• The Journal of Korean Association of Computer Education
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• v.10 no.5
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• pp.43-51
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• 2007

We have developed a peer agent to support programming learning and analyzed its educational effects in a programming course. The agent acts as a tutor or a tutee. The role of a tutor/tutee is like the role of a navigator/driver in pair programming. While students are learning with the peer agent, the students' programming abilities are modeled. Based on the student's model, the peer agent provides appropriate feedbacks and contents to the learner. The peer agent gives positive effects on learners' achievement and self-efficacy in a programming course. It means that the peer agent system helps the learner in an affective domain as well as a cognitive domain.

• The Journal of Korean Association of Computer Education
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• v.12 no.4
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• pp.1-11
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• 2009

There are several studies in order to learn about programming language. This paper develops a peer agent system based on teaching and learning strategies in order to improve problem-solving ability. Problem-solving ability involves three components - content understanding, domain-dependent problem-solving strategies, and self-regulation. This paper shows the teaching and learning strategies about components of problem-solving for learning to program and the peer agent system uses the teaching and learning strategies. This study intents to analyze the educational effects of the peer agent system. The results show that the system has superior effects on problem-solving ability compared to traditional programming courses or pair programming courses. It means that the peer agent system is the effective educational system in improving student's problem-solving ability.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.168-173
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• 2008

Recently, many researchers have studied on agent-based edutainment systems to improve students' learning experience. In this paper, we present AR Gardening which makes users experience interactive flower gardening with a bluebird, a learning companion agent, squatting in an augmented picture. The proposed system augments the animated bluebird to support interactive edutainment experiences. The bluebird perceives users' actions as well as environmental situations. It then appraises situational information to provide participants with problem-solving guidelines. Moreover, the bluebird responds like a companion than an instructor through anthropomorphic expression. To demonstrate our work, we exhibited the implemented AR Gardening and reviewed participants' responses to the system. In this exhibition, we could find that the learning companion-like bluebird helped users experience how to properly grow the flower in our educational setting. Ultimately, we expect that an augmented peer learning agent is one of the key factors for developing effective edutainment applications.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.3
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• pp.355-362
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• 2009

In this paper, we present a framework for managing group behaviors in multi-agent swarm systems. The framework explores the benefits by dynamic associations with the proposed artificial potential functions to realize complex swarming behaviors. A key development is the introduction of a set of flocking by dynamic association (DA) algorithms that effectively deal with a host of swarming issues such as cooperation for fast migration to a target, flexible and agile formation, and inter-agent collision avoidance. In particular, the DA algorithms employ a so-called systematic singular association (SSA) rule for fast migration to a target and compact formation through inter-agent interaction. The resulting algorithms enjoy two important interrelated benefits. First, the SSA rule greatly reduces time-consuming for migration and satisfies low possibility that agents may be lost. Secondly, the SSA is advantageous for practical implementations, since it considers for agents even the case that a target is blocked by obstacles. Extensive simulation presents to illustrate the viability and effectiveness of the proposed framework.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.4
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• pp.1-10
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• 2010

This present paper's goal is to show a virtual human agent's movement-data loci based on realistically limited perception data is human-like. To determine human-likeness of the movement-data loci, we consider interactions between two parameters: Realistically Limited Perception (RLP) data and Incremental Movement-Path data Generation (IMPG). That is to consider how the former (i.e., RLP), one of the simulated parameters of human thought or its elements dictates the latter (i.e., IMPG), one of the simulated parameters of human movement behavior. Mapping DB is a prerequisite for navigation in an agent system because it functions as an interface between perception and movement behavior. Although Hill et al. studied mapping DB methodology based on RLP, their research dealt only with a rendering camera's view point data. The agent system in this present paper was integrated with the Hill's mapping DB module and then the two parameters' interaction was considered on a military reconnaissance mission with unexpected enemy emergence. Movement loci that were generated by the agent and subjects were compared with each other. The agent system in this present research verifies that it can be a functional test bed for producing human-like movement-data loci although the human-likeness of agent is the result of a pilot test, determined by two parameters (RLP and IMPG) and only 30 subjects.