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1. Understanding the “Pathway” Towards a Searcher’s Learning Objective

   https://doi.org/https://doi.org/10.1145/3495222  

   Urgo, Kelsey; Arguello, Jaime (January 2022, ACM transactions on information systems)

   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. 

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2. Anderson and Krathwohl's Two-Dimensional Taxonomy Applied to Task Creation and Learning Assessment

   https://doi.org/10.1145/3341981.3344226  

   Urgo, K.; Arguello, J.; Capra, R. (October 2019, Proceedings of the 5th ACM SIGIR International Conference on the Theory of Information Retrieval)

   Search tasks play an important role in the study and development of interactive information retrieval (IIR) systems. Prior work has examined how search tasks vary along dimensions such as the task’s main activity, end goal, structure, and complexity. Recently, researchers have been exploring task complexity from the perspective of cognitive complexity—related to the types (and variety) of mental activities required by the task. Anderson & Krathwohl’s two-dimensional taxonomy of learning has been a commonly used framework for investigating tasks from the perspective of cognitive complexity [1]. A&K’s 2D taxonomy involves a cognitive process dimension and an orthogonal knowledge dimension. Prior IIR research has successfully leveraged the cognitive process dimension of this 2D taxonomy to develop search tasks and investigate their effects on searchers’ needs, perceptions, and behaviors. However, the knowledge dimension of the taxonomy has been largely ignored. In this conceptual paper, we argue that future IIR research should consider both dimensions of A&K’s taxonomy. Specifically, we discuss related work, present details on both dimensions of A&K’s taxonomy, and explain how to use the taxonomy to develop search tasks and learning assessment materials. Additionally, we discuss how considering both dimensions of A&K’s taxonomy has important implications for future IIR research. 

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3. Learning assessments in search-as-learning: A survey of prior work and opportunities for future research

   https://doi.org/https://doi.org/10.1016/j.ipm.2021.102821  

   Urgo, Kelsey; Arguello, Jaime (January 2022, Information processing and management)

   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). 

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4. Anderson and Krathwohl's Two-Dimensional Taxonomy Applied to Supporting and Predicting Learning During Search

   https://doi.org/10.1145/3343413.3377947  

   Urgo, Kelsey (March 2020, Proceedings of the 2020 Conference on Human Information Interaction and Retrieval)

   There is a growing body of research in the Search as Learning community that recognizes the need for users to learn during search, but modern search systems have yet to adapt to support this need. Our research proposes three research goals toward addressing the support of user learning during search. Research goal 1 (RG1) introduces a more precise and reliable metric of assessing user learning. Anderson & Krathwohl’s 2-dimensional taxonomy is used as a framework to develop learning objectives and assessment questions to measure user learning during search. Additionally, Anderson & Krathwohl’s taxonomy is used as a coding scheme to outline the pathways users traverse along the way to a particular learning objective. Research goal 2 (RG2) investigates the prediction of learning objectives using behavioral measures. Finally, research goal 3 (RG3) proposes a search system that presents information relevant to the user based on their current learning sub-goal and scaffolds information based on the pathways they are likely to traverse given a particular learning objective. 

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5. Immersion and Learning: Cognitive and Affective Outcomes of Immersive Virtual Reality Learning Experiences

   Queiroz, A. C. (May 2020, 70th International Communication Association Conference)

   This study aimed to compare the effects of immersive virtual reality (IVR) videos and 2D educational videos on cognitive (i.e. conceptual knowledge) and non-cognitive (i.e. self-efficacy) learning outcomes. Fifty-three students from an all-girls middle school learned about humans’ impact on the ocean through either IVR videos, using a virtual reality (VR) headset, or through 2D videos, using a computer monitor. Results replicate previous findings suggesting that conceptual knowledge gains between IVR and desktop learning experiences is not significant. Also, results show that participants who watched IVR videos reported higher self-efficacy scores and expressed higher feelings of presence than participants who watched the same videos using a computer monitor. Finally, further analyses revealed that the feeling of presence mediated both cognitive and non-cognitive learning outcomes. 

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