본 연구에서는 계절 변화에 관련된 개념 유형을 분석하고, 관련 개념들에 관한 토의에 의해 인지갈등을 해소하는 방법을 강조한 발생학습 전략의 적용 효과를 알아보고자 하였다. 계절변화 관련 지구과학 개념 유형은 100가지 였으며, 그 중에서 66가지는 오개념을 포함한 비과학적 개념 유형이었다. 계절변화에 관련된 개념 평가도구는 R&D과정과 2번의 현장검증을 거쳐 개발되었다. 실험집단에는 지구과학개념 유형과 인지갈등을 반영한 4단계의 발생학습 전략을 적용하였다. 한편 통제집단에는 전통적인 교수 학습 전략을 적용하였다. 유의수준 .05에서 공변량분석을 실시한 결과 두 집단 간에는 유의미한 차이가 있는 것으로 나타났다. 이러한 연구 결과는 계절변화와 관련된 오개념을 지구과학적 개념으로 변화시키는 데 발생학습전략이 하나의 가능한 대안이 될 수 있음을 의미하는 것으로 볼 수 있다.
본 연구에서는 계절 변화에 관련된 개념 유형을 분석하고, 관련 개념들에 관한 토의에 의해 인지갈등을 해소하는 방법을 강조한 발생학습 전략의 적용 효과를 알아보고자 하였다. 계절변화 관련 지구과학 개념 유형은 100가지 였으며, 그 중에서 66가지는 오개념을 포함한 비과학적 개념 유형이었다. 계절변화에 관련된 개념 평가도구는 R&D과정과 2번의 현장검증을 거쳐 개발되었다. 실험집단에는 지구과학개념 유형과 인지갈등을 반영한 4단계의 발생학습 전략을 적용하였다. 한편 통제집단에는 전통적인 교수 학습 전략을 적용하였다. 유의수준 .05에서 공변량분석을 실시한 결과 두 집단 간에는 유의미한 차이가 있는 것으로 나타났다. 이러한 연구 결과는 계절변화와 관련된 오개념을 지구과학적 개념으로 변화시키는 데 발생학습전략이 하나의 가능한 대안이 될 수 있음을 의미하는 것으로 볼 수 있다.
This study was designed to analyze the types of concepts about earth science related to seasonal changes, so as to develop a generative learning model focused on dissolving cognitive conflicts between the aforementioned concepts through debates and using said debates to find out how effectively the ...
This study was designed to analyze the types of concepts about earth science related to seasonal changes, so as to develop a generative learning model focused on dissolving cognitive conflicts between the aforementioned concepts through debates and using said debates to find out how effectively the model works. There are 100 types of earth science concepts concering seasonal changes, 66 of which are unscientific in nature, including misconceptions. Through a second field trial and a research and development (R&D) process, a test on these concepts was developed, consisting of 14 items. For the experimental group, a four-phase generative learning strategy that reflects the types of earth science concepts and cognitive conflicts between such concepts was developed through pre-analysis and discussion, respectively. On the other hand, a traditional teaching and teaming strategy was used for the control group. A meaningful statistic gap found between the two groups through a covariance analysis, the significance level of which was 0.05. This result may be interpreted to mean that the generative teaming strategy is a possible alternative for correcting misconceptions about scientific concepts of seasonal changes.
This study was designed to analyze the types of concepts about earth science related to seasonal changes, so as to develop a generative learning model focused on dissolving cognitive conflicts between the aforementioned concepts through debates and using said debates to find out how effectively the model works. There are 100 types of earth science concepts concering seasonal changes, 66 of which are unscientific in nature, including misconceptions. Through a second field trial and a research and development (R&D) process, a test on these concepts was developed, consisting of 14 items. For the experimental group, a four-phase generative learning strategy that reflects the types of earth science concepts and cognitive conflicts between such concepts was developed through pre-analysis and discussion, respectively. On the other hand, a traditional teaching and teaming strategy was used for the control group. A meaningful statistic gap found between the two groups through a covariance analysis, the significance level of which was 0.05. This result may be interpreted to mean that the generative teaming strategy is a possible alternative for correcting misconceptions about scientific concepts of seasonal changes.
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문제 정의
The final aim of research on this topic, therefore, is to find out preconceptions related to the specific science concepts learners have and to transform misconceptions into correct scientific concepts (Gilbert et al., 1985).
This study developed a test of earth science con-cepts on seasonal changes through an R&D process. The test was made up of 14 two-tiered, two-to-four-option multiple choice items.
This test was designed to extract earth science concepts about seasonal changes from learners. It consists of 14 items.
제안 방법
The test was applied to a first field trial to get l* earners responses. On the basis of this first field trial, types of conceptions were analyzed and a generative learning strategy was developed. The effects of such generative learning strategy on changes in earth science concepts was analyzed, and the following conclusion is hereby made.
This study analyzes the types of, and reasons for, misconceptions on seasonal changes and finds out how a generative learning strategy focused on cognitive conflict through debates between colleagues affects changes in concepts about earth science.
This study, which deals with science textbooks from middle school and science and earth science textbooks from high school, follows this procedure: experimental design and sampling, text analysis by analyzing units that describe the reasons for seasonal changes, development of an earth science concept test related to the reasons for seasonal changes, development and adoption of a generative learning strategy, and pre- and post-test and statistical analysis on concept changes. This methodology is illustrated in greater detail as follows:
대상 데이터
known. As such, 43 boys and girls, each attending first year high school, were tested. Table 2 shows an analysis of the results of such tests.
Chosen as subjects were 86 boys and girls, each attending high schools in large cities. Of these, 85 (43 boys and 42 girls) were in the experimental group and 87 (43 boys and 44 girls) were in the control group.
It consists of 14 items. Item 12 is shown below.
The test was made up of 14 two-tiered, two-to-four-option multiple choice items. The test was applied to a first field trial to get l* earners responses.
이론/모형
An earth science concept test was developed in this study using Borg and GalFs R&D (1983). Fig.
성능/효과
Third, the test results on the treatment of the gen-er언ive learning strategy showed a 16.4% increase in the experimental group and a 10.5% increase in the control group. Statistically, there was a meaningful difference, according to the covariance analysis, the significance level of which was 0.
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