• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1303-1311
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• 1999

In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.

• Journal of Information Technology Services
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• v.7 no.4
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• pp.231-246
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• 2008

This paper suggests an e-learning system model, a goal-driven personalized e-learning system, which increase the effectiveness of learning. An e-learning system following this model makes the learner choose the learning goal. The learner's choice would lead learning. Therefore, the system enables a personalized adaptive learning, which will raise the effectiveness of learning. Moreover, this paper proposes a SCORM standard, which modifies SCORM 2004 that has been insufficient to implement the "goal driven personalized e-learning system." We add a data model representing the goal that motivates learning, and propose a standard for statistics on learning objects usage. We propose each standard for contents model and sequencing information model which are parts of "goal driven personalized e-learning system." We also propose that manifest file should be added for the standard for contents model, and the file which represents the information of hierarchical structure and general learning paths should be added for the standard for sequencing information model. As a result, the system could sequence and search learning objects. We proposed an e-learning system and modified SCORM standards by considering the many factors of adaptive learning. We expect that the system enables us to optimally design personalized e-learning system.

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.158-165
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• 2011

Statistics by structural equation modeling techniques were used to assess a model of chemistry learning strategy based on performance goal orientation. In the optimal Model III of this research, Performance-approach goal was positively related to the use of learning strategy(p<.05) and achievement need(p<.05). Performance-avoidance goal was negatively related to learning strategy(p<.05) and was positively related to self-handicapping tendency(p<.15). Performance-approach goal affected learning strategy indirectly through achievement need(p<.05). Use of achievement need was positively related to learning strategy(p<.05) and self-handicapping tendency(p<.35). Self-handicapping tendency affected learning strategy negatively(p<.05). Implications of these findings for learning strategy in chemistry education are discussed.

• The Journal of Information Systems
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• v.30 no.2
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• pp.127-146
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• 2021

Research Purpose Advances in digital technology have facilitated the widespread adoption of online learning, which has become a substantial way of learning. Although digital immigrants have become a main group of users of learning online, there is a lack of understanding of their online learning. This study aims to explore digital immigrants' adoption of online learning from the goal-pursuit perspective to gain insight into how they use online learning. Research Method A laddering interview was conducted with 22 Korean adults to elicit their goals in online learning. Then, a means-end chain analysis was used to derive their hierarchical goal structure. Findings The results reveal digital immigrants' goal structure of online learning, consisting of four attributes of online learning (e.g., accessibility, diversity, up-to-dateness, and repeatability) and six goals (e.g., self-esteem, enjoyment, recognition, productivity, gaining insights, and positive relations). This study contributes to the literature by providing a rich picture of their use of online learning.

• Journal of the Korean Chemical Society
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• v.51 no.5
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• pp.459-470
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• 2007

The structural equation modeling techniques were used to assess a model of chemistry learning strategy based on self-handicapping tendency and goal orientation. Data were collected during chemistry lessons in two high schools. In the optimal model II-2 of this research, the self-handicapping tendency was negatively related to the use of self-efficacy. The learning goal was positively related to the use of self-efficacy and to learning strategy. The performance- approach goal was positively related to self-efficacy but presented an negative relationship to learning strategy. The performance-avoidance goal was negatively related to self-efficacy but presented an positive relationship to learning strategy. Besides affecting the learning strategy through self-efficacy indirectly, the learning goal, performance-approach goal, and performance-avoidance goal affected learning strategy directly. The self-handicapping tendency and performance- avoidance goal were a negative predictors of self-efficacy, but the learning goal and performance-approach goal were a positive predictors. And the self-efficacy affected learning strategy positively. The implications of these findings for learning strategy in chemistry are discussed. Although the paths model of relationships of the motivations to learn and learning strategies in chemistry education as mentioned above is established, the more systematic search for the higher self-efficacy and learning strategy in different courses and curriculums may be needed.

• Journal of Digital Convergence
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• v.11 no.12
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• pp.829-834
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• 2013

This study intended to find out an efficient teaching-learning method by identifying the effect of achievement goal directivity and self-regulated learning strategy on the level of learning achievement in class. This study analyzed the relationship between the achievement goal directivity and the average and standard deviation of self-regulated learning strategy as well as the relationship between the achievement goal directivity and the learning achievement of self-regulated learning strategy and also analyzed the effect of achievement goal directivity and self-regulated learning strategy on the level of learning achievement, based on the result of survey targeting 133 university students attending the Department of Social Welfare. The findings show that the higher the tendency of achievement goal directivity and self-regulated learning ability, the higher the academic performance, which means that it is necessary to resolve any anxiety through motivation control training and mentoring learning for academic achievement of students and inducing their proactive participation in class.

• Journal of Korean Elementary Science Education
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• v.29 no.3
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• pp.307-315
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• 2010

In this study, we investigated the effects of cooperative learning by the levels of students' performance goal orientation in science classes on 6th graders' science achievement and science learning motivation. Two classes (47 students) from an elementary school were respectively assigned to a control group and a treatment group. A performance goal orientation test and a science learning motivation test were administered as pretests. The intervention of cooperative learning lasted for 24 class periods. A researcher-made achievement test and the science learning motivation test were administered after the instructions. ANCOVA results indicated that the score of the treatment group was significantly higher than that of the control group in the achievement test. However, no interaction was found between the cooperative learning treatment and the levels of students' performance goal orientation. There were significant aptitude-treatment interactions in science learning motivation.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.13-16
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• 2001

In reinforcement teaming, Q-learning converges quite slowly to a good policy. Its because searching for the goal state takes very long time in a large stochastic domain. So I propose the speedup method using the Q-value initialization for model-free reinforcement learning. In the speedup method, it learns a naive model of a domain and makes boundaries around the goal state. By using these boundaries, it assigns the initial Q-values to the state-action pairs and does Q-learning with the initial Q-values. The initial Q-values guide the agent to the goal state in the early states of learning, so that Q-teaming updates Q-values efficiently. Therefore it saves exploration time to search for the goal state and has better performance than Q-learning. 1 present Speedup Q-learning algorithm to implement the speedup method. This algorithm is evaluated. in a grid-world domain and compared to Q-teaming.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.1-8
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• 2006

This study investigated the causal relationships between high school student multiple-choice algorithmic chemistry problem solving and 1) the motivational variables of achievement goal (task goal/performance goal/performance-avoidance) and perceived ability, and 2) the cognitive variables of learning strategy (deep learning/surface learning) and self-regulation. Path analysis supported a causal model in which perceived ability and task goal were found to positively influence algorithmic chemistry problem-solving ability via self-regulation. In particular it was found that perceived ability directly influenced algorithmic chemistry problem-solving ability. Moreover, deep learning was found to have been influenced by perceived ability and task goal, while surface learning was influenced by performance-avoidance goal. Lastly, there did not appear to be any causal relationship between learning strategy and algorithmic chemistry problem-solving ability.

• Hwankyungkyoyuk
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• v.20 no.3
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• pp.31-44
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• 2007

The solution of the environment problem is the common issue all over the world, for this reason the necessity of the environmental education of school has emphasized. On this a variety method for environmental education is needed, this paper planned and applied the 'ecology & environment' for high school which are based on GBS theory and presented a new model of environment education. GBS(Goal-Based Scenario) is that learners are presented with an end goal that is motivating and challenging. This goal is structured such that, in order to successfully meet it learners are required to build a predetermined core set of skills and knowledge by process mission and scenario. GBS is an active learning environment in which learners are trained in study that have a real-world context. When they are back in real-world they have increased ability to apply what was learned by reflecting on the GBS learning experience. This study was designed on GBS theory and taught a class by using internet Blog. As a result, when carefully reviewing the materials such as final presentation reflect journal, we conclude that the students' awareness of a learning environment is improved and the students seems to try to apply the learning outcome to a real life.