UBC Theses and Dissertations
Understanding self-efficacy in search as self-determined learning Cole, Amelia W.
Working learners regularly access information with web search engines to enhance their skills and find information. Search as learning (SAL) is a research agenda that rethinks search from a learning perspective. This dissertation applies a new self-determined learning paradigm (a learner-centric approach focusing on agency) to SAL and unpacks the role of self-efficacy when user experience professionals learn using search engines in natural settings. Understanding self-efficacy began with the rigorous development of a SAL-specific measure of self-efficacy, followed by a mixed-method study culminating in data-prompted interviews. I found a statistically significant decline in one cognitive process, schema training, after 5 days of learning using search. Schema training refers to the known methods for finding information online; results show that some participants had an incomplete mental model of web search systems. As a result, searchers may be rewarded with feelings of gratification after their search while reinforcing poor search habits over time. The data-prompted interviews were analysed using reflexive thematic analysis. I found that although self-efficacy improved for some participants, many participants experienced a decline in self-efficacious processes over the course of a week. Participants who experienced a decline reported feelings of failure, difficulty assessing the credibility of resources, tendency to take sources at face value, and a lack of psychologically safe sensemaking opportunities within their social network. Participants who experienced an increase in self-efficacious processes reported having naturally occurring mentorships, well-defined distal goals, and a healthy amount of skepticism of information found online. This dissertation contributes a partially validated self-efficacy scale for use in SAL contexts with working learners, demonstrates how to improve ecological validity of SAL studies by combining in-situ data collection through experience sampling with follow-up interviews (i.e., data-prompted interviews), and contributes to the discussion of the design of search-centric learning systems. The implications of this research emphasise the importance of the broader learning ecology—inclusive of people, learning tasks, and systems—when discussing the design of search systems for learning purposes.
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