• 지식경영연구
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• 제15권3호
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• pp.121-140
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• 2014

Many firms have been utilizing SNSs such as Facebook and Twitter actively in order to boost interactions with customers that promote product and service innovations and effective marketing. Although positive outcomes of the customer interactions in SNSs are expected, there exist few studies on the effects of interactions between customers and firms in the SNS context. This study empirically examines how customer experiences in multi-dimensional interactions (i.e., pragmatic, sociability, usability, and hedonic interaction) in corporate SNSs influence customer trust in the firm, and how customer learning benefits are associated with firm benefits such as gaining customer trust. The results indicate that all four dimensions of customer interactions in SNSs have significant effects on customer learning benefits, which in turn significantly influence customer trust in the firm. Meanwhile, the results reveal that there are also direct relationships between specific dimensions of customer interactions in SNSs and the two dimensions of customer trust (i.e., ability-based and benevolence/integrity-based). Based on the findings, this study diagnoses the status of corporate SNSs in terms of collaboration with customers and provides practical implications for firms which attempt to capitalize on the multi-dimensional customer interactions in SNSs and to facilitate innovative activities with customers.

• 한국과학교육학회지
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• 제23권4호
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• pp.375-385
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• 2003

In an effort to provide information about the effect of Microcomputer-Based Laboratory (MBL) use in science teaching and learning on student achievement and attitudes, a review of research analyzed studies was done between 1981 and 2001, using a meta-analysis procedure. Thirty-seven published and unpublished studies were reviewed. Use of MBL was shown to be potentially effective in the following condition of class; two students, physics teaching, more than one topic, or at the college level. Appropriate research design strategies, financial support (including hardware and software), and the use of more than one instrument for assessing the effect of the MBL instruction are crucial factors for more informative research studies. While helpful in many respects, the prior research revealed a number of problems related to the use of MBL in school science teaching and learning. The prior research does not support the desired intention described in the theory-based outcomes and reveals so little about how teachers and students use MBL, how it influences their teaching and learning, and how effectively it fits into the existing science curriculum. In order to know if the integration of MBL in the existing school science is worth it or not, more careful research design and comprehensive research should be done.

• The Journal of Asian Finance, Economics and Business
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• 제8권12호
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• pp.241-251
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• 2021

This study aims to analyze the impact of technology adoption on students' satisfaction with the higher education system in Vietnam. With the continuous development of information and technology, the education sector in particular and many economic sectors in Vietnam have witnessed an explosion of applications and interventions in teaching-learning. However, these innovations have also received a lot of criticism regarding their effectiveness and feasibility. Although the numerous benefits that technology adoption has brought to education are apparent, many practitioners have not adjusted to this transition, resulting in lower learner satisfaction. Through a survey of more than 2472 university students in Vietnam, the results find a positive relationship between technology adoption and student satisfaction in higher education. We also test how nine contingent factors including gender, income, major, self-study time, learning methods, technology administration, self-ability in adopting technology, technology accessibility, and purpose of using technology can moderate that relationship. Indeed, technology adoption acts as a facilitator to make learning more convenient, effective, and accessible, rather than completely affecting learning outcomes and satisfaction. This result suggests that self-motivation is an important and decisive factor in improving satisfaction through choosing and applying technology effectively and appropriately.

• 의학교육논단
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• 제23권2호
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• pp.104-107
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• 2021

Building professional identity is the most basic purpose of medical education. Students who enter medical schools do not have an identity rooted in the medical profession, and universities should therefore take steps to help students form their identity as doctors, attitudes, beliefs, and values through the curriculum. However, while medical knowledge and clinical skills are fully reflected in basic medical education, issues persist regarding education on values, attitudes, and beliefs that are important for professional identity. Regarding the process of professional identity formation, it is important to keep in mind that rapid changes in modern society lead to corresponding changes in socio-cultural expectations and demands related to professional identity, resulting in discrepancies between the reality of medical education and the actual field of medicine. Medical schools need to prepare students for these discrepancies, and in-depth discussions should address what is important and what should be solved first at medical education sites. However, it is difficult to generalize the tasks of professional identity formation in the field of medical education because each medical school may have unique circumstances. This article discusses the tasks that medical education should solve for professional identity formation education in terms of five aspects: establishing learning outcomes, training educational experts, introducing transformative learning, utilizing self-directed learning, and developing evaluation methods.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제26권4호
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• pp.333-349
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• 2023

Recently, AI has become a crucial tool in mathematics education due to advances in machine learning and deep learning. Considering the importance of AI, examining teachers' beliefs about AI in mathematics education (AIME) is crucial, as these beliefs affect their instruction and student learning experiences. The present study developed a scale to measure preservice teachers' (PST) beliefs about AIME through factor analysis and rigorous reliability and validity analyses. The study analyzed 202 PST's data and developed a scale comprising three factors and 11 items. The first factor gauges PSTs' beliefs regarding their roles in using AI for mathematics education (4 items), the second factor assesses PSTs' beliefs about using AI for mathematics teaching (3 items), and the third factor explores PSTs' beliefs about AI for mathematics learning (4 items). Moreover, the outcomes of confirmatory factor analysis affirm that the three-factor model outperforms other models (a one-factor or a two-factor model). These findings are in line with previous scales examining mathematics teacher beliefs, reinforcing the notion that such beliefs are multifaceted and developed through diverse experiences. Descriptive analysis reveals that overall PSTs exhibit positive beliefs about AIME. However, they show relatively lower levels of beliefs about their roles in using AI for mathematics education. Practical and theoretical implications are discussed.

• 아시아태평양코퍼스연구
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• 제4권2호
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• pp.41-55
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• 2023

In data-driven learning (DDL), learners explore a corpus to understand vocabulary and grammar. Although many studies have emphasized the role of DDL in second language (L2) acquisition, L2 light verbs have been largely under-explored. To bridge this gap, this study focused on the learning outcomes of L2 light verbs among 29 intermediate-level Japanese university students. The research zeroed in on six prevalent light verbs in English: "make," "do," "take," "have," "give," and "get." Over nine weeks, the participants engaged with verb-noun collocations using worksheets that juxtaposed Japanese translations of the target collocations with their English equivalents, with the verbs omitted. With the aid of Wordbanks Online, they filled in the blanks and constructed accurate sentences. Before this activity, a 20-minute tutorial was given to the participants on how to interpret the concordance lines. The effectiveness of the DDL method was evaluated using pre-tests, immediate post-tests, and delayed post-tests. The results showed that DDL significantly improved the participants' knowledge of the target collocations of light verbs and nouns; the post-test and delayed post-test scores were significantly higher than the pre-test scores. The results showed that, overall, DDL contributed to memorizing the collocations of light verbs and nouns; however, DDL had different effects on the memorization of collocations across different light verbs. The extent of work on the worksheet is not the only factor in its retention, and observing concordance lines may promote learners' memorization of light-verb collocations.

• 한국공간구조학회논문집
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• 제23권4호
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• 한국과학교육학회지
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• 제28권8호
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• pp.786-795
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• 2008

The purpose of this research is to propose a program for teaching and learning effective problem-solving for gifted students based on abductive inference. The role of abductive inference is important for scientific discoveries and creative inferences in problem-solving processes. The characteristics of creativity and abductive inference were investigated, and the following were discussed: (a) a suggestion for a new program based on abductive inference for creative outcomes, this program largely consists of two phases: generative hypotheses and confirmative hypotheses, (b) a survey of the validity of a program. It is typical that hypotheses are confirmed in phases through experiments based on hypothetic deductive methodology. However, because generative hypotheses of this hypothetic deductive methodology are not manifest, we maintained that abductive inference strategies must be used in a Creative Teaching-learning Program for gifted science students.

• Design & Manufacturing
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• 제18권2호
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• 대한간호학회지
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• 제45권6호
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• pp.939-948
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• 2015

Purpose: An emerging trend in Asian higher education is English-medium instruction (EMI), which uses English as the primary instructional language. EMI prepares domestic students for international leadership; however, students report difficulty in learning, and educators have raised questions concerning the effectiveness of EMI. The flipped learning model (FLM), in which lecture and homework activities for a course are reversed, was applied to an English-medium course offered by a college of nursing in Korea. The aims of this study were to: 1) revise an existing English-medium nursing course using the FLM; 2) explore students' learning experiences and their acceptance of the FLM; and 3) identify key factors in the success of FLM. Methods: We used a descriptive, cross-sectional, mixed-methods design and the participants were students at one nursing school in Korea. A series of course development meetings with faculties from the nursing school and the center for teaching and learning were used to develop the course format and content. We conducted course evaluations using the Flipped Course Evaluation Questionnaire with open-ended questions and focus group interviews. Results: Students (N=75) in a 15-week nursing course responded to a survey after completing the course. Among them, seven students participated in one of two focus groups. Overall, students accepted and favored the flipped learning strategy, and indicated that the method enhanced lecture content and their understanding of it. Factors associated with effective instruction included structured monitoring systems and motivational environments. Conclusion: The FLM requires sufficient preparation to facilitate student motivation and maximize learning outcomes.