An empirical analysis of search engines’ response to web search queries associated with the classroom setting
Published in Aslib Journal of Information Management, 2020
Recommended citation: Anuyah, O., Milton, A., Green, M., & Pera, M. S. (2020). " An empirical analysis of search engines’ response to web search queries associated with the classroom setting " Aslib Journal of Information Management. 72(1). http://ashleemilton.github.io/files/empiricalAslib2020.pdf
Purpose The purpose of this paper is to examine strengths and limitations that search engines (SEs) exhibit when responding to web search queries associated with the grade school curriculum
Design/methodology/approach The authors employed a simulation-based experimental approach to conduct an in-depth empirical examination of SEs and used web search queries that capture information needs in different search scenarios.
Findings Outcomes from this study highlight that child-oriented SEs are more effective than traditional ones when filtering inappropriate resources, but often fail to retrieve educational materials. All SEs examined offered resources at reading levels higher than that of the target audience and often prioritized resources with popular top-level domain (e.g. “.com”).
Practical implications Findings have implications for human intervention, search literacy in schools, and the enhancement of existing SEs. Results shed light on the impact on children’s education that result from introducing misconception about SEs when these tools either retrieve no results or offer irrelevant resources, in response to web search queries pertinent to the grade school curriculum.
Originality/value The authors examined child-oriented and popular SEs retrieval of resources aligning with task objectives and user capabilities–resources that match user reading skills, do not contain hate-speech and sexually-explicit content, are non-opinionated, and are curriculum-relevant. Findings identified limitations of existing SEs (both directly or indirectly supporting young users) and demonstrate the need to improve SE filtering and ranking algorithms.