• Journal of The Korean Association For Science Education
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• v.25 no.5
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• pp.563-570
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• 2005

This study investigated the relationships among middle school students' understanding about the nature of scientific knowledge, conceptual understanding, and learning strategies. Grade 7 students (N=162) in Incheon completed the nature of scientific knowledge scales (NSKS) and a learning strategy questionnaire. After learning density by way of a CAl program, a conception test was administered. Results indicated that students' conceptual understanding and both deep and surface learning strategies were significantly correlated to their understanding about the nature of scientific knowledge. A cluster analysis was used to classify students on the basis of their deep and surface learning strategies. Three clusters of students with distinctive learning strategy patterns were found; high deep-low surface strategy (cluster 1), low deep-high surface strategy (cluster 2), and high deep-high surface strategy (cluster 3). One-way ANOVA results revealed that the scores of cluster 3 were significantly higher than those of the others in the NSKS and the conception test. Additionally, cluster 1 also performed better than cluster 2 in the conception test. Lastly, educational implications were discussed.

• 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.

• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.1-8
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• 2006

This study investigated the causal relationships between high school student multiple-choice algorithmic chemistry problem solving and 1) the motivational variables of achievement goal (task goal/performance goal/performance-avoidance) and perceived ability, and 2) the cognitive variables of learning strategy (deep learning/surface learning) and self-regulation. Path analysis supported a causal model in which perceived ability and task goal were found to positively influence algorithmic chemistry problem-solving ability via self-regulation. In particular it was found that perceived ability directly influenced algorithmic chemistry problem-solving ability. Moreover, deep learning was found to have been influenced by perceived ability and task goal, while surface learning was influenced by performance-avoidance goal. Lastly, there did not appear to be any causal relationship between learning strategy and algorithmic chemistry problem-solving ability.

• Journal of Sensor Science and Technology
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• v.33 no.3
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• pp.139-146
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• 2024

Surface-enhanced Raman scattering (SERS) enables the detection of various types of π-conjugated biological and chemical molecules owing to its exceptional sensitivity in obtaining unique spectra, offering nondestructive classification capabilities for target analytes. Herein, we demonstrate an innovative strategy that provides significant machine learning (ML)-enabled predictive SERS platforms through surface-engineered graphene via complementary hybridization with Au nanoparticles (NPs). The hybridized Au NPs/graphene SERS platforms showed exceptional sensitivity (10^-7 M) due to the collaborative strong correlation between the localized electromagnetic effect and the enhanced chemical bonding reactivity. The chemical and physical properties of the demonstrated SERS platform were systematically investigated using microscopy and spectroscopic analysis. Furthermore, an innovative strategy employing ML is proposed to predict various analytes based on a featured Raman spectral database. Using a customized data-preprocessing algorithm, the feature data for ML were extracted from the Raman peak characteristic information, such as intensity, position, and width, from the SERS spectrum data. Additionally, sophisticated evaluations of various types of ML classification models were conducted using k-fold cross-validation (k = 5), showing 99% prediction accuracy.

• Journal of The Korean Association For Science Education
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• v.27 no.3
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• pp.169-179
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• 2007

We investigated the effects of hypothetical deductive experiment on students' views about the nature of science (NOS). Participants were 212 eighth graders from a middle school and they were assigned to a control group and an experimental group. Students of the control group did guided experiment in small group and students of the experimental group did hypothetical deductive experiment in small group. The results revealed that both students of the control group and the experimental group possessed similar views about NOS in a pretest. But the experimental group exhibited more sophisticated views about the theory of dependance of observation, scientific reasoning and hypothesis in the posttest. Students who used mainly surface learning strategy within the experimental group exhibited more sophisticated views about hypothesis in the posttest. On the other hand, students who used mainly deep learning strategy within the experimental group exhibited more sophisticated views about the theory of dependance of observation, scientific reasoning and hypothesis in the posttest.

• The Journal of Korean Academy of Sensory Integration
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• v.16 no.3
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• pp.11-21
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• 2018

Objective : The purpose of this study is to investigate correlation of sensory processing patterns, learning styles and learning strategies for university students. Methods : Participants of this study are 115 students from K university in Busan, South Korea. Measurements are Adolescent/Adult Sensory Profile (AASP) for sensory processing patterns, the Study Process Questionnaire (SPQ) for learning styles, and the Motivated Strategies for Learning Questionnaire (MSLQ) for learning strategies. The data collected was analyzed by SPSS/WIN 20.0 for chisuare test and Pearson corelation coefficient. Results : For sensory processing patterns and learning styles, there were correlation between low registration type and surface type of learning (p=0.03), and between sensory seeking type and deep type of learning (p=0.02). For sensory processing patterns and learning strategies, sensory seeking type was correlated with organized learning strategy (p=0.00), and sensory sensitivity type was correlated with organizational learning strategy (p=0.03) and meta-cognitive learning strategy (p=0.00). Conclusion : This study found that there is correlation between sensory processing patterns, learning styles and learning strategies with implying learning styles and learning strategies can be different depends on sensory procession pattern. The results of this study can be used as a basic data to select learning type and learning strategy appropriate for an individual based on his or her sensory processing patterns.

• Journal of The Korean Association For Science Education
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• v.17 no.1
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• pp.75-83
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• 1997

The purpose of this study was to determine the strategies that middle school students used in solving problems concerning density and solubility. These were compared in the aspects of problem contexts for 42 students of varying logical reasoning ability, spatial ability, and learning approach. A coding scheme used consists of five categories: reading & organization, production, errors, evaluation, and strategy. Students' protocols were analyzed after intercoder agreement had been established to be .95. The results were as follows: 1. Students had more difficulties in reading and organizing the problems in everyday contexts than in scientific contexts. Students at the concrete-operational stage and / or surface approach were more likely to have difficulties in reading and organizing the problems than those at the formal-operational stage and / or deep approach. 2. Students tended to split up the solubility problems into sub-problems and to solve the density problem in everyday contexts in random manner. These were significantly correlated with the test scores concerning logical reasoning ability, spatial ability, and learning approach at the .1 level of significance. 3. Major errors in solving the density problems were to disregard the given information or generated and to use inappropriate information. Many errors in solving the solubility problems were found to be executive errors. The strategy to use the information given appropriately was positively related to students' logical reasoning ability, spatial ability, and learning approach. 4. More evaluation strategies were found in everyday contexts. Their strategies to grasp the meaning of answers and to check the math were significantly related to students' logical reasoning ability. 5. Students used the random trial-and-error strategy more than the systematic strategy and the systematic trial-and-error strategy, especially in everyday contexts. The strategies used by the students were significantly related to students' logical reasoning ability, spatial ability, and learning approach.

• 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 the Korean institute of surface engineering
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• v.55 no.3
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• pp.156-163
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• 2022

An ammonium ion with a size and charge similar to that of potassium can bind to valinomycin, which is used as an ion carrier for potassium, and cause a meaningful interference effect on the detection of potassium ions. Currently, there are few ion sensors that correct the interference effect of ammonium ions, and there are few studies that specifically suggest the mechanism of the interference effect. By fabricating a SPCE-based potassium ion-selective electrode, the electromotive force was measured in the concentration range of potassium in the nutrient solution, and the linear range was measured to be 10^-5 to 10^-2 M, and the detection limit was 10^-5.19 M. And the interference phenomenon of the potassium sensor was investigated in the concentration range of ammonium ions present in the nutrient solution. Therefore, a data-based analysis strategy using deep learning was presented as a method to minimize the interference effect.

• Journal of Korean Elementary Science Education
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• v.26 no.3
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• pp.286-294
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• 2007

This study investigated the relationship between elementary school students' creative problem solving skills in terms of science and cognitive strategies. Creative problem solving in science was measured by 4 variables; appropriateness, scientific ability, concreteness, and originality. Cognitive strategies were measured by 6 variables; surface(rehearsal), deep(elaboration and organization), and metacognitive strategies(planning, monitoring, and regulating). The KEDI Creative Problems Solving Test in Science(Cho et al., 1997) and the Motivated Strategies for Learning Questionnaire(Pintrich & DeGroot, 1990) were administered to 72 subjects. Data were analyzed by means of Pearson's correlation and multiple regression analysis. Our findings indicated a positive correlation between creative problem solving in science and cognitive strategies. The surface cognitive strategy (rehearsal) positively predicted the total score, the scientific ability's score, the concrete score, and the original score of creative problem solving in science. The deep cognitive strategy(organization) positively predicted the appropriate score and the metacognitive strategy(planning) positively predicted the original score of scientific creative problem solving skills.