Proposed Optimal Growth Pathfinding Method Based on Growth Trajectories

Proposed Optimal Growth Pathfinding Method Based on Growth Trajectories

Niu Woyuan (Department of Computer Science and Intelligent System, Osaka Prefecture University, Sakai, Japan), Ryosuke Saga (Department of Computer Science and Intelligent System, Osaka Prefecture University, Sakai, Japan), Hiroshi Tsuji (Department of Computer Science and Intelligent System, Osaka Prefecture University, Sakai, Japan) and Yukie Majima (Department of Computer Science and Intelligent System, Osaka Prefecture University, Sakai, Japan)
Copyright: © 2015 |Pages: 20
DOI: 10.4018/IJKSS.2015100105
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Abstract

In this study, the authors propose an optimal growth pathfinding method to support learners in effectively mastering a set of capabilities. Under the assumption of prerequisite relationships among learning objectives, the main processes of the method are as follows: (1) extracting the capability structure from growth trajectories, (2) remodeling the problem as a traveling salesman problem with restrictions among learning objectives, and (3) generating the cost matrix and obtaining the optimal growth path. In addition, a flexible approach to data standardization as a step of capability structure extraction is discussed. The proposed method is also applied to a software engineer growth dataset with 30 responders.
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The formal concepts of capability space and capability structure are derived from the analogy of KST (Doignon & Falmagne, 1985). The basic concepts of KST and the definitions proposed by Tsuji et al. (2013) will be briefly discussed. Doignon and Falmagne (1985) proposed the KST, which provides a formal means to describe the structure of a given domain of knowledge. Knowledge state is a set of knowledge items (problem) that an individual is able to solve. Prerequisite relationships (also formalized by surmised relationships) between knowledge items restrict the set of possible knowledge states. The set of all possible knowledge states is called a knowledge space (Stahl, 2011).

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