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Search-as-learning research has emphasized the need to better support searchers when learning about complex topics online. Prior work in the learning sciences has shown that effective self-regulated learning (SRL), in which goals are a central function, is critical to improving learning outcomes. This dissertation investigates the influence of subgoals on learning during search. Two conditions were investigated: \textsc{Subgoals} and \textsc{NoSubgoals}. In the \textsc{Subgoals} condition, a tool called the Subgoal Manager was used to help searchers to develop specific subgoals associated with an overall learning-oriented search task. The influence of subgoals is explored along four dimensions: (1) learning outcomes; (2) searcher perceptions; (3) search behaviors; and (4) SRL processes. Learning outcomes were measured with two assessments, an established multiple-choice conceptual knowledge test and an open-ended summary of learning. Learning assessments were administered immediately after search and one week after search to capture learning retention. A qualitative analysis was conducted to identify the percentage of true statements on open-ended learning assessments. A think-aloud protocol was used to capture SRL processes. A second qualitative analysis was conducted to categorize SRL processes from think-aloud comments and behaviors during the search session. Findings from the dissertation suggest that subgoals improved learning during search. Additionally, it seems that subgoals helped participants to better retain what was learned one week later. Findings also suggest that SRL processes of participants in the \textsc{Subgoals} condition were more frequent and more diverse. SRL processes that were explicitly supported by the Subgoal Manager seemed to be more frequent in the \textsc{Subgoals} condition as well as SRL processes that were not explicitly supported. 

Researchers in the learning sciences have demonstrated the benefits of effective self-regulated learning (SRL) in improving learning outcomes. The search-as-learning community aims to improve learning outcomes during search, but offers limited research exploring the impact of SRL on learning during search. Current limited research in search-as-learning explores only \textit{perceptions} of SRL processes \textit{after} the search process~\cite{crescenzi_supporting_2021}. Results from such analyses are limited in that SRL is a dynamic, active process and participant perceptions of SRL can be unreliable~\cite{winne_exploring_2002, greene_domain-specificity_2015}. In this paper, we propose the implementation of an SRL coding framework to capture SRL processes as they unfold throughout a search session. Additionally, we offer several implications for future work using the proposed methodology.