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.

As you probably know, Grockit offers learners the choice of three different modes of study: individual practice, peer study groups, and instructor-led lessons. What you might not know (unless you’ve read about Research at Grockit) is that these three modes of learning draws on ongoing research in three corresponding subfields of study. Last week, I got up-to-speed on the state of the art in one of these areas, individual study, at the Tenth International Conference on Intelligent Tutoring Systems. I wanted to share a few highlights from the conference here.

On Day 1, I started by co-organizing a workshop with Erin Walker and Carolyn Penstein Rosé (both of Carnegie Mellon University) on Opportunities for intelligent and adaptive behavior in collaborative learning systems. It was a great opportunity to bring together a group of people actively pursuing research at the intersection of intelligent systems and collaborative learning. Here is a snippet from the workshop website.

Intelligent tutoring systems are generally designed to tailor instruction to the individual student, but this does not mean that ITS-guided learning must necessarily be a solitary activity. A variety of recent systems have demonstrated ways in which an adaptive learning environment can incorporate and benefit from the presence of multiple learners. Similarly, students using computer-supported collaborative learning systems have been shown to benefit from the introduction of adaptive support that targets the collaboration. In this workshop, we invite discussion and seek to explore ways in which the combination of collaborative and intelligent aspects of a system can benefit the learner by creating a more productive learning environment.

If you’re interested in learning more about this workshop, you can find the presenter list, rapid-fire slides, and full proceedings on the workshop website.

As the week continued, I got a chance to hear presentations on a wide variety of research projects. A few of the talks that I found interesting this year touched on these topics: deciding if and when to provide hints, identifying the moment of learning from patterns in data, incorporating teachers into the process of designing a system, automatically generating hints from patterns in past data, understanding the ways in which human tutors are adaptive, incorporating dialogue agents in peer collaborations, and modeling learning gains over time. You can see the full spectrum of work presented in the conference proceedings index.

Grockit was proud to be one of the sponsors of this year’s Intelligent Tutoring Systems conference.