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Trade-offs in data representations for learner models in interactive simulations

University of British Columbia

Interactive simulations can foster student driven, exploratory learning. However, students may not always learn effectively in these unstructured environments. Due to this, it would be advantageous to provide adaptive support to those that are not effectively using the learning environment. To achieve this, it is helpful to build a user-model that can estimate the learner’s trajectories and need for help during interaction. However, this is challenging because it is hard to know a priori which behaviors are conducive to learning. It is particularly challenging in complex Exploratory Learning Environments (like in PhET’s DC Circuit Construction Kit which is used in this work) because of the large variety of ways to interact. To address this problem, we evaluate multiple representations of student interactions with the simulation that capture different amounts of granularity and feature engineering. We then apply the student modeling framework proposed in [1] to mine the student behaviors and classify learners. Our results indicate that the proposed framework is able to extend to a more complex environment in that we are able to successfully classify students and identify behaviors intuitively associated with high and low learning. We also discuss the trade-offs between the differing levels of granularity and feature engineering in the tested interaction representations in terms of their ability to evaluate learning and inform feedback. [1] Samad Kardan and Cristina Conati. 2011. A Framework for Capturing Distinguishing User Interaction Behaviours in Novel Interfaces. Proceedings of the 4th International Conference on Educational Data Mining, 159–168.

Text

2015-10-24

Attribution-NonCommercial-NoDerivs 2.5 Canada

http://hdl.handle.net/2429/55058

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/2.5/ca/