As learning spaces become increasingly augmented with technology, we are seeing a surge in the availability of real-time data about learning activities. Using data mining, real-time analytics, and intelligent software agents, I have designed tools that help teachers to understand the state of the class at the individual, small group, and whole class levels; to orchestrate the flow of activities; and to know where and when they are needed at critical points in students’ learning.
Using a blend of learning analytics and interaction analysis, I have been able to accurately predict participants’ productive and unproductive states when tinkering in Oztoc an immersive interactive museum exhibit. These findings also resulted in the development of a real-time tablet application to help museum explainers know when they are needed during visitors’ explorations with the exhibit.
With a focus on tablet and mobile applications, I have spent the last nine years pushing the boundaries of data mining and analytics for real-time teacher supports across formal and informal environments.
Tissenbaum, M., Kumar, V., & Berland, M. (2016, June). Modeling Visitor Behavior in a Game-Based Engineering Museum Exhibit with Hidden Markov Models. In Proceedings of the 9th International Conference on Educational Data Mining (pp. 517-522). ACM.
Tissenbaum & Slotta (2015), Scripting and orchestration of learning across contexts: A role for intelligent agents and data mining. In Milrad, Wong & Specht (eds.) Seamless Learning in the Age of Connectivity. Springer.
Tissenbaum, M., & Slotta, J. (2016, June). Supporting real-time teacher orchestration in a smart classroom setting. In Real-Time Visualization of Student Activities to Support Classroom Orchestration. Symposium conducted at the 12th International Conference of the Learning Sciences (pp. 1043-1044), Singapore.
Slotta, J. D., Tissenbaum, M., & Lui, M. (2013, April). Orchestrating of complex inquiry: three roles for learning analytics in a smart classroom infrastructure. In Proceedings of the Third International Conference on Learning Analytics and Knowledge (pp. 270-274). ACM.