• 한국과학교육학회지
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• 제19권2호
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• pp.315-328
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• 1999

과학 및 과학교과에 대한 학생들의 정의적 인식이 어떻게 하면 긍정적인 방향으로 행동변화를 가져올 수 있는가?에 연구의 목적을 가지고서, 과학수업 과정에서 교사에 의한 학습환경의 분위기나 풍토를 조성하는 심리적 측면이 학생들의 성취도에 미치는 영향을 조사하였다. 1년간 동일한 교사로부터 과학수업을 받은 중 고등학교 2, 3학년학생들이 가지고 있는 교사인식을 바탕으로 심리적 학습환경을 측정하고, 이 환경인식에 따른 과학불안 인식, 과학에 대한 태도, 과학 탐구능력, 과학성취도와의 관계 및 효과를 조사하였다. 이를 토대로 밝혀진 결과는 심리적 학습 환경은 학생들의 정의적 인식 성향과 인지적 학습결과에 직 간접적인 효과를 주며, 유의한 정적 상관을 보였다. 즉, 교사가 조성하는 수업태도, 인성특성, 학습분위기, 수업행동, 지원적 행동 등은 학생들의 과학에 대한 정의적인 행동특성을 변화시킬 수 있는 수업의 사태로써 유의하게 작용할 수 있으며, 과학 성취에도 의미있게 작용한다는 점을 시사해주고 있다.

• 한국과학교육학회지
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• 제17권4호
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• pp.501-510
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• 1997

Science teachers' beliefs about science and school science and their perceptions of the science laboratory learning environment were investigated with an assumption that science laboratory teaching would be affected by science teachers' beliefs. Likert-scale questionnaires of BASSSQ and SLEI were used in this study. The major findings were as follows: 1. Science teachers showed inconsistent beliefs about science and school science. Their responses reflected a patch-like view of postmodern epistemology and objectivism They also showed somewhat different views about science and school science. It was found that science teachers had strong objectivist views about science in some parts. but they had moderate constructivist views about school science in other parts; 2. The mean scores of student cohesiveness, integration. and rule clarity on the actual version in SLEl were relatively high, but those of open-endedness and physical environment were very low; 3. There was no association between teachers' beliefs about science and their perceptions of the science laboratory learning environment. But some associations were found between teachers' beliefs about school science and their perception on student cohesiveness, integration, and rule clarity of the actual science laboratory learning environment. Teachers' beliefs about school science had some statistically significant correlations with their perceptions on all scales of the preferred version of SLEI. We could not show a causal relationship between teachers' beliefs and their science laboratory learning environment through these results. But it can be suggested that teachers' beliefs about school science do have a role in constructing a desirable science laboratory learning environment, as we found that there were statistically significant correlations between them.

• 대한화학회지
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• 제54권5호
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• pp.643-649
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• 2010

이 연구에서는 고등학생들의 이론과 자료에 대한 인식론적 관점과 과학 과정 기술, 선호하는 실험 학습 환경에 대한 인식, 실험 수업에 대한 태도 사이의 관계를 조사하였다. 연구 결과, 과학 과정 기술, 선호하는 실험 학습 환경에 대한 인식의 모든 하위 범주(응집성, 개방성, 통합성, 준칙성, 물리적 환경), 실험 수업에 대한 태도는 이론과 자료에 대한 인식론적 관점과 유의미한 상관이 있었다. 중다 회귀 분석 결과, 과학 과정 기술, 선호하는 실험 학습 환경에 대한 인식의 하위 범주 중 개방성과 물리적 환경, 실험 수업에 대한 태도가 이론과 자료에 대한 인식론적 관점을 유의미하게 예측하였다.

• 한국과학교육학회지
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• 제17권2호
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• pp.139-148
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• 1997

This study investigated the influences of the cooperative learning strategies upon students' achievement and their perceptions of learning environments in a middle school physical science course. Prior to instruction, the Group Assessment of Logical Thinking was administered, and its score was used as a blocking variable. Mid-term examination score was used as a covariate. For the treatment group with heterogeneous grouping, cooperative learning instruction (the Learning Together model) was used, which emphasized group reward, individual accountability, and role division. For the control group, traditional instruction was used. After instruction, an achievement test consisting of three subtests (knowledge, understanding, and application), and the perception questionnaire of classroom and laboratory environments, were administered. ANCOVA results revealed that there was a significant interaction between instruction and the level of logical reasoning ability although there were no significant differences in all three subtest scores of the achievement test. For the concrete operational reasoners, the treatment group performed better in the subtests of understanding and application than the control group. For students at the formal and transition levels, however, the treatment group scored lower than the control group. Significant interactions were also found in the perceptions of classroom environment and laboratory environment. For the concrete operational reasoners, the treatment group showed more positive perception than the control group. For the students at the formal and transition levels, the control group had positive perception than the treatment group. Educational implications are discussed.

• ETRI Journal
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• 제43권4호
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• pp.630-639
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• 2021

Autonomous driving requires a computerized perception of the environment for safety and machine-learning evaluation. Recognizing semantic information is difficult, as the objective is to instantly recognize and distinguish items in the environment. Training a model with real-time semantic capability and high reliability requires extensive and specialized datasets. However, generalized datasets are unavailable and are typically difficult to construct for specific tasks. Hence, a light detection and ranging semantic dataset suitable for semantic simultaneous localization and mapping and specialized for autonomous driving is proposed. This dataset is provided in a form that can be easily used by users familiar with existing two-dimensional image datasets, and it contains various weather and light conditions collected from a complex and diverse practical setting. An incremental and suggestive annotation routine is proposed to improve annotation efficiency. A model is trained to simultaneously predict segmentation labels and suggest class-representative frames. Experimental results demonstrate that the proposed algorithm yields a more efficient dataset than uniformly sampled datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권10호
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• pp.3989-4006
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• 2020

The intelligent agriculture monitoring is based on the perception and analysis of environmental data, which enables the monitoring of the production environment and the control of environmental regulation equipment. As the scale of the application continues to expand, a large amount of data will be generated from the perception layer and uploaded to the cloud service, which will bring challenges of insufficient bandwidth and processing capacity. A fog-based offline and real-time hybrid data analysis architecture was proposed in this paper, which combines offline and real-time analysis to enable real-time data processing on resource-constrained IoT devices. Furthermore, we propose a data process-ing algorithm based on the incremental principal component analysis, which can achieve data dimensionality reduction and update of principal components. We also introduce the concept of Squared Prediction Error (SPE) value and realize the abnormal detection of data through the combination of SPE value and data fusion algorithm. To ensure the accuracy and effectiveness of the algorithm, we design a regular-SPE hybrid model update strategy, which enables the principal component to be updated on demand when data anomalies are found. In addition, this strategy can significantly reduce resource consumption growth due to the data analysis architectures. Practical datasets-based simulations have confirmed that the proposed algorithm can perform data fusion and exception processing in real-time on resource-constrained devices; Our model update strategy can reduce the overall system resource consumption while ensuring the accuracy of the algorithm.