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A data mining approach for adding adaptive interventions to exploratory learning environments

University of British Columbia

Due to the open-ended nature of the interaction with Exploratory Learning Environments (ELEs), it is not trivial to add mechanisms for providing adaptive support to users. Our goal is to devise and evaluate a data mining approach for providing adaptive interventions that help users to achieve better task performance during interaction with ELEs. The general idea of this thesis is as follows: In an exploratory and open-ended environment, we collect interaction data of users while they are working with the system, and then find representative patterns of behavior for different user groups that achieved various levels of task performance. We use these patterns to provide adaptive real-time interventions designed to suggest or enforce the effective interaction behaviors while discouraging or preventing the ineffective ones. We test and confirm the hypothesis that as a result of these interventions, the average learning performance of the new users who work with the adaptive version of this ELE is significantly higher than the non-adaptive version. We use an interactive simulation for learning Constraint Satisfaction Problems (CSP), the AIspace CSP applet, as the test-bed for our research and propose a framework which covers the entire process described above, called the User Modeling and Adaptation (UMA) framework. The contributions of this thesis are two-fold: i) It contributes to the Educational Data Mining (EDM) research, by devising, modifying, and testing different techniques and mechanisms for a complete data mining based approach to delivering adaptive interventions in ELEs summarized in the UMA framework. The UMA framework consists of 3 phases: Behavior Discovery, User Classification, and Adaptive Support. We assessed each of the above phases in a series of user studies. This work is the first to fully evaluate and provide positive evidence for the use of a data mining approach for deriving and delivering adaptive interventions in ELEs with the goal of improving the user’s performance. ii) It also contributes to the user modeling and user-adapted interaction community by providing new evidence for the usefulness of the eye-gaze data for the purpose of predicting learning performance of users while interacting with an ELE.

Text

2017-07-04

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/62133

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/