Searching to Learn with Instructional Scaffolding

Searching to Learn with Instructional Scaffolding

Câmara, Arthur (TU Delft Web Information Systems)
Roy, N. (TU Delft Web Information Systems)
Maxwell, D.M. (TU Delft Web Information Systems)
Hauff, C. (TU Delft Web Information Systems)

2021

Web search engines are today considered to be the primary tool to assist and empower learners in finding information relevant to their learning goals- be it learning something new, improving their existing skills, or just fulfilling a curiosity. While several approaches for improving search engines for the learning scenario have been proposed (e.g. a specific ranking function), instructional scaffolding (or simply scaffolding)-a traditional learning support strategy-has not been studied in the context of search as learning, despite being shown to be effective for improving learning in both digital and traditional learning contexts. When scaffolding is employed, instructors provide learners with support throughout their autonomous learning process. We hypothesize that the usageof scaffolding techniques within a search system can be an effective way to help learners achieve their learning objectives whilst searching. As such, this paper investigates the incorporation of scaffolding into a search system employing three different strategies (as well as a control condition): (i) AQe, the automatic expansion of user queries with relevant subtopics; (ii) CURATEDsc, the presenting of a manually curated static list of relevant subtopics on the search engine result page; and (iii) FEEDBACKsc, which projects real-time feedback about a user's exploration of the topic space on top of the CURATEDsc visualization. To investigate the effectiveness of these approaches withrespect to human learning, we conduct a user study (N=126) where participants were tasked with searching and learning about topics such as genetically modified organisms. We find that (i) the introduction of the proposed scaffolding methods in the proposed topics does not significantly improve learning gains. However, (ii) it does significantly impact search behavior. Furthermore, (iii) immediate feedback of the participants' learning (FEEDBACKsc) leads to undesirable user behavior, with participants seemingly focusing on the feedback gauges instead of learning.

query expansion
search as learning
search interface
task oriented search
user interface

http://resolver.tudelft.nl/uuid:5f2025f5-ca07-4453-8fa3-f255dce7c75f

https://doi.org/10.1145/3406522.3446012

Association for Computing Machinery (ACM), New York

978-1-4503-8055-3

CHIIR 2021: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval

CHIIR 2021, 2021-03-14 → 2021-03-19, Online event due to COVID-19 , Virtual, Australia

Institutional Repository

conference paper

© 2021 Arthur Câmara, N. Roy, D.M. Maxwell, C. Hauff