• Journal of The Korean Association of Information Education
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• v.15 no.4
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• pp.645-660
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• 2011

The purpose of this study is to identify the causal relationship among perceived instrumentality, mastery goal orientation, self-regulated learning and academic achievement in cyber education. 317 current students of the W cyber university participated in the study. The results of structural equation modeling analysis are as follows: First, endogenous instrumentality affects mastery goal orientation. Second, mastery goal orientation affects self-regulated learning. Third, while exogenous instrumentality had negative effect on academic achievement, self-regulated learning was the only variable that had positive effect on academic achievement. In addition, the result indicated that endogenous instrumentality had indirect effect on academic achievement and self-regulated learning mediated between mastery goal orientation and academic achievement.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.523-531
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• 2018

The purpose of this study based on descriptive research is to examine the influences on the self-regulated learning ability due to nursing students' achievement-goal and to make sure mediating effect of autonomous support of professor. A convenience sample of 294 nursing students were recruited from a college in C province from April to May, 2018. Data are analyzed using SPSS/WIN 21.0 program. As a result, it is turned out that there are positive correlation between mastery goal, performance approach goal, self-regulated learning ability and autonomous support of professor. In the Sobel test results, autonomous support of professor had a partial mediating effect between mastery goal and self-regulated learning ability(Z=3.922, p<.001). So, a new teaching method should be developed through the convergence of related factors of self - regulated learning ability.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.395-405
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• 2021

This study explored the structural relationship between achievement goal orientation, learning motivation, and level of interest on the level of flow in distance PBL classes. A structural model was constructed through analysis of previous studies. Data for the verification of the structural model were collected from 59 students in the teaching courses. The study took PBL lessons remotely. After the PBL class was ended, flow, achievement goal orientation, learning motivation, and level of interest were measured. The collected data were analyzed using the structural equation model analysis method. As a result, the structural model proposed in this study was verified to be valid. It was verified that the level of interest mediated the influence of achievement goal orientation and learning motivation to influence commitment. Next, it was verified that the path through which the level of interest mediates the effect of achievement goal orientation has the greatest influence. Therefore, it is possible to suggest that the level of flow can be adjusted if the level of interest is adjusted in consideration of the achievement goal orientation of learners when operating distance PBL classes.

• Educational Technology International
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• v.11 no.1
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• pp.171-192
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• 2010

This study was to investigate the elaborated project based learning model for scientifically gifted in the context of R & E project learning. It is important for the scientifically gifted to provide the appropriate learning environments instead of general learning model for the gifted. Although R & E project learning model is effective, the model has the limitations of managing the course for the scientifically gifted. To improve R & E learning model, the elaborated project based learning model was suggested with integration of both project based learning model and goal based scenario. The elaborated project-based learning model was comprised with 'basic learning process', 'elaboration through inquiry', and 'presentation and reflection'. To measure the satisfaction, eighty scientifically gifted students participated in the class. The result shows that learners were satisfied with the elaborated project-based learning up to 90%, and teachers were satisfied with this model up to 77%.

• Journal of The Korean Association For Science Education
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• v.25 no.3
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• pp.321-326
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• 2005

The purpose of this study was to identify distinctive achievement goal patterns of students and examine their influence on learning strategies (deep/surface) and science achievement. Cluster analysis procedure was performed to classify students on the basis of task, performance, and performance-avoidance goal scores. The results produced 3 clusters of students with different achievement goal patterns: high task goal (cluster 1), high task-high performance goal (cluster 2), and low task-low performance goal (cluster 3). One-way ANOVA results revealed that the scores of cluster 2 were significantly higher than those of clusters 1 and 3 in deep learning strategy. The science achievement test scores of clusters 1 and 2 were higher than those of cluster 3. Looking at surface learning strategy, however, the test scores of cluster 3 were significantly higher than those of clusters 1 and 2. The educational implications of these findings are discussed.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.181-188
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• 2022

An intelligent distributed multi-agent system (IDMS) using reinforcement learning (RL) is a challenging and intricate problem in which single or multiple agent(s) aim to achieve their specific goals (sub-goal and final goal), where they move their states in a complex and cluttered environment. The environment provided by the IDMS provides a cumulative optimal reward for each action based on the policy of the learning process. Most actions involve interacting with a given IDMS environment; therefore, it can provide the following elements: a starting agent state, multiple obstacles, agent goals, and a cluttered index. The reward in the environment is also reflected by RL-based agents, in which agents can move randomly or intelligently to reach their respective goals, to improve the agent learning performance. We extend different cases of intelligent multi-agent systems from our previous works: (a) a proposed environment-clutter-based-index for agent sub-goal selection and analysis of its effect, and (b) a newly proposed RL reward scheme based on the environmental clutter-index to identify and analyze the prerequisites and conditions for improving the overall system.

• Educational Technology International
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• v.19 no.2
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• pp.175-197
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• 2018

Flipped classroom (FC) has gained attention as an active learning approach. Designing effective out-of-class activities to help prepare students for in-class activities is fundamental for successful implementation of FC. This study investigated the effectiveness of Goal-Based Scenarios (GBS) for out-of-class learning in FC. Four out of twelve units in a medical humanities course for Year 2 medical students was redesigned into a FC format, where e-learning modules were designed using a GBS approach for out-of-class activities and classroom debates were implemented for in-class activities. The other eight units were delivered in a conventional classroom debate format, which included reading text materials as pre-class assignments. A formative evaluation study was conducted using questionnaires and interview methods and students' academic achievements were evaluated by comparing their pre- and post-test scores between FC and conventional units. Students had positive perceptions of the e-learning modules in GBS approach and preferred the structure of learning in the FC format. Students' pre-test scores were slightly higher in the FC units, yet their post-test scores were comparable with conventional units. This study illustrates students' perceptions that the learning was bettered structured in FC and that the out-of-class learning using the GBS approach helped them better prepared for in-class activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1477-1491
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• 2017

Inspired by the multi-scale nature of hippocampal place cells, a biologically inspired model based on a multi-scale spatial representation for goal-directed navigation is proposed in order to achieve robotic spatial cognition and autonomous navigation. First, a map of the place cells is constructed in different scales, which is used for encoding the spatial environment. Then, the firing rate of the place cells in each layer is calculated by the Gaussian function as the input of the Q-learning process. The robot decides on its next direction for movement through several candidate actions according to the rules of action selection. After several training trials, the robot can accumulate experiential knowledge and thus learn an appropriate navigation policy to find its goal. The results in simulation show that, in contrast to the other two methods(G-Q, S-Q), the multi-scale model presented in this paper is not only in line with the multi-scale nature of place cells, but also has a faster learning potential to find the optimized path to the goal. Additionally, this method also has a good ability to complete the goal-directed navigation task in large space and in the environments with obstacles.

• Culinary science and hospitality research
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• v.20 no.1
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• pp.1-17
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• 2014

Under the needs to study the predictors of the frontline employees' service-oriented organizational citizenship behaviors(OCBs) in the hotel industry, this study aimed to the influence of goal orientations on the OCBs. The two factors of performance goal and learning goal orientations were hypothesized to influence each dimension of OCBs(loyalty, service delivery, and participation). The data from 266 five star deluxe hotel employees were analyzed with descriptive statistics, multi-variate analysis of variance, and structural equation modeling conducted using SPSS 19.0 and AMOS 20.0. The results showed learning goal orientation positively influenced all dimensions of the OCBs while performance goal orientation positively influenced all dimensions except loyalty. These results suggest that hotel practitioners need to seek the applicants who are willingly oriented to specific goals at recruiting process. Furthermore, hotel organizations need to utilize the employees' goal approach to motivate their performances.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.5
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• pp.265-270
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• 2010

Reinforcement learning performs learning through interacting with trial-and-error in dynamic environment. Therefore, in dynamic environment, reinforcement learning method like TD-learning and TD(${\lambda}$)-learning are faster in learning than the conventional stochastic learning method. However, because many of the proposed reinforcement learning algorithms are given the reinforcement value only when the learning agent has reached its goal state, most of the reinforcement algorithms converge to the optimal solution too slowly. In this paper, we present GDRLS algorithm for finding the shortest path faster in a maze environment. GDRLS is select the candidate states that can guide the shortest path in maze environment, and learn only the candidate states to find the shortest path. Through experiments, we can see that GDRLS can search the shortest path faster than TD-learning and TD(${\lambda}$)-learning in maze environment.