Unpacking 'the Next Black Box': Investigating the Cognitive and Affective Underpinnings of Student Self-Assessment
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Authors
Rickey, Nathan
Date
Type
thesis
Language
eng
Keyword
classroom assessment , student self-assessment , self-regulated learning , trace data , web analytics , K-12 education , case study , assessment as learning , cognition and affect
Alternative Title
Abstract
Despite theoretical and empirical arguments for its essential role in learning, and its consequent centrality in classroom assessment frameworks and policy globally, little is known about student self-assessment’s internal cognitive and affective processes. Left without a theory to inform teachers in supporting productive student self-assessment (SSA), many students will never learn to be sources of their own feedback, the foundation for independent, lifelong learning. This research responds to calls to examine how students in K-12 contexts think and feel while engaged in SSA tasks that are supported by literature – the next black box of classroom assessment research. First, I synthesized relevant self-assessment literature within a highly granular self-regulated learning (SRL) model. Drawing on this theoretical foundation, I employed a collective case study using digital trace data to infer the ways in which a class of Year 12 students (n=16) in a UK secondary school thought and felt during an evidence informed self-assessment activity. Matomo, a web analytics platform, collected session recording and heatmap data which elucidated participants’ cognitive and affective operations as they completed a writing task, self-assessed their work using exemplars and rubrics, and revised their writing accordingly. I analyzed log files of trace data to a) infer which SRL subprocesses participants activate, b) generate self-assessment process graphs and profiles for each participant, and c) investigate how participants engaged in each process based on the content of their work. Findings highlight the recursive and weakly sequenced nature of SSA processes, as well key cognitive and affective trends. Moreover, SSA profiles for each participant demonstrate how trace data and learning analytics can support advances in student learning through SSA. Forming the basis for an initial theory of SSA cognition and affect, this research advances SSA theory, a core component of classroom assessment.
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Copying and Preserving Your Thesis
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ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.