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Using data mining to dynamically build up just in time learner models

dc.contributor.advisorMcCalla, Gordonen_US
dc.contributor.committeeMemberVassileva, Julitaen_US
dc.contributor.committeeMemberLudwig, Simoneen_US
dc.contributor.committeeMemberMorrison, Dirken_US
dc.creatorLiu, Wengangen_US
dc.date.accessioned2010-01-27T21:56:39Zen_US
dc.date.accessioned2013-01-04T04:24:45Z
dc.date.available2011-02-09T08:00:00Zen_US
dc.date.available2013-01-04T04:24:45Z
dc.date.created2009-12en_US
dc.date.issued2009-12en_US
dc.date.submittedDecember 2009en_US
dc.description.abstractUsing rich data collected from e-learning systems, it may be possible to build up just in time dynamic learner models to analyze learners' behaviours and to evaluate learners' performance in online education systems. The goal is to create metrics to measure learners' characteristics from usage data. To achieve this goal we need to use data mining methods, especially clustering algorithms, to find patterns from which metrics can be derived from usage data. In this thesis, we propose a six layer model (raw data layer, fact data layer, data mining layer, measurement layer, metric layer and pedagogical application layer) to create a just in time learner model which draws inferences from usage data. In this approach, we collect raw data from online systems, filter fact data from raw data, and then use clustering mining methods to create measurements and metrics. In a pilot study, we used usage data collected from the iHelp system to create measurements and metrics to observe learners' behaviours in a real online system. The measurements and metrics relate to a learner's sociability, activity levels, learning styles, and knowledge levels. To validate the approach we designed two experiments to compare the metrics and measurements extracted from the iHelp system: expert evaluations and learner self evaluations. Even though the experiments did not produce statistically significant results, this approach shows promise to describe learners' behaviours through dynamically generated measurements and metric. Continued research on these kinds of methodologies is promising.en_US
dc.identifier.urihttp://hdl.handle.net/10388/etd-01272010-215639en_US
dc.language.isoen_USen_US
dc.subjectEducational Data Miningen_US
dc.subjectLearner Modelen_US
dc.titleUsing data mining to dynamically build up just in time learner modelsen_US
dc.type.genreThesisen_US
dc.type.materialtexten_US
thesis.degree.departmentComputer Scienceen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorUniversity of Saskatchewanen_US
thesis.degree.levelMastersen_US
thesis.degree.nameMaster of Science (M.Sc.)en_US

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