We were lucky to have Daniel Furr join us this summer for the Grockit Graduate Research Internship program. Dan is a Ph.D. student in the University of California Berkeley’s Graduate School of Education, with a focus on Quantitative Methods and Evaluation.

At Grockit, Dan has been working on a variety of assessment-oriented projects, such as: on understanding how much data is needed to make use of an item response theory model in a Grockit network, on longitudinal models that help us refine our understanding of student improvement over time, and on extending our current models to incorporate the effect of collaboration, discussion, and repetition on a student’s probability of response accuracy. Understanding the effect of factors like these allows us to better select appropriate challenges for students in Grockit games.

About this internship, Dan writes:

Over the summer I focused on comparing approaches to estimating IRT models and experimenting with longitudinal models to measure user improvement over time. I compared estimations conducted with different R packages, with varying subsets of data, and ways of incorporating information on social interactions that occur alongside item responses. I used random item models to assess learning over time–time as calendar time, as item presentation order, and as discrete “sittings”. The people of Grockit are welcoming and innovative, and I feel fortunate to have had the chance to work with them and grapple with many interesting complexities.

I enjoyed working with Dan over the past few months, and wish him well on his return to academia in the Fall. Keep an eye on this blog for announcements of new assessment-oriented features on Grockit, several of which have benefited from Dan’s hard work this summer.