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

计算机科学 任务(项目管理) 主动学习(机器学习) 数据科学 人工智能 工程类 系统工程
作者
Kelsey Urgo,Jaime Arguello
出处
期刊:Information Processing and Management [Elsevier]
卷期号:59 (2): 102821-102821 被引量:14
标识
DOI:10.1016/j.ipm.2021.102821
摘要

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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