• Journal of Korean Elementary Science Education
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• v.30 no.3
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• pp.353-366
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• 2011

An alternative vision for science inquiry that appears to be important and challenging is model-based inquiry in which students generate, evaluate and revise their explanatory model. Pre-service teachers should be given opportunities to develop and use their mechanistic explanatory models in order to participate in the practice of science and to have a sound understanding of science. With this view, this study described a case of pre-service elementary teachers' scientific modeling in magnetism. The aims of this study were to explore difficulties preservice elementary teachers encountered while they engaged in a model-based inquiry, and to examine how their understandings of the nature of scientific models changed after the model-based inquiry. The data analysis revealed that the pre-service teachers had difficulties in drawing and writing their own thinking because they had little experience of expressing their own science ideas. When asked to predict what would happen, they could not understand what it meant to make a prediction "based on their model". They did not know how to use or consider their model in making a prediction. At the end of the model-based inquiry they reached a final consensus of a best model. However, they were very anxious about whether the model was the "correct" answer. With respect to the nature of scientific models, almost all of the pre-service teachers initially viewed models only as a communication tool among scientists or students and teachers to help understand others' ideas. After the model-based inquiry, however, many of them understood that they could create, test, and revise their "own" models "by themselves". They also realized the key aspects of scientific models that a model can be changed as evidence is accumulated and a model is a knowledge production tool as well as a communication tool. The results indicated that pre-service elementary teachers' understandings of the nature of scientific models and their previous school science experiences could affect their performance on a model-based inquiry, and their experience of scientific modeling could help them enhance their understandings of the nature of scientific models.

• Journal of Korean Elementary Science Education
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• v.31 no.1
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• pp.71-83
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• 2012

The purpose of this study was to explore ecological concepts, epistemological reasoning and reasoning processes through constructing 'food web and food pyramid' in ecology. We conducted classes which involved a 'food web and food pyramid' for $6^{th}$ grade students. Each class is constructed of small groups to do modeling and epistemological reasoning through communication. The researcher had videotaped and recorded each class and have made transcription about classes. We analysed patterns of 'food web and food pyramid models' and reasoning processes according to scientific epistemology using transcription data and student outputs. As a result, students represented phenomenon-based reasoning, relation-based reasoning and model-based reasoning in scientific epistemology from their modeling. Students usually did relation-based reasoning and model-based reasoning in food web which explains ecological phenonenon, while they usually did model-based reasoning in food pyramid which expects ecological phenomenon. Student's reasoning can be limited when they have misconception of scientific knowledge and are limited by fragmentary knowledge. This represents that students has to do relation-based reasoning and model-based reasoning is beneficial in their ecological model. It also suggests that students need to define correct-conception related to ecological modeling(food web, food pyramid).

• Journal of Korean Elementary Science Education
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• v.38 no.3
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• pp.375-386
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• 2019

This study explores how elementary school students construct food pyramid prediction models using scientific reasoning. Thirty small groups of sixth-grade students in the Kyoungki province (n=138) participated in this study; each small group constructed a food pyramid prediction model based on scientific reasoning, utilizing prior knowledge on topics such as biotic and abiotic factors, food chains, food webs, and food pyramid concepts. To understand the scientific reasoning applied by the students during the modeling process, three forms of qualitative data were collected and analyzed: each small group's discourse, their representation, and the researcher's field notes. Based on this data, the researcher categorized the students' model patterns into three categories and identified how the students used scientific reasoning in their model patterns. The study found that the model patterns consisted of the population number variation model, the biological and abiotic factors change model, and the equilibrium model. In the population number variation model, students used phenomenon-based reasoning and relation-based reasoning to predict variations in the number of producers and consumers. In the biotic and abiotic factors change model, students used relation-based reasoning to predict the effects on producers and consumers as well as on decomposers and abiotic factors. In the equilibrium model, students predicted that "the food pyramid would reach equilibrium," using relation-based reasoning and model-based reasoning. This study demonstrates that elementary school students can systematically elaborate on complicated ecology concepts using scientific reasoning and modeling processes.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.621-635
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• 2020

Water quality models are scientific tools that simulate and interpret the relationship between physical, chemical and biological reactions to external pollutant loads in water systems. They are actively used as a key technology in environmental water management. With recent advances in computational power, water quality modeling technology has evolved into a coupled three-dimensional modeling of hydrodynamics, water quality, and ecological inputs. However, there is uncertainty in the simulated results due to the increasing model complexity, knowledge gaps in simulating complex aquatic ecosystem, and the distrust of stakeholders due to nontransparent modeling processes. These issues have become difficult obstacles for the practical use of water quality models in the water management decision process. The objectives of this paper were to review the theoretical background, needs, and development status of water quality modeling technology. Additionally, we present the potential future directions of water quality modeling technology as a scientific tool for national environmental water management. The main development directions can be summarized as follows: quantification of parameter sensitivities and model uncertainty, acquisition and use of high frequency and high resolution data based on IoT sensor technology, conjunctive use of mechanistic models and data-driven models, and securing transparency in the water quality modeling process. These advances in the field of water quality modeling warrant joint research with modeling experts, statisticians, and ecologists, combined with active communication between policy makers and stakeholders.

• Journal of The Korean Association For Science Education
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• v.36 no.1
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• pp.15-28
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• 2016

The purpose of this study is to investigate the changes of students' modeling ability in terms of 'meta-modeling knowledge' and 'modeling practice' through co-construction of scientific model. Co-construction of scientific model instructions about astronomy were given to 41 middle-school students. The students were given a before and after instruction modeling ability tests. The results show that students' 'meta-modeling knowledge' has changed into a more scientifically advanced thinking about models and modeling after the instruction. Students were able to be aware that 'they could express their thoughts using models', 'many models could be used to explain a single phenomena' and 'scientific models may change' through co-construction modeling process. The change in the 'modeling practice' of the students was divided into four cases (the level improving, the level lowering, the high-level maintaining, the low-level maintaining) depending on the change of pre-posttest levels. The modeling practice level of most students has improved through the instruction. These changes were influenced by co-construction process that provides opportunities to compete and compare their models to other models. Meanwhile, the modeling practice level of few students has lowered or maintained low level. Science score of these students at school was relatively high and they thought that the goal of learning is to get a higher score in exams by finding the correct answer. This means that students who were kept well under traditional instruction may feel harder to adapt to co-construction of scientific model instruction, which focuses more on the process of constructing knowledge based on evidences.

• Journal of Korean Elementary Science Education
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• v.36 no.1
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• pp.16-30
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• 2017

The purpose of this study was to interpret modeling-based elementary science lessons from a perspective of distributed cognition. Data sources included three consecutive elementary science lessons dealing with particle models of gases and students' worksheet generated from modeling activities during the lessons. The data were analyzed in ways that could reveal the affordances and constraints of students' mental models and an external model in the science textbook, as well as the evolution of the models. The results showed that the students' mental models and the external model provided both affordances for and constraints to scientific problem solving and that the models evolved in the process of overcoming the constraints. Implications for science lessons and science education research were suggested.

• Journal of the Korean earth science society
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• v.37 no.4
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• pp.243-268
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• 2016

The purpose of this study was to explore the effects of Cogenerative Dialogues embedded in a modeling-centered science learning and instruction on 7th grade female $students{\acute{i}}$ understanding of scientific models and modelling A total of 49 7th grade female students in two classrooms participated in a series of five modeling-centered science lessons, and 17 students volunteered to participate in this study. Participating students were divided into four groups, and two groups were randomly assigned to a treatment group who were asked to participate in Cogenerative Dialogues after each lesson, while the others, a control group, who did not. For data analysis, Upmeier and $Kr{\ddot{u}ger^{\prime}s$ framework was used to explore $participants{\acute{i}}$ understanding of model, and a revised $Baek{\acute{i}}s$ framework was used to examine $participants{\acute{i}}$ modeling process. Data analysis indicated that students who participated in Cogenerative Dialogues generally showed richer understanding of scientific models, as well as modeling, than the others who did not. This study suggests that Cogenerative Dialogues can be used as an educationally meaningful method for science educators to encourage students actively participate in a whole process of science instruction and learning, which assists them to increase their understanding not only of scientific models and modeling specifically but also of the nature and processes of scientific practice in general.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.9-22
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• 2009

This paper first presents the author's personal view on the importance of modeling in scientific research in general, and then describes two of his works toward modeling certain aspects of human speech communication. The first work is concerned with the physiological and physical mechanisms of controlling the voice fundamental frequency of speech, which is an important parameter for expressing information on tone, accent, and intonation. The second work is concerned with the cognitive processes involved in a discrimination test of speech stimuli, which gives rise to the phenomenon of so-called categorical perception. They are meant to illustrate the power of models based on deep understanding and precise formulation of the functions of the mechanisms/processes that underlie observed phenomena. Finally, it also presents the author's view on some models that are yet to be developed.

• Journal of the Korean Society of Earth Science Education
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• v.14 no.1
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• pp.1-20
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• 2021

The purpose of this study was to understand students' representational competence while they engaged in learning in geological field trips with scientific models and modeling(Mt. Gwanak and the Hantan-river were formed). Ten students agreed to participate in this study voluntarily. They were attending the Institute of Gifted Education in the Seoul Metropolitan area. The data were collected for all students' activities during field trips and modeling activities using simultaneous video and voice recording, the interview after classes, written data(note) made by the students. The analysis framework that distinguished levels of representational competence and added the resulting interpretation with the final models in the process of scientific models. Results suggested that representational competence levels varied from one to six. However, students showed relatively low levels of representational competence in outdoor learning environments than indoor learning environments. In other words, it began with a relatively low level of representational competence in outdoor class. Then students developed a higher level of representational competence indoor class. Ultimately, we need to understand students' representational competence implies a tool to explain phenomena in the process of modeling activities.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1096-1107
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• 2019

In this paper, we present an ontology-based approach to labeling influential topics of scientific articles. First, to look for influential topics from scientific article, topic modeling is performed, and then social network analysis is applied to the selected topic models. Abstracts of research papers related to data mining published over the 20 years from 1995 to 2015 are collected and analyzed in this research. Second, to interpret and to explain selected influential topics, the UniDM ontology is constructed from Wikipedia and serves as concept hierarchies of topic models. Our experimental results show that the subjects of data management and queries are identified in the most interrelated topic among other topics, which is followed by that of recommender systems and text mining. Also, the subjects of recommender systems and context-aware systems belong to the most influential topic, and the subject of k-nearest neighbor classifier belongs to the closest topic to other topics. The proposed framework provides a general model for interpreting topics in topic models, which plays an important role in overcoming ambiguous and arbitrary interpretation of topics in topic modeling.