• Journal of The Korean Association For Science Education
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• v.19 no.1
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• pp.19-28
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• 1999

The major purposes of this study are to examine the effects of portfolio assessment on elementary school student's science knowledge, inquiry ability, science attitudes and to investigate students' perceptions on portfolio assessment. Control group consists of 45 fourth-graders at M-Elementary school located at Miwon, Chongwon-gun, Chung-buk and experimental group 36 fourth-graders of G-Elementary school located in Daejeon-si. The inventories of scientific knowledge I, inquiry ability, and science attitudes were administered to both groups as a pre-test. The experimental group was given portfolio assessment instruction and control group traditional instruction for about six weeks. Inventories about scientific knowledge 2, inquiry ability, and science attitudes were administered to both groups as a post-test. A questionnaire on the perception on portfolio assessment was given to experimental group after the treatment. The results were statistically analyzed with SPSS. Control group showed higher score on scientific knowledge than that of experimental group (p<0.5). No statistically meaningful difference was identified in inquiry ability and scientific attitude. More in-depth analysis revealed that scientific attitudes were improved statistically meaningfully by portfolio assessment. The students' perceptions on portfolio assessment is very positive. Students have positive responses on interests in portfolio assessment, feelings of involvement in learning, self-regulated learning, higher levels of thinking, intentions of participation in portfolio assessment.

• Journal of The Korean Association For Science Education
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• v.36 no.4
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• pp.539-550
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• 2016

The purpose of the study is to conceptualize SSI-PCK by identifying major components and sub-components to promote science teachers' confidence and knowledge on teaching SSIs. To achieve this, I conducted extensive literature reviews on teachers' perceptions on SSI, case studies of teachers addressing SSIs, SSI instructional strategies, etc. as well as PCK. Results indicate that SSI-PCK include six major components: 1) Orientation for Teaching SSI (OTS), 2) Knowledge of Instructional Strategies for Teaching SSI (KIS), 3) Knowledge of Curriculum (KC), 4) Knowledge of Students' SSI Learning (KSL), 5) Knowledge of Assessment in SSI Learning (KAS), and 6) Knowledge of Learning Contexts (KLC). OTS refers to teachers' instructional goals and intentions for teaching SSIs. Teachers often present a) activity-driven, b) knowledge and higher order thinking skills, c) application of science in everyday life, d) nature of science and technology, e) citizenship and f) activism orientations for teaching SSIs. KIS indicates teachers' instructional knowledge required for effectively designing and implementing SSI lessons. It includes a) SSI lesson design, b) utilizing progressive instructional strategies, and c) constructing collaborative classroom cultures. KC refers to teachers' knowledge on a) connection to science curriculum (horizontal/vertical) and b) connection to other subject matters. KSL refers to teachers' knowledge on a) learner experiences in SSI learning, b) difficulties in SSI learning, and c) SSI reasoning patterns. KAS indicates teachers' knowledge on a) dimensions of SSI learning to assess, and b) methods of assessing SSI learning. Finally, KLC refers to teachers' knowledge on the cultures of a) classrooms, b) schools, and c) community and society where they are located when teaching SSIs.

• Journal of the Korean Applied Science and Technology
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• v.37 no.3
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• pp.473-483
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• 2020

This paper proposes a tourism recommendation system in intelligent cloud environment using information of tourist behavior applied with perceived value. This proposed system applied tourist information and empirical analysis information that reflected the perceptual value of tourists in their behavior to the tourism recommendation system using wide and deep learning technology. This proposal system was applied to the tourism recommendation system by collecting and analyzing various tourist information that can be collected and analyzing the values that tourists were usually aware of and the intentions of people's behavior. It provides empirical information by analyzing and mapping the association of tourism information, perceived value and behavior to tourism platforms in various fields that have been used. In addition, the tourism recommendation system using wide and deep learning technology, which can achieve both memorization and generalization in one model by learning linear model components and neural only components together, and the method of pipeline operation was presented. As a result of applying wide and deep learning model, the recommendation system presented in this paper showed that the app subscription rate on the visiting page of the tourism-related app store increased by 3.9% compared to the control group, and the other 1% group applied a model using only the same variables and only the deep side of the neural network structure, resulting in a 1% increase in subscription rate compared to the model using only the deep side. In addition, by measuring the area (AUC) below the receiver operating characteristic curve for the dataset, offline AUC was also derived that the wide-and-deep learning model was somewhat higher, but more influential in online traffic.

• Journal of Korean Library and Information Science Society
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• v.43 no.4
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• pp.139-159
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• 2012

The purpose of this research is to increase understanding about 'an attitude' and to develop the contents of the reading attitude survey questionnaire through the analysis and comparison of reading attitude models. An attitude has an individual's perception and feeling about events, problems, people or things, and it also includes the state prepared for reaction. An attitude consists of emotion, cognition and behavior and it is formed by experience, learning or value judgment. Reading attitudes are composed of cognitive factors that represent beliefs or opinions about reading, emotional factors that represent evaluation and emotion about reading, and behavioral factors that represent intentions or behavior to reading. The analysis of the components of the reading attitude models shows the fact that the influencing factors of reading attitude formation are the reading experience, beliefs of reading results, beliefs about others' expectations and reading environments. Thus, the contents of reading attitude survey questionnaires should include such contents as reading experience, beliefs of reading results, beliefs about others' expectations, and reading environments.

• Journal of KIISE
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• v.44 no.12
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• pp.1252-1257
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• 2017

In dialogues, speakers' intentions can be represented by sets of an emotion, a speech act, and a predicator. Therefore, dialogue systems should capture and process these implied characteristics of utterances. Many previous studies have considered such determination as independent classification problems, but others have showed them to be associated with each other. In this paper, we propose an integrated model that simultaneously determines emotions, speech acts, and predicators using a convolution neural network. The proposed model consists of a particular abstraction layer, mutually independent informations of these characteristics are abstracted. In the shared abstraction layer, combinations of the independent information is abstracted. During training, errors of emotions, errors of speech acts, and errors of predicators are partially back-propagated through the layers. In the experiments, the proposed integrated model showed better performances (2%p in emotion determination, 11%p in speech act determination, and 3%p in predicator determination) than independent determination models.

• Convergence Security Journal
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• v.11 no.3
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• pp.55-66
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• 2011

This research focus on the value of smart phones for university students in Korea, considering on the correlations between the main causes influencing intrinsic value(price attributes, function attributes), network value(learning effects attributes, externalities attributes) user satisfaction, and intentions of repurchase of the smart phones market in Korea. Through the statistical analyses on the 8 hypotheses from a research model, we found that intrinsic value and network value gave an attentive influence on user satisfaction and repurchase intention. Call charge and Liquid crystal display and Design of smart phone have an influenced user satisfaction and repurchase intention.

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.197-211
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• 2021

The emergence of knowledge-sharing live streamers provides more diversified content to the live streaming platform. Analysis of the factors affecting the intention to use knowledge sharing live streaming users can allow the live streaming platform to understand better the adoption characteristics of users who follow this type of content. Help platform operators provide better services and help live streaming platforms innovate. Based on the TAM model, this research uses questionnaire surveys and structural equation models to construct a conceptual model of the influencing factors of users' intentions in the knowledge sharing live streaming and conduct an empirical analysis on the influencing factor models. The results of data analysis show that a significant influence of users' attitudes of knowledge sharing live streaming is perceived usefulness, followed by flow experience; perceived value has a positive impact on users' attitudes and intention to use, and the positive influence of users attitude significantly affect the user's intention.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.133-142
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• 2021

This study aims to expand the effect of online education into the aspect of active exploration and sharing of class-related issues by learners. Based on theoretical discussions, Two types of motivation (personal and social) to explore issues, engagement, attitude toward issue content, and eWOM model were verified. As a result of the study, it was found that the impact of personal and social motivations that online education has on engagement on specific issues, and the positive(+) influence on attitudes toward issue content and word of mouth intentions on SNS, considering engagement as a parameter. In this study, the role of engagement in inducing the next learning by oneself was confirmed, and it can be seen that social and personal motives for issues and class content should be utilized to increase engagement.

• International Journal of Knowledge Content Development & Technology
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• v.11 no.4
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• pp.7-34
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• 2021

The purpose of the study was to explore the uses and gratification of social media among first-year student groups at a satellite campus of a public university in Ghana. The study employed a descriptive survey design. The study involved all 1061 first-year university students in six academic departments of the College. A total of 680 (64%) participants returned validly completed copies of the questionnaire. Descriptive statistics and thematic analysis were employed for data analysis. The findings indicate that WhatsApp was the most popular application for social media groups, while a need for information-sharing, peer-tutoring and learning, and finding and keeping friends were the primary motivations for joining social media groups. First-year students are involved mainly in reactive activities, as most engage when solving an academic assignment through group discussions. Though challenges persist, such as posting of unwanted images, inadequate participation, and ineffective and irrelevant communication, most are willing to continue their social media groups' membership in the long term. This study provides valuable insight into transitioning students' lived experiences on social media from the group perspective. These insights are valuable conceptually and practically to academic counsellors, librarians and student affairs officers who are expected to provide on-going education on (social) media literacy to first-year students to enhance the adjustment process. The study is the first of its kind in Ghana that investigates social media group participants' exit intentions.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.