A data mining approach for adding adaptive interventions to exploratory learning environments
department or school: Science, Faculty of
department or school: Computer Science, Department of
data provider: DSpace
degree campus: UBCV
creator: Kardan, Samad
issued: 2017-07-04T16:52:30Z
issued: 2017
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: 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…
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/62133
note: A DATA MINING APPROACH FOR ADDING ADAPTIVE INTERVENTIONS TO EXPLORATORY LEARNING ENVIRONMENTS by Samad Kardan B.Sc., Computer Engineering, Ferdowsi University of Mashhad, Iran, 2006 M.Sc., Information Technology, Amirkabir University of Technology, Iran, 2009 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Computer Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2017 © Samad Kardan, 2017 ii Abstract 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: ï· It contributes to the Educational Data Mining (EDM) research, by devising, modifying, and test…
has type: Thesis/Dissertation
date issued: 2017-09
is shown at: 10.14288/1.0348694
language: eng
degree discipline: Computer Science
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/62133