• 전자공학회논문지 IE
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• v.49 no.2
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• pp.65-74
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• 2012

The purpose of this study is to verify the prediction of mastery-approach goal orientation, task value, and self-regulated learning strategy on academic satisfaction and achievement of cyber engineering university students. For this study, 219 engineering students of H cyber university who enrolled in the spring semester of 2011 was chosen and completed web surveys. A hypothetical model proposed included mastery-approach goal orientation, task value, and self-regulated learning strategy as predictors, and academic satisfaction and achievement as criteria variables. The results of this study through multiple regression analysis indicated that task value(${\beta}$=.401) and self-regulated learning strategy(${\beta}$=.401) predicted significantly on academic satisfaction. In addition, self-regulated learning strategy(${\beta}$=.301) and mastery-approach goal orientation(${\beta}$=.196) predicted significantly on academic achievement. The result of this study suggested that mastery-approach goal orientation, task value, and self-regulated learning strategy should be considered for improving academic satisfaction and achievement in cyber engineering education.

• Journal of the Korea Convergence Society
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• v.11 no.4
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• pp.287-294
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• 2020

The purpose of this study was to analyze the influence of the achievement goal orientation of military cadets and grit on the military cadets' certification of physical fitness rank and on the improvement of the grade. To accomplish the research purpose, a survey was conducted to 350 cadets of the Korea Military Academy in December 2019, and 322 valid responses were analyzed by applying the path analyses. As a result, among the sub-factors of achievement goal orientation, the direct effect of task goal orientation on the two sub-factors of the grit was statistically significant. In addition, the effect of task goal orientation on grade physical certification was statistically significant, with the use of grit(maintaining interest) and grit(continuing effort) as meditating variables. In addition, task goal orientation had a significant effect on the improvement of physical strength grade via two sub-factors of Grit as meditating variables.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.149.3-149
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• 2001

Task allocation is a key problem in multiagent systems. The importance of automated negotiation protocols for solving the task allocation problem is increasing as a consequence of increased multi-agent applications. In this paper, we introduce the multiagent Traveling Salesman Problem(TSP) as an example of task reallocation problem, and suggest Vickery auction as an inter-agent coordination mechanism for solving this problem. In order to apply this market-based coordination mechanism into multiagent TSPs, we define the profit of each agent, the ultimate goal of negotiation, cities to be traded out through auctions, the bidding strategy, and the order of auctions. The primary advantage of such approach is that it can find an optimal task allocation ...

• Journal of Educational Research in Mathematics
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• v.8 no.1
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• pp.163-182
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• 1998

Performance assessment is one of the authentic assessment method that are consistent with new curriculum goal, concentrated on the process rather than the results of problem solving. But the key to good assessment is matching the assessment task to intended objectives. Based on the review of literatures, the current performance assessment task was critically analysed. As a result, this study developed appropriate model and criteria of performance assessment task to elementary mathematics.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.9
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• pp.407-418
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• 2023

The Multi-Object Goal Visual Navigation(MultiOn) is a visual navigation task in which an agent must visit to multiple object goals in an unknown indoor environment in a given order. Existing models for the MultiOn task suffer from the limitation that they cannot utilize an integrated view of multimodal context because use only a unimodal context map. To overcome this limitation, in this paper, we propose a novel deep neural network-based agent model for MultiOn task. The proposed model, MCFMO, uses a multimodal context map, containing visual appearance features, semantic features of environmental objects, and goal object features. Moreover, the proposed model effectively fuses these three heterogeneous features into a global multimodal context map by using a point-wise convolutional neural network module. Lastly, the proposed model adopts an auxiliary task learning module to predict the observation status, goal direction and the goal distance, which can guide to learn the navigational policy efficiently. Conducting various quantitative and qualitative experiments using the Habitat-Matterport3D simulation environment and scene dataset, we demonstrate the superiority of the proposed model.

• 한국IT서비스학회:학술대회논문집
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• 2007.11a
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• pp.479-484
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• 2007

Annual IT budget of major companies in South Korea is 44.7 billion Korean Won on average in 2007. A survey shows that among these companies, 58% of them had decided to increase the budget compared to that in previous year. In this study, we investigate relationship between IS investment goal and IS performance with moderating effect of process for IS infrastructure maintenance. For this purpose, we used a set of data with 271 responses gathered from manufacturing industry in 2006. Our results show that the relationship exists but varies across the task characteristics and there's no moderating effect. This finding implies that practitioners should be aware of that different goals need to be established based on task characteristics and more effort to harmonize the process with task activities is required to them.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5104-5113
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• 2012

To study the influence of leadership types on achievement goal orientation of high school athletes, Multifactor Leadership Questionnaire (MLQ) and Task and Ego Orientation in Sport Questionnaire (TEOSQ) were used to two-hundred-ninety athletes in Physical Education High Schools. The data from the survey were analyzed by SPSS 19.0 and the results of the analysis were as in the following. First, there were meaningful differences in the leadership factors according to the demographic characteristics. Male athletes tended to be more conscious of the individualized consideration(transformation leadership), contingent reward, management by exception(transactional leadership) of the leadership factors. Second, there were differences in athletes' achievement goal orientation. Comparing to the female athletes, male athletes gave more importance on the task goal orientation. Third, there were differences in the leader's leadership types on the athlete's achievement goal orientation. The athletes with self goal orientation and the individualized consideration (transformational leadership) of leadership types showed statistically meaningful differences. The athletes with high self goal orientation had a negative relationship to the individualized consideration and a positive relationship to the intellectual stimulation. The athletes with task goal orientation showed differences in the transactional leadership as well as in the contingent reward.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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• pp.415-423
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• 1997

The purposes of this study were to investigate the intercorrelations among various motivational patterns and learning strategies and to examine the differences in motivation and strategy usage in terms of students' science achievement level, gender, and grade. A questionnaire on achievement goal, self-efficacy, self-concept of ability, expectancy, value, causal attributions, and learning strategies was administered to 360 junior high/high school students (178 males, 182 females). Students who adopted performance-oriented goal tended not to be task oriented. Task-oriented students had high levels of self-efficacy, high self-concept of ability, and expectancies for future performance in science. They also valued science and attributed thier failures to the lack of effort. However, performance-oriented students evaluated their ability negatively, did not value science, and attributed thier failures to uncontrollable causes. With respect to learning strategy, task-oriented students tended to use deep-level strategy, whereas performance-oriented students tended to use surface-level strategy and not to use deep-level strategy. High-achieving students, boys, and junior high school students were more task-oriented, evaluated their ability more positively, and valued science more than low-achieving students, girls, and high school students, respectively. High-achieving students and boys also used deep-level strategy more than each of their counterparts. However, no significant difference in learning strategy was found between junior high school students and high school students. Educational implications of these findings are discussed.

• ETRI Journal
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• v.30 no.1
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• pp.1-12
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• 2008

In this paper, we propose an efficient single-resource task scheduling algorithm for the Communication, Ocean, and Meteorological Satellite. Among general satellite planning functions such as constraint check, priority check, and task scheduling, this paper focuses on the task scheduling algorithm, which resolves conflict among tasks which have an exclusion relation and the same priority. The goal of the proposed task scheduling algorithm is to maximize the number of tasks that can be scheduled. The rationale of the algorithm is that a discarded task can be scheduled instead of a previously selected one depending on the expected benefit acquired by doing so. The evaluation results show that the proposed algorithm enhances the number of tasks that can be scheduled considerably.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.