The degree of knowledge gain metric: Evaluating how searchers acquire new information while searching online

This study presents the degree of knowledge gain, a behavioral metric that estimates how much searchers learn during Web searches. Grounded in information theory and Shannon entropy, the metric quantifies reductions in uncertainty by analyzing query reformulations, clicks, and document positions, avoiding reliance on pre/post-testing or self-reports. The metric was validated with a cohort of legal professionals who completed three domain-specific search tasks on a national legal platform. Behavioral coding of think-aloud sessions and statistical tests (analysis of variance; Spearman rank correlation) demonstrated strong positive correlations between the degree of knowledge gain and conventional transfer of learning scores, with structured tasks producing the highest gains. This approach offers an automated, scalable indicator of learning, enriching the Searching as Learning paradigm and enabling real-time adaptation in personalized search, intelligent tutoring, and conversational systems.

Authors

Institutions

Publication Details

Journal
Journal of Information Science
Published
2026-09-22
DOI
https://doi.org/10.1177/01655515261478899
Primary Topic
Information Retrieval and Search Behavior
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

The degree of knowledge gain metric: Evaluating how searchers acquire new information while searching online

Marcelo Tibau, Sean Wolfgand Matsui Siqueira, Bernardo Pereira Nunes, Pertti Vakkari
Journal of Information Science
Information Retrieval and Search Behavior
article

The degree of knowledge gain metric: Evaluating how searchers acquire new information while searching online

Marcelo Tibau, Sean Wolfgand Matsui Siqueira, Bernardo Pereira Nunes, Pertti Vakkari
article en

Abstract

This study presents the degree of knowledge gain, a behavioral metric that estimates how much searchers learn during Web searches. Grounded in information theory and Shannon entropy, the metric quantifies reductions in uncertainty by analyzing query reformulations, clicks, and document positions, avoiding reliance on pre/post-testing or self-reports. The metric was validated with a cohort of legal professionals who completed three domain-specific search tasks on a national legal platform. Behavioral coding of think-aloud sessions and statistical tests (analysis of variance; Spearman rank correlation) demonstrated strong positive correlations between the degree of knowledge gain and conventional transfer of learning scores, with structured tasks producing the highest gains. This approach offers an automated, scalable indicator of learning, enriching the Searching as Learning paradigm and enabling real-time adaptation in personalized search, intelligent tutoring, and conversational systems.

Journal of Information Science
Australian National University (AU), Tampere University (FI), Universidade Federal do Estado do Rio de Janeiro (BR)
Quality Education
Openalex Percentile: Top 4%
Information Retrieval and Search Behavior
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.