.png){kind=icon}
.png){kind=icon}
Return
Knowledge exploration with concept association techniques
.png){kind=icon}
PRE
AI
DOI：10.1108/14684521011084627.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Purpose - Exploratory learning is regarded as an important ability for developing knowledge from open environments. During the exploration, learners not only need to acquire new information based on their current interests, but also they need to form new perspectives by incorporating new knowledge into their previous knowledge. This paper seeks to address these issues. Design/methodology/approach - To this end, this paper proposes an approach that includes a concept association bank to recommend related concepts in a domain based on the goal of an exploration. By doing so, learners' knowledge can be expanded beyond their current understanding. An experiment was conducted to investigate how the proposed approach facilitated the learners' exploration. Findings - The results indicated that the concept association bank is a useful mechanism to help learners gain new understanding, including providing exploration directions, reducing complexity and cognitive load, facilitating data- and goal-driven exploration strategies, and commenting on new understanding. The implications of these results are discussed. Originality/value - Current recommendation systems emphasise a data-driven strategy, which seeks isolated pieces of information, instead of suggesting directions related to their exploration goal. The problem with such an approach is that learners' exploration will be limited by their existing knowledge. Thus, this paper presents an approach to support both data- and goal-driven strategies.
Keywords:
Worldwide web
Learning
Knowledge mining
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.5
Papers:
2.1K
Citations:
3.0K