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Please use this identifier to cite or link to this item: http://hdl.handle.net/10791/233

Title: A causal model of writing competence
Authors: Clemens, Clayton
Supervisor(s): Kumar, Vive (Faculty of Science, School of Computing and Information Systems)
Examining Committee: Kinshuk (University of North Texas, College of Information)
Chang, Maiga (Faculty of Science, School of Computing and Information Systems)
Degree: Master of Science, Information Systems (MScIS)
Department: Faculty of Science and Technology
Keywords: Writing
Causal inference
Issue Date: 14-Jun-2017
Abstract: Traditionally, assessing competence in English composition involves ignoring most of the steps in the writing process and focusing only on the final submission. The writing process cannot be assessed sufficiently in a traditional setting, and no formal structures exist for improving process-based writing competences. In online distributed learning environments, students’ steps to complete a composition can be recorded at granular levels using content capturing methods. For each step in the writing process, numeric qualities of the text can be determined using natural language processing. These metrics combine into measures of writing competence. The volume of generated data is cumbersome to submit through distributed environments, and time-consuming to process. This research overcomes the difficulty by simulating writing traces from a corpus of completed essays. A writing analytics engine processes the traces, and the resultant metrics are examined using causal inference, producing multiple statistical models of writing competence as systems of interventions.
Graduation Date: Jun-2018
URI: http://hdl.handle.net/10791/233
Appears in Collections:Theses & Dissertations

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