• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.77-86
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• 2024

This study analyzed students' discourse and learning to investigate the impact of using Google Docs in science classes. The researcher, who is also a science teacher, conducted classes for 49 second-year middle school students. The classes included one using Google Docs and another using traditional paper worksheets covering identical content. Students' discourse collected from each class was compared and analyzed using Epistemic Network Analysis (ENA). The findings indicated that in the class using Google Docs, the proportion of discourse related to task was higher compared to the traditional class. More specifically, discourse regarding taking and uploading photos was prominent. However, such discourse did not lead to peer learning as intended by the teacher. An analysis based on achievement levels revealed that the class utilizing Google Docs had a relatively higher proportion of discourse from lower-achieving students. Additionally, differences were observed in the types of utterances and connection structures between the higher and lower-achieving students. The higher-achieving students took a leading role in providing suggestions and explanations, while the lower-achieving students played a role in transcribing them, with this tendency being more pronounced in the class using Google Docs. Lastly, students' changes in perception regarding the cause of static electricity were visualized using ENA. Based on the research findings, this study proposes strategies to enhance collaborative learning using Google Docs, including the use of open-ended problems to allow diverse opinions and outputs, and exploring the potential use of ENA to assess the learning effects of conceptual learning.

• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.225-236
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• 2023

This study investigated the characteristics of pre-service elementary teachers' TPACK in science lesson planning using VR/AR content based on epistemic network analysis (ENA). Seven TPACK coding elements were derived inductively based on the existing TPACK framework. Then, the pre-service elementary teachers' discourse in science lesson planning was coded according to the seven TPACK coding elements and analyzed using the ENA Web Tool. The discourses of the two groups were analyzed and compared, and the differences between the two groups, which the researchers analyzed qualitatively, were clearly shown on the ENA graph. Based on these findings, the researchers argued that the ENA method is a useful research tool for analyzing the complex interactions of technology knowledge (TK), content knowledge (CK), and pedagogical knowledge (PK), which is different from previous TPACK research. Also, the researchers discussed the implications for the TPACK competency development of pre-service teachers by comparing the characteristics of the two groups' discourse.

• Asia Pacific Journal of Corpus Research
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• v.2 no.1
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• pp.23-34
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• 2021

This research examines Japanese non-native speakers' (JNNS) modal auxiliary verb use from two different perspectives: frequency of use and preferences for modalities. Additionally, error analysis is carried out to identify errors in modal use common among JNNSs. Their modal use is compared to that of English native speakers within a spoken dialogue corpus which is part of the International Corpus Network of Asian Learners' English. Research findings show at a statistically significant level that when compared to native speakers, JNNSs underuse past forms of modals and infrequently convey epistemic modality, indicating the possibility that JNNSs fail to express their opinions or thoughts indirectly when needed or to convey politeness appropriately. Error analysis identifies the following three types of common errors: (1) the use of incorrect tenses of modal verb phrases, (2) the use of inflected verb forms after modals, and (3) the non-use of main verbs after modals. The first type of error is largely because JNNSs do not master how to express past meanings of modals. The second and third types of errors seem to be due to first language transfer into second language acquisition and JNNSs' overgeneralization of the subject-verb agreement rules to modals respectively.

• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.351-368
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• 2021

This paper, by applying a reliability-based framework, develops seismic vulnerability macrozonation maps for Tehran, the capital and one of the most earthquake-vulnerable city of Iran. Seismic performance assessment of 3-, 4- and 5-story steel moment resisting frames (SMRFs), designed according to ASCE/SEI 41-17 and Iranian Code of Practice for Seismic Resistant Design of Buildings (2800 Standard), is investigated in terms of overall maximum inter-story drift ratio (MIDR) and unit repair cost ratio which is hereafter known as "damage ratio". To this end, Tehran city is first meshed into a network of 66 points to numerically locate low- to mid-rise SMRFs. Active faults around Tehran are next modeled explicitly. Two different combination of faults, based on available seismological data, are then developed to explore the impact of choosing a proper seismic scenario. In addition, soil effect is exclusively addressed. After building analytical models, reliability methods in combination with structure-specific probabilistic models are applied to predict demand and damage ratio of structures in a cost-effective paradigm. Due to capability of proposed methodology incorporating both aleatory and epistemic uncertainties explicitly, this framework which is centered on the regional demand and damage ratio estimation via structure-specific characteristics can efficiently pave the way for decision makers to find the most vulnerable area in a regional scale. This technical basis can also be adapted to any other structures which the demand and/or damage ratio prediction models are developed.