• Journal of Convergence for Information Technology
• /
• v.11 no.1
• /
• pp.167-174
• /
• 2021

The purpose of this study is to examine the effect of computational thinking course on learning flow and collaborative self-efficacy of university students and to explore the effectiveness of computational thinking course as liberal arts education. For this study, 177 freshmen at a university in Chungnam Province were surveyed learning flow and collaborative self-efficiency. The collected data were analyzed with Stata IC 14 for ��2 test and t-test. The results of this study are as follows. First, the learning flow of students who took computational thinking course were significantly higher(t=3.837 p<.001) compared to those who did not. Second, the collective self-efficiency of students who took computational thinking course were significantly higher(t=2.277 p<.01) compared to those who did not. Therefore the results show that computational course has positive effects on learning flow and collaborative self-efficiency of university students. Based on these findings, discussions and implications were presented on the importance and effectiveness of computational thinking course.

• The Journal of the Korea Contents Association
• /
• v.16 no.9
• /
• pp.211-224
• /
• 2016

The purpose of this study were to devise prerequisite learning reminding model to elementary mathematics classes and actually apply it to fifth graders in experimental lessons, thus investigating their effects on mathematics academic achievement and self-efficacy. the study conducted a pre and post test to measure academic achievement and self-efficacy on the experiment and control group. the finding were as follows. First, the study found significant differences in mathematics academic achievement between the experiment and control group. mathematics lessons based on the prerequisite learning reminding model resulted in no significant differences among the upper and lower level groups. Secondly, the study analyzed the effects of prerequisite learning reminding model on the self-efficacy and found significant differences in self-efficacy between the experiment and control group. While there were no differences in self-confidence and preference for task difficulty among the subarea of self-efficacy, it had positive differences effect on self-regulation efficacy.

• The Journal of the Korea Contents Association
• /
• v.22 no.9
• /
• pp.401-411
• /
• 2022

This study tests a model for the effect of low self-control on cyber crime and its mediating process. Intervening variables include smartphone addiction, social learning and opportunity variables. Analyzing a data from college students in Seoul, it is shown that the effect of low self-control has a significant effect on smartphone addiction. Results also show that smartphone addiction has a positive significant effect on differential association with illegal peers, illegal definition, and illegal opportunity, and in turn cybercrime. In addition, it is found that low self-control has also a direct effect on differential association with illegal peers, illegal definitions, and illegal opportunity without mediating by smartphone addiction. As a result, it is reveal that the effect of low self-control on cybercrime is indirect and its effect is mediated by smartphone addiction, differential association with illegal peers, illegal definitions, and illegal opportunity.

• Journal of Internet Computing and Services
• /
• v.22 no.6
• /
• pp.141-150
• /
• 2021

Based on Artificial Intelligence technology, AI-enabled warfare is expected to become the main issue in the future warfare. Natural language processing technology is a core technology of AI technology, and it can significantly contribute to reducing the information burden of underrstanidng reports, information objects and intelligences written in natural language by commanders and staff. In this paper, we propose a Language model-based Multi-source Information Integration (LAMII) framework to reduce the information overload of commanders and support rapid decision-making. The proposed LAMII framework consists of the key steps of representation learning based on language models in self-supervsied way and document integration using autoencoders. In the first step, representation learning that can identify the similar relationship between two heterogeneous sentences is performed using the self-supervised learning technique. In the second step, using the learned model, documents that implies similar contents or topics from multiple sources are found and integrated. At this time, the autoencoder is used to measure the information redundancy of the sentences in order to remove the duplicate sentences. In order to prove the superiority of this paper, we conducted comparison experiments using the language models and the benchmark sets used to evaluate their performance. As a result of the experiment, it was demonstrated that the proposed LAMII framework can effectively predict the similar relationship between heterogeneous sentence compared to other language models.

• The Journal of Korean Association of Computer Education
• /
• v.16 no.4
• /
• pp.55-62
• /
• 2013

Most of the APP market today, it is biased in the field of games and entertainment. In contrast, market-share of the educational APP is very low. This phenomenon is due to two major problems. The first is a decrease in the reuse because of the test of simple pattern. The second is difficult to consider user-level range that was learned previously. In this case it is necessary for students to do additional effort. This paper, propose an educational intelligent educational APP to solve the problems described above and shows implementation results. This system analyzes the stored results that have been saved to determine the area of vulnerability. Time-based Re-validation module helps long-term memory of student. The proposed system in this way directly supports self-directed learning. Therefore, the students can be able to relearn weak area autonomously. It results in improved academic achievement.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.5
• /
• pp.187-197
• /
• 2020

Recently, various methods of text embedding using deep learning algorithms have been proposed. Especially, the way of using pre-trained language model which uses tremendous amount of text data in training is mainly applied for embedding new text data. However, traditional pre-trained language model has some limitations that it is hard to understand unique context of new text data when the text has too many tokens. In this paper, we propose self-supervised learning-based fine tuning method for pre-trained language model to infer vectors of long-text. Also, we applied our method to news articles and classified them into categories and compared classification accuracy with traditional models. As a result, it was confirmed that the vector generated by the proposed model more accurately expresses the inherent characteristics of the document than the vectors generated by the traditional models.

• Journal of the Korea Convergence Society
• /
• v.8 no.10
• /
• pp.75-84
• /
• 2017

The purpose of this study is to identify the relationships among Social maturity(SM), Self-directed learning(SDL) and Self-concept of professional nursing(SCPN), and influencing factors of SCPN of nursing students. The survey was performed on 221 nursing students in two universities. Data were collected using a structured questionnaires and analysed with PASW 22.0. Study findings revealed that SCPN has a significant correlation with SM and SDL. Major satisfaction(${\beta}=.179$), Meaning on nursing(${\beta}=.274$), SM(${\beta}=.118$) and SDL(${\beta}=.211$) about SCPN were significant predictive variables. This variables accounted for 34.4% of the variance in SCPN. The findings indicate the necessity of developing educational programs to enhance nursing students's SM and SDL for increasing positive SCPN.

• Journal of the Korea Convergence Society
• /
• v.11 no.8
• /
• pp.391-400
• /
• 2020

This study was done to compare the effects of learner-led and faculty-led fundamental nursing practice on skill performance, self-directed learning, problem-solving competency, and satisfaction. The learner-led practice was consisted of pre-class open lab and peer supervision. The faculty-led practice was consisted of faculty supervision for a student individually without pre-class open lab. The study was a pre-posttest, non-synchronized, quasi-experimental design between two groups. Data from 134 nursing students in either faculty-led (n=54) or learner-led fundamental nursing practice group (n=80) was collected using a checklist and self-reported questionnaire. Nursing skill performance score and class satisfaction of the learner-led group were significantly higher than those of the faculty-led group. This study showed that learner-led fundamental nursing practice consisting of pre-class open lab and peer supervision was an efficient practice method to improve students' skill performance and satisfaction maintaining their self-directed learning ability and problem-solving competency.

• Journal of Digital Convergence
• /
• v.17 no.5
• /
• pp.407-416
• /
• 2019

This study was to investigate the factors affecting the learning strategies of the students in the Department of Health Science. The data collection was conducted through a self-reporting questionnaire to 373 college students majoring in health and health care at two universities in J.do. and the collected data was analyzed using the SPSS/WIN 23.0 program. The study found that there were significant differences in learning strategies depending on Motive of application and major satisfaction level, and there were significant differences in academic stress with the grade, gender, Motive of application, major satisfaction, and academic self-efficiency, and self-directed learning depending on the grade, motive of application, and major satisfaction level. The results showed that the correlation between learning strategy and academic self-efficacy(r=.478, p<.001) and self-directed learning(r=645, p<.001), academic stress(r=-.193, p<.001). Self-directed learning(${\beta}=0.61$), major satisfaction (satisfaction) (${\beta}=0.31$), and major satisfaction (usually)(${\beta}=0.24$) affect the learning strategy, with 42.6% overall explanation. Based on the results of the study, the search for ways to improve major satisfaction levels and self-directed learning skills could have a positive impact on improving the learning strategy of college students in the health sector.

• The Journal of the Korea Contents Association
• /
• v.9 no.7
• /
• pp.402-410
• /
• 2009

A lot of learning systems are applying self-directed learning to increase learner's learning effect. But most of this methods are hardly applied to fit the construction of learning contents considering learner's characteristics or it was processing the learning course without learner's choice. In this research, we proposed the recommendation method that can support the learning contents as calculate learner's preference contents based on learning history information when learner design the learning course. In the result, we chose test learner group and was able to know to generally increase average score of each learner after test between existing method and proposal one.