• Journal of The Korean Association For Science Education
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• v.19 no.2
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• pp.315-328
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• 1999

The purpose of this study was to investigate the effects of psychological learning environment generated by science teachers upon students' affective perceptions and cognitive learning. The subjects of the study were consist of 2.693 students from secondary school. The students' perception were examined by the tools of PLEIS(Psychological Learning Environment Instrument by Science teacher). SAMS(Science Anxiety Measurement Scale). HARS(High schools' s Attitude Related Science). and ALWSS(Attitude toward Laboratory Work Scale in Secondary school). and cognitive learning outcomes assessed to TIPS II (Test of Integrated Process Skills II ) and science test score. The results of this study suggest that positive psychological learning environment by science teacher should be offered to students for the improvement of science achievement. and learning environment will be used as an instrument of self assessment for improving science teaching strategy. Understanding of relationship among psychological learning environment, affective perception and cognitive learning will be helpful to the design of science teaching and learning process.

• Journal of The Korean Association For Science Education
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• v.17 no.4
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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.

• Journal of the Korean Chemical Society
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• v.54 no.5
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• pp.643-649
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• 2010

In this study, the relationships among high school students' epistemological views on theory and data, science process skills, the perceptions of the preferred laboratory learning environment and attitudes toward laboratory work were investigated. The results indicated that science process skills, all subcategories of the perceptions of the preferred laboratory learning environment (student cohesiveness, open-endedness, integration, rule clarity, and material environment) and attitudes toward laboratory work were significantly correlated with epistemological views on theory and data. The results of multiple regression analysis revealed that science process skills, open-endedness and material environment and attitudes toward laboratory work significantly predicted epistemological views on theory and data.

• Journal of The Korean Association For Science Education
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• v.17 no.2
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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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• v.43 no.4
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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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• v.14 no.10
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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.