Developing Computational Methods to Measure and Track Learners’ Spatial Reasoning in an Open-Ended Simulation



Published Aug 12, 2015
Aditi Mallavarapu Leilah Lyons Tia Shelley Brian Slattery


Interactive learning environments can provide learners with opportunities to explore rich, real-world problem spaces, but the nature of these problem spaces can make assessing learner progress difficult. Such assessment can be useful for providing formative and summative feedback to the learners, to educators, and to the designers of the environments. This work represents part of a growing body of research that is applying EDM techniques to more open-ended problem spaces. 
The open-ended problem space under study here was an environmental science simulation. Learners were confronted with the real-world challenge of effectively placing green infrastructure in an urban neighborhood to reduce surface flooding. Learners could try out many different arrangements of green infrastructure and use the simulation to test each solution’s impact on flooding. The solutions proposed by the learners were logged during a series of experimental trials with different user interface designs for the simulation. As with many open-problem spaces, analyzing this data was difficult due to the large state space, many good solutions, and many alternate paths to those good solutions . 
This work proposes a procedure for reducing the state space while maintaining critical spatial properties of the solutions. Spatial reasoning problems are a class of problems new to EDM, so this work will set the stage for further research in this area. This work also demonstrates a procedure for discovering effective spatial strategies and solution paths, and demonstrates how this information can be used to give formative feedback to the designers of the interactive learning environment.

How to Cite

Mallavarapu, A., Lyons, L., Shelley, T., & Slattery, B. (2015). Developing Computational Methods to Measure and Track Learners’ Spatial Reasoning in an Open-Ended Simulation. JEDM | Journal of Educational Data Mining, 7(2), 49-82. Retrieved from
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EDM 2015 Journal Track