru Matsuda on the SimStudent project. My goal within the project was to identify ways to characterize students' sub-optimal learning behaviors by leveraging the scientific literature as well as exploratory data analysis to translate student log data and effectively operationalize behaviors of interest for detection by a search algorithm. 

Furthermore, I developed algorithms in R for detecting these sub-optimal behaviors. The end goal is to use such algorithms to detect student behavior in real-time so as to provide online suggestions that guide students towards behaviors that improve learning outcomes. 

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Independent Study & Applied

Machine Learning - Spring & Summer 2017


Tools and Languages:

R, RMarkdown, Shiny, Java, XML, HTML, WEKA

Tools and Languages:

R, Excel