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중학생이 지각한 부모의 학업성취압력이 학업지연행동에 미치는 영향과 평가염려 완벽주의의 매개효과
주하나(Hana Joo),박주희(Ju Hee Park) 한국아동학회 2021 아동학회지 Vol.42 No.5
Objectives: This study aimed to examine the mediating effect of evaluative concerns perfectionism on the relationship between parental academic achievement pressure perceived by middle school students and their academic procrastination. Methods: The study participants were 522 middle school students from first to third grade from three middle schools located in Gunsan, Iksan, and Jeonju. All data were analyzed using descriptive statistics and Pearson’s correlation coefficients. Structural equation modeling was also used to investigate the mediating model. Results: The result of this study showed that the level of parental academic achievement pressure perceived by middle school students increased their level of academic procrastination. In addition, the level of students’ evaluative concerns perfectionism increased the level of their academic procrastination. Second, middle school students’ evaluative concerns perfectionism partially mediated the relationship between parental academic achievement pressure perceived by them and their academic procrastination. That is, the higher the level of parental academic achievement pressure, the higher the level of evaluative concerns perfectionism, and consequently, the higher the middle school students’ academic procrastination. Conclusion: The outcomes suggest that it is important for parents to set proper expectations for their children and provide them with sufficient support, such as respecting their autonomy in academic decision-making to prevent students’ academic procrastination. In addition, interventions to change maladaptive cognitive beliefs including evaluation concerns perfectionism would help students reduce their academic procrastination. In conclusion, these findings suggest several ways to prevent and decrease middle school students’ academic procrastination by empirically verifying predictors on academic procrastination.
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TREASURE Talk 코칭모델을 활용한 임산부의 상호작용기술 증진 교육훈련 프로그램 구성과 효과
주하나,이은미,김정화 한국코칭학회 2020 코칭연구 Vol.13 No.5
본 연구의 목적은 TREASURE Talk 코칭모델을 활용한 임산부의 상호작용 기술 증진 교육훈련 프로그램을 구성하고 그 효과를 검증하는 것이다. 이를 위해 프로그램을 구성하고 임산부 13명을 대상으로 2019년 12월 10일부터 31일까 지 상호작용기술 증진 교육훈련 프로그램을 실행하였다. 프로그램의 효과를 검증을 위해 아동-성인 상호작용 코칭척도(CICAIO)로 SPSS 21.0를 활용하여 t- 검증을 실시하였다. 또한 임산부의 상호작용 중 코칭 기술 활용 빈도를 측정하기 위해 TREASURE Talk 코딩 시스템(TTCS)을 사용하여 전문가 간 일치도를 보았다. 연구결과 TREASURE Talk 코칭모델을 활용한 프로그램으로 놀잇감 선정, 스크립트, 동영상을 제작하여 프로그램이 구성되었다. 또한 프로그램을 실시한 결과 관심가지고 따라가기(T), 반영하기(R), 격려하기(E), 긍정으로 이해하기(U), 기억하고 표현하기(R1), 축하하기(E2)의 TREASURE Talk 코칭 기술이 증진된 것으로 효과가 검증되었다. The purpose of this study is to construct an interaction training program pregnant women using the TREASURE Talk coaching model and to verify its effectiveness. The program was organized and an interactive education and training program was implemented for 13 prospective mothers from December 10 to 31, 2019. To validate the effectiveness of the program, we used SPSS 21.0 to conduct t-verification of the child-adult interaction coaching measures (CICAIO). In addition, the TREASURE Talk coding system(TTCS) was used to measure the frequency of coaching skills during interaction of the pregnant women, and the consensus among the experts was visible. As a result, a program using the TREASURE Talk coaching model was organized by selecting a play object, creating scripts, and using videos. In addition, we applied the TREASURE Talk coaching of tracking with interest (T), reflecting (R), encouraging (E), understanding (U), reminding (R1), encouraging (E2) The effect was verified as the interaction technology was improved.
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주하람(Haram Joo),김준오(Juno Kim),이상완(Sang Wan Lee) 한국지능시스템학회 2018 한국지능시스템학회논문지 Vol.28 No.5
본 논문은 상태천이에 불확실성이 있는 동적 환경에서도 안정적인 학습이 가능한 model-based 강화학습 전략을 제안한다. 기존의 강화학습 알고리즘은 보상의 기대치 최대화에 초점을 둔 model-free 방식으로 환경의 불확실성을 경험적으로 습득하므로 적응 속도가 느리다. 이에 비해 환경 모델을 학습하는 model- based 방식은 아직 경험하지 못한 상황에 대한 시뮬레이션 결과를 보상의 기대치 학습에 적용함으로써 환경변화에 빠른 적응이 가능하다. 본 연구에서는 환경의 상태천이에 대한 확률 모델을 온라인 학습하고, 학습된 모델을 이용하여 확률적으로 시나리오를 시뮬레이션하며, 이를 바탕으로 보상의 기대치를 최대화하는 전략을 찾아내는 model-based 강화학습 방식을 구현하였다. OpenAI의 FrozenLake 시뮬레이터를 이용하여 불확실성을 내포한 동적 환경을 구현하였으며, 제안한 모델과 기존 방법의 성능을 다양한 측면에서 비교하였다. 제안된 모델은 상태천이의 불확실성과 환경변화의 불안정성이 모두 존재하는 극한 상황 속에서도 변화에 강인한 전략 탐색의 기틀을 제공한다. This paper proposes a model-based reinforcement learning strategy that enables stable learning even in a dynamic environment containing state transition uncertainty. The existing reinforcement learning algorithm is a model-free method that focuses on maximizing the expectation of the reward, and the adaptation speed is slow because it empirically learns the uncertainty of the environment. In contrast, the model-based method that learns the environmental model can adapt quickly to changes in the environment by applying the simulation results to the expectation reward. In this paper, we propose a model-based reinforcement learning method that finds a strategy that maximizes the expectation of reward based on the on-line learning of the probability transition model of the environment, simulates the scenario probabilistically using the learned model. We implemented the dynamic environment containing uncertainty using FrozenLake simulator of OpenAI and compared the performance of the proposed model with the existing method in various aspects. The proposed model provides a framework for strategy exploration even in extreme situations where both uncertainty of state transition and instability of environmental change exist.
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