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Measuring and understanding self-handicapping in education

Resource type
Thesis type
(Thesis) Ph.D.
Date created
2020-09-10
Authors/Contributors
Abstract
Self-handicapping is intentionally fabricating obstacles to performance. It is very prevalent in education where it interferes with learning and lowers academic achievement. Few self-handicapping experiments have approximated authentic learning situations, elevating concerns about ecological validity and generalizability. This study addressed several methodological concerns by (a) posing a task common in education, and (b) offering participants multiple occasions to choose among several productive, neutral, or self-handicapping approaches to learning. Undergraduate learners were randomly assigned to receive contingent or non-contingent success feedback on three learning tasks. Each task offered multiple occasions to claim or practise self-handicapping by making selections within a component of the software. Those selections caused changes in the learning environment while participants worked on tasks and generated data about self-handicapping more realistically situated and in finer grain than data gathered in prior research. Results indicate this method for unobtrusively recording data about self-handicapping validly represented the construct. Learners' choices reflected preferences for certain handicaps and described patterns of hidden versus blatant self-handicapping. Evidence for self-handicapping and self-regulated learning across tasks was found. Some learners repeatedly self-handicapped, Others self-regulated learning over time by demonstrating metacognitive awareness, monitoring, and control of learning activities regardless of feedback provided. Encouraging metacognition may aid self-handicappers to more productively self-regulate their learning over time.
Document
Identifier
etd21085
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Winne, Philip H.
Language
English
Member of collection
Download file Size
input_data\20984\etd21085.pdf 1.46 MB

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