Search as Learning

Abstract

Search systems are often designed to support simple look-up tasks, such as fact-finding and navigation tasks. However, people increasingly use search engines to complete tasks that require deeper learning. In recent years, the search as learning (SAL) research community has argued that search systems should also be designed to support information-seeking tasks that involve complex learning as an important outcome. This monograph aims to provide a comprehensive review of prior research in search as learning and related areas.

Searching to learn can be characterized by specific learning objectives, strategies, and context. Therefore, we begin by reviewing research in education that has aimed at characterizing learning objectives, strategies, and context. Then, we review methods used in prior studies to measure learning during a search session. Here, we discuss two important recommendations for future work: (1) measuring learning retention and (2) measuring a learner's ability to transfer their new knowledge to a novel scenario. Following this, we discuss studies that have focused on understanding factors that influence learning during search and search behaviors that are predictive of learning. Next, we survey tools that have been developed to support learning during search. Searching for the purpose of learning is often a solitary activity. Research in self-regulated learning (SRL) aims to understand how people monitor and control their own learning. Therefore, we review existing models of SRL, methods to measure engagement with specific SRL processes, and tools to support effective SRL. We conclude by discussing potential areas for future research.