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

Kelsey Urgo, Jaime Arguello
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引用次数: 8

Abstract

Search systems are often used to support learning-oriented goals. This trend has given rise to the “search-as-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’ think-aloud 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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了解通往搜索者学习目标的“路径”
搜索系统通常用于支持面向学习的目标。这一趋势引发了“搜索即学习”运动,该运动提出搜索系统应该被设计为支持学习。为此,一个重要的研究问题是:搜索者的学习目标(LO)类型如何影响他们实现该目标的轨迹(或途径)?我们报告了一项实验室研究(N = 36),其中参与者收集信息以满足特定类型的LO。为了表征LOs和通路,我们利用了Anderson和Krathwohl (A&K)的分类法[3]。A&K的分类法将LOs置于两个正交维度的交叉点:(1)认知过程(CP)(记住、理解、应用、分析、评估和创造)和(2)知识类型(事实、概念、程序和元认知知识)。参与者完成了以学习为导向的搜索任务,这些任务沿着三个cp(应用、评估和创造)和三个知识类型(事实、概念和程序知识)变化。路径被定义为一系列学习实例(例如,子目标),这些学习实例也被A&K的分类法分类为细胞。我们的研究使用了有声思考协议,并通过对参与者的有声思考评论和记录的屏幕活动进行定性分析来生成路径。我们调查了三个研究问题。首先,在RQ1中,我们研究了LO对路径特性(如路径长度)的影响。其次,在RQ2中,我们研究了LO对沿着该通路穿越的A&K细胞类型的影响。第三,在RQ3中,我们研究了A&K细胞之间沿目标知识类型的通路的常见和不常见转换。我们讨论了我们的结果对设计支持学习的搜索系统的影响。
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