Measuring self-regulation in a computer-based open online inquiry learning environment using Google
Item
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Title
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Measuring self-regulation in a computer-based open online inquiry learning environment using Google
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Identifier
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d_2009_2013:0e9acf9255da:10893
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identifier
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10971
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Creator
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Winkler, Christoph,
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Contributor
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Barry J. Zimmerman
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Date
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2011
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Language
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English
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Publisher
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City University of New York.
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Subject
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Educational psychology | Educational technology | Calibration | Computer-based learning environments | Measurement | Microanalysis | Online inquiry | Self-regulation
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Abstract
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This dissertation investigated the impact of expert-modeling of a self-regulatory strategy in an open online inquiry learning environment (Google) on forty (n = 40) community college students' performance on an online inquiry task, the role of key self-regulatory measures, and calibration (accuracy of performance judgment) during the online inquiry phases searching/evaluating, and synthesizing. Theory-driven data for the study was gathered by employing both microanalytic and trace data methods. The results generally supported the hypotheses and showed that expert-modeling of a self-regulatory strategy helped students to improve performance during the online inquiry phases searching/evaluating and synthesis. In addition, the study showed that the key self-regulatory measures planning, self-efficacy, and attribution were predictive of students' writing scores. A comparison of mean-bias scores as a measure of calibration universally showed that student overestimated their performance at key points during their online inquiry (presentation of task, search completion, essay completion). Self-regulatory strategy instruction, however, helped students in the Expert-Modeling group to significantly better calibrate their performance after the presentation of the task and completion of the essay. The study further showed that students in the Expert-Modeling group entered qualitatively higher search terms and selected qualitatively higher websites for their inquiry. An exploratory analysis of the students' search patterns (self-efficacy and self-evaluation for websites selected for inquiry) showed lower mean variances for students who received the self-regulatory strategy training during phases searching/evaluating, which is indicative of a more consistent and successful search pattern during their online inquiry.
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Type
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dissertation
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Source
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2009_2013.csv
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degree
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Ph.D.
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Program
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Educational Psychology
