Modeling Knowledge Change Behaviors in Learning-related Tasks

Chang Liu, Xiaoxuan Song, Hanrui Liu, Nicholas J. Belkin

Research output: Contribution to journalConference articlepeer-review


In Search as Learning (SAL) research, when and how learning occurs during the search process has been a focus that attracts research attention. The goal of this study is to explore and characterize searchers' knowledge change patterns in the context of learning-related tasks from a process perspective. A user experiment was conducted, and participants were asked to search for two learning-related search tasks in a laboratory environment, and draw mind maps before and during search to keep a record of what they know about the task. Searchers' knowledge change behaviors during the search process were extracted from their mind maps and analyzed based on the "Actions-Tactics-Strategies (ATS)" research path. In this study, we report current preliminary analysis, which discovered twenty-five types of knowledge change actions, and identified eight types of knowledge change tactics using bottom-up clustering methods. The findings are the basis for our further exploration of searchers' learning strategies during the whole session, also present a complete behavioral and cognitive picture of searchers' knowledge change process, for search systems providing assistance at different stages of searching and learning.

Original languageAmerican English
JournalCEUR Workshop Proceedings
StatePublished - 2020
Event2020 International Conference on Information and Knowledge Management Workshops, CIKMW 2020 - Galway, Ireland
Duration: Oct 19 2020Oct 23 2020

ASJC Scopus subject areas

  • Computer Science(all)


  • Actions-Tactics-Strategies (ATS)
  • Knowledge change behaviors
  • Knowledge structure
  • Learning-related tasks
  • Search as Learning (SAL)


Dive into the research topics of 'Modeling Knowledge Change Behaviors in Learning-related Tasks'. Together they form a unique fingerprint.

Cite this