• 한국콘텐츠학회논문지
• /
• 제22권9호
• /
• pp.667-677
• /
• 2022

전문대학생이 인식한 스마트 학습기기의 유용성, 용이성, 사용의도 및 학습 활용의 구조적 관계를 분석하고자 한다. 연구문제는 첫째, 스마트 학습기기의 용이성 및 유용성과 사용의도의 관계, 둘째, 스마트 학습기기의 유용성 및 용이성과 학습 활용의 관계, 셋째, 스마트 학습기기의 유용성 및 용이성과 학습 활용의 관계에서 사용의도의 매개효과를 알아본다. 연구방법은 스마트학습에 참여한 학생 350명을 대상으로 설문을 실시하여 가정검토, 확인적 요인분석, 구조방정식 추정 및 매개분석을 위한 Bootsrapping 등을 실시하였다. 이러한 절차로 분석된 결과는 첫째, 전문대학생이 인식한 스마트 학습기기의 유용성 및 용이성은 사용의도에 영향을 주는 것으로 나타났다. 둘째, 전문대학생이 지각한 스마트 기기의 유용성 및 용이성 인식은 스마트 학습기기 학습 활용에 영향을 주는 것으로 나타났다. 셋째, 전문대학생이 지각한 스마트기기 사용 의도는 유용성과 학습활용의 관계를 매개하는 것으로 나타났고, 용이성과 학습활용의 관계를 매개하는 것으로 나타났다. 시사점은 교수자들이 수업현장에서 전문대학생들에게 스마트기기의 유용함과 용이성을 인지하게 함으로써 스마트 학습기기의 사용 의도를 적절하게 인식하고 활용할 수 있도록 하고, 전문대학생들에게 스마트기기의 유용함과 용이성을 인지하게 하여 수업현장에서 스마트 학습기기의 학습 활용도를 높일 수 있도록 한다. 또한 전문대학생의 스마트 기기의 유용성을 인지할 수 있도록 하고 학습 활용의 확대를 꾀하고자 하는 노력이 필요하다.

• 품질경영학회지
• /
• 제47권4호
• /
• pp.687-700
• /
• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• 한국언어정보학회지:언어와정보
• /
• 제14권2호
• /
• pp.159-181
• /
• 2010

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

• 한국보건간호학회지
• /
• 제30권3호
• /
• pp.405-419
• /
• 2016

Purpose: The purpose of this study was to examine the moderating effects of self-directed learning ability on the relationship between mentoring function and career commitment in registered nurses. Methods: A total of 396 registered nurses from 19 hospitals located in Incheon were included in this study. Collected data were analyzed by confirmatory factor analysis, reliability analysis, descriptive statistics analysis, Pearson product moment correlation and hierarchical regression analysis with SPSS and AMOS 18.0 program. Results: The mentoring function and self-directed learning ability showed to have a positive effect on career commitment. Self-directed learning ability showed to have a moderating effect on the relationship between mentoring function and career commitment. Conclusion: To enhance the level of career commitment perceived by registered nurses, hospital organizations need to implement effective mentoring programs and develop self-directed learning ability in nurses.

• 한국초등과학교육학회지:초등과학교육
• /
• 제31권1호
• /
• pp.125-134
• /
• 2012

Interest has been studied as one of the construct to understand and improve learning in science. While informal science education is getting increasing attention as science education has been extended from formal school science to informal science learning including after-school program or science museum activity, however, little has been studied in comparing to the needs. In this study the authors investigated students' interest about learning science in the context of informal science education. For this the survey tool in the article of Im and Pak (2000) was utilized through modification, and 155 elementary students' responses were analyzed with factor analysis and basic statistics. The factor analysis showed that the students' interest about learning science in the context of informal science education has multi dimensions like subject, motivation, and activity dimension. The result showed that students' interest decreased as their grade is higher, and that the interest of intrinsic motivation, empirical activity, and descriptive subject were relatively high while the interest of extrinsic motivation, cognitive activity, and specific subjects were low. From this study the authors could infer the necessity of instructional strategy in consideration of students' interest for more effective science learning in informal science education environment.

• 한국수학교육학회지시리즈A:수학교육
• /
• 제49권4호
• /
• pp.501-522
• /
• 2010

In this study, 'Instruments of the achievement attribution in mathematical learning' was develop to investigate the reasons of mathematical learning achievement by reflecting Korean middle school and high school students' psychological characters and learning context in mathematical learning. To develop the appropriate items for the achievement attribution in mathematical learning, after reviewing attribution literature thoroughly, first version of the instrument was developed and Exploratory Factor Analysis and Confirmatory Factor Analysis were conducted. Then, to reduce the effect of the gender difference and achievement level difference, Differential Item Functioning was performed. Also, using Multiple group Confirmatory Factor Analysis, this instrument was investigated to see whether this can be used for both middle school and high school. The final items for success attribution are 3 items for luck, 3 items for effort, 2 items for ability. The failure attribution were composed of 3 items for luck, 3 items for effort, 2 items for ability, and 2 items for other. The instrument was developed by using large samples and psychometric analysis. Therefore, mathematic teachers can use this instrument efficiently to make a foundation for better learning environment so students' cognitive area and affective area can be harmonized.

• International Journal of Computer Science & Network Security
• /
• 제21권8호
• /
• pp.238-246
• /
• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Structural Engineering and Mechanics
• /
• 제83권2호
• /
• pp.167-178
• /
• 2022

Surrogate models aim to approximate the performance function with an active-learning design of experiments (DoE) to obtain a sufficiently accurate prediction of the performance function's sign for an inexpensive computational demand in reliability analysis. Nevertheless, many existing active-learning methods are limited to the Kriging model, while the uncertainties of the Kriging itself affect the reliability analysis results. Moreover, the existing general active-learning methods may not achieve a fully satisfactory balance between accuracy and efficiency. Therefore, a novel active-learning method GLM-CM is constructed to yield the issues, which conciliates several merits of existing methods. To demonstrate the performance of the proposed method, four examples, concerning both mathematical and engineering problems, were selected. By benchmarking obtained results with literature findings, various surrogate models combined with the proposed method not only provide an accurate reliability evaluation while highly alleviating the computational burden, but also provides a satisfactory balance between accuracy and efficiency compared to the other reliability methods.

• 스마트미디어저널
• /
• 제13권6호
• /
• pp.35-43
• /
• 2024

During the post-pandemic period, the interest in foreign language learning surged, leading to increased usage of language-learning apps. With the rising demand for these apps, analyzing app reviews becomes essential, as they provide valuable insights into user experiences and suggestions for improvement. This research focuses on extracting insights into users' opinions, sentiments, and overall satisfaction from reviews of HelloTalk, one of the most renowned language-learning apps. We employed topic modeling and emotion analysis approaches to analyze reviews collected from the Google Play Store. Several experiments were conducted to evaluate the performance of sentiment classification models with different settings. In addition, we identified dominant emotions and topics within the app reviews using feature importance analysis. The experimental results show that the Random Forest model with topics and emotions outperforms other approaches in accuracy, recall, and F1 score. The findings reveal that topics emphasizing language learning and community interactions, as well as the use of language learning tools and the learning experience, are prominent. Moreover, the emotions of 'admiration' and 'annoyance' emerge as significant factors across all models. This research highlights that incorporating emotion scores into the model and utilizing a broader range of emotion labels enhances model performance.

• International Journal of Advanced Culture Technology
• /
• 제9권4호
• /
• pp.9-17
• /
• 2021

This is a descriptive investigative study which attempts to confirm the mediating effect of learning motivation in the relationship between self-directed learning, learning commitment, and learning persistence intention of university students in an online learning environment. The questionnaires were randomly distributed online and the agreed questionnaires were retrieved, with a total of 338 copies used for analysis. The following is the summary of the findings. First, there were significant differences in learning persistence intention according to general characteristics depending on age, major, part-time job, and academic level. Second, the results showed a positive correlation between self-directed learning, learning commitment, learning motivation, and learning persistence intentions of the subjects were statistically significant. Third, after checking the mediating effect of learning motivation in relation to self-directed learning, learning commitment and learning motivation, the learning motivation has a partial mediating effect on learning and 23% explanatory power, and the learning commitment was found to have a complete mediating effect on the impact of learning motivation on learning intentions with 21% explanatory power. Based on these results, it is necessary to provide a more diverse educational environment, such as operating a motivation semester program that can improve learning motivations along with learning commitment, and the use of a variety of contents that can focus the learner's interest or attention.