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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. 

Search systems are often used to support learning-oriented goals. This trend has given rise to the “searchas- learning” movement, which proposes that search systems should be designed to support learning. To this end, an important research question is: How does a searcher’s type of learning objective (LO) influence their trajectory (or pathway) toward that objective? We report on a lab study (N = 36) in which participants gathered information to meet a specific type of LO. To characterize LOs and pathways, we leveraged Anderson and Krathwohl’s (A&K’s) taxonomy [3]. A&K’s taxonomy situates LOs at the intersection of two orthogonal dimensions: (1) cognitive process (CP) (remember, understand, apply, analyze, evaluate, and create) and (2) knowledge type (factual, conceptual, procedural, and metacognitive knowledge). Participants completed learning-oriented search tasks that varied along three CPs (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). A pathway is defined as a sequence of learning instances (e.g., subgoals) that were also each classified into cells from A&K’s taxonomy. Our study used a think-aloud protocol, and pathways were generated through a qualitative analysis of participants’ thinkaloud comments and recorded screen activities. We investigate three research questions. First, in RQ1, we study the impact of the LO on pathway characteristics (e.g., pathway length). Second, in RQ2, we study the impact of the LO on the types of A&K cells traversed along the pathway. Third, in RQ3, we study common and uncommon transitions between A&K cells along pathways conditioned on the knowledge type of the objective. We discuss implications of our results for designing search systems to support learning. 

People often search for information in order to learn something new. In recent years, the “search-as-learning” movement has argued that search systems should be better designed to support learning. Current search systems (especially Web search engines) are largely designed and optimized to fulfill simple look-up tasks (e.g., navigational or fact-finding search tasks). However, they provide less support for searchers working on complex tasks that involve learning. Search-as-learning studies have investigated a wide range of research questions. For example, studies have aimed to better understand how characteristics of the individual searcher, the type of search task, and interactive features provided by the system can influence learning outcomes. Learning assessment is a key component in search-as-learning studies. Assessment materials are used to both gauge prior knowledge and measure learning during or after one or more search sessions. In this paper, we provide a systematic review of different types of assessments used in search-as-learning studies to date. The paper makes the following three contributions. First, we review different types of assessments used and discuss their potential benefits and drawbacks. Second, we review assessments used outside of search-as-learning, which may provide insights and opportunities for future research. Third, we provide recommendations for future research. Importantly, we argue that future studies should clearly define learning objectives and develop assessment materials that reliably capture the intended type of learning. For example, assessment materials should test a participant’s ability to engage with specific cognitive processes, which may range from simple (e.g., memorization) to more complex (e.g., critical and creative thinking). Additionally, we argue that future studies should consider two dimensions that are understudied in search-as-learning: long-term retention (i.e., being able to use what was learned in the long term) and transfer of learning (i.e., being able to use what was learned in a novel context). 

An important question in interactive information retrieval (IIR) is: How do task characteristics influence users’ needs? In this paper, we investigate the effects of cognitive task complexity on the types of information considered useful for a task. We characterize information types from two perspectives. From one perspective, we classify task-related information items based on inherent characteristics (referred to as info-types): factual statements, concepts/definitions, opinionated statements, and insights—tips/advice related to the task domain. From a second perspective, we used Byström and Järvelin’s framework [5] to define information types based on how the information might be used to complete the task (referred to as functional roles): (1) to help the task doer understand the task requirements (problem information); (2) to help the task doer strategize on how to approach the task (problem-solving information); and (3) to help the task doer learn about the task domain (domain information). Our results suggest that: (1) cognitive task complexity influences the functional roles of information items deemed useful for the task (RQ1); (2) certain info-types are more (or less) likely to play certain functional roles (RQ2); and task complexity influences the variety of functional roles played by info-types (RQ3). 

A search trail is an interactive visualization of how a previous searcher approached a related task. Using search trails to assist users requires understanding aspects of the task, user, and trails. In this paper, we examine two questions. First, what are task characteristics that influence a user's ability to gain benefits from others' trails? Second, what is the impact of a "mismatch" between a current user's task and previous user's task which originated the trail? We report on a study that investigated the influence of two factors on participants' perceptions and behaviors while using search trails to complete tasks. Our first factor, task scope, focused on the scope of the task assigned to the participant (broad to narrow). Our manipulation of this factor involved varying the number of constraints associated with tasks. Our second factor, trail scope, focused on the scope of the task that originated the search trails given to participants. We investigated how task scope and trail scope affected participants' (RQ1) pre-task perceptions, (RQ2) post-task perceptions, and (RQ3) search behaviors. We discuss implications of our results for systems that use search trails to provide assistance. 

In recent years, the “search as learning” community has argued that search systems should be designed to support learning. We report on a lab study in which we manipulated the learning objectives associated with search tasks assigned to participants. We manipulated learning objectives by leveraging Anderson and Krathwohl’s taxonomy of learning (A&K’s taxonomy) [2], which situates learning objectives at the intersection of two orthogonal dimensions: the cognitive process and the knowledge type dimension. Participants in our study completed tasks with learning objectives that varied across three cognitive processes (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). We focus on the effects of the task’s cognitive process and knowledge type on participants’ pre-/post-task perceptions and search behaviors. Our results found that the three knowledge types considered in our study had a greater effect than the three cognitive processes. Specifically, conceptual knowledge tasks were perceived to be more difficult and required more search activity. We discuss implications for designing search systems that support learning.