• Journal of the Korea Convergence Society
• /
• v.12 no.4
• /
• pp.135-140
• /
• 2021

The present research intends to examine how college students' self-determination affects their beliefs in and achievement in foreign language learning in the current pandemic times. The data under discussion was collected from a survey questionnaire conducted to a group of 107 students attending at a four-year university in Cheonan. With the software SPSS Version 21.0, a set of statistical methods were employed: (i) descriptive statistics along with (ii) correlation analysis and (iii) regression analysis. The current analysis identified a positive correlation between their self-determination and both beliefs Foreign Langage Learning and Learning Outcomes, which means that the former exerts a significant impact on the latter. The results are expected to help educators arrange strategic plans that can enhance collegians' self-determination for their better performance of foreign language learning.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.3
• /
• pp.487-495
• /
• 2021

The purpose of this study is to make it easier, more fun and more convenient to learn a foreign language through the development of an efficient audio contents platform that utilizes each person's native language ability. In order to achieve the goal to produce audio contents centering on the native language used in real life. Contents that are created without much effort in daily life could be used as precious contents for foreign language learners to learn the natural use of the language. Currently, most of the online foreign language learning platforms have problems with the contents depletion and the low practicality of contents. Accordingly, I am expecting this platform improves the existing shortcomings, giving foreign language learners the opportunity to learn a foreign language more realistically and at the same time giving native speakers an opportunity to generate additional revenue by utilizing their spare time.

• The Journal of Korean Association of Computer Education
• /
• v.23 no.4
• /
• pp.23-33
• /
• 2020

Online judge provides compilation, execution, and immediate feedback on the source submitted by the learner, and ensures the accuracy and reliability of the evaluation, but it's difficult to select the language according to the level of the learner because most of them provide only textual language. In this study, a block language for online judge was developed and applied to high school classes, and the difference in self-efficacy between the block language and the textual language group was confirmed. It was found that Block language group have more ability expectation to overcome disgust experience than textual language group and Textual language group have significant decrease in ability expectation to start activity and to continue activity. It implies that Block language has an effect on self-efficacy for afterward programming activities, and methods of teaching, learning and evaluation should be devised in the case of textual language so that student's self-efficacy does not deteriorate at the initial and ongoing stage of activity. The results of this study are meaningful in that it provide various implications of methods for enhancing self-efficacy in high school class of programming.

• Journal of Creative Information Culture
• /
• v.7 no.2
• /
• pp.91-98
• /
• 2021

As the amount of time that elementary school students spend online increased due to Corona 19, the amount of posts, comments, and chats they write increased, and problems such as offending others' feelings or using swear words are occurring. Netiquette is being educated in elementary school, but training time is insufficient. In addition, it is difficult to expect changes in student behavior. So, technical support through natural language processing is needed. In this study, an experiment was conducted to automatically filter profanity sentences by applying them to a pre-trained language model on sentences written by elementary school students. In the experiment, chat details of elementary school 4-6 graders were collected on an online learning platform, and general sentences and profanity sentences were trained through a pre-learned language model. As a result of the experiment, as a result of classifying profanity sentences, it was analyzed that the precision was 75%. It has been shown that if the learning data is sufficiently supplemented, it can be sufficiently applied to the online platform used by elementary school students.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.4
• /
• pp.1738-1764
• /
• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• International Journal of Computer Science & Network Security
• /
• v.21 no.11
• /
• pp.97-104
• /
• 2021

The authors provide characteristics of the open educational platforms, classification and quantitative analysis regarding the availability of IT courses, teaching language, thematic directions on the following platforms: Coursera, EdX, Udemy, MIT Open Course Ware, OpenLearn, Intuit, Prometheus, UoPeople, Open Learning Initiative, Open University of Maidan (OUM). The quantitative analysis results are structured and visualized by tables and diagrams. The authors propose to use open educational resources (teaching, learning or research materials that are in the public domain or released with an intellectual property license that allows free use, adaptation, and distribution) for organization of independent work; for organization of distance or correspondence training; for professional development of teachers; for possibility and expediency of author's methods dissemination in the development of their own courses and promoting them on open platforms. Post-project activities are considered in comparing the courses content of one thematic direction, as well as studying the experience of their attending on different platforms.

• Journal of Information Science Theory and Practice
• /
• v.12 no.1
• /
• pp.39-59
• /
• 2024

An online social network is a platform that is continuously expanding, which enables groups of people to share their views and communicate with one another using the Internet. The social relations among members of the public are significantly improved because of this gesture. Despite these advantages and opportunities, criminals are continuing to broaden their attempts to exploit people by making use of techniques and approaches designed to undermine and exploit their victims for criminal activities. The field of digital forensics, on the other hand, has made significant progress in reducing the impact of this risk. Even though most of these digital forensic investigation techniques are carried out manually, most of these methods are not usually appropriate for use with online social networks due to their complexity, growth in data volumes, and technical issues that are present in these environments. In both civil and criminal cases, including sexual harassment, intellectual property theft, cyberstalking, online terrorism, and cyberbullying, forensic investigations on social media platforms have become more crucial. This study explores the use of machine learning techniques for addressing criminal incidents on social media platforms, particularly during forensic investigations. In addition, it outlines some of the difficulties encountered by forensic investigators while investigating crimes on social networking sites.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.334-342
• /
• 2022

Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.

• Language and Information
• /
• v.14 no.2
• /
• 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.

• English Language & Literature Teaching
• /
• v.17 no.4
• /
• pp.75-95
• /
• 2011

The purpose of this study was to evaluate a group activity, reciprocal peer tutoring (RPT), in order to investigate advantages and challenges of RPT in promoting cooperative and collaborative learning environment for EFL University reading instruction. The participants in this study were 89 students taking an English reading course at a Korean university. RPT is a learning strategy whereby learners help each other and learn by teaching. This program was supported by a Vygotskyan perspective which assumes that learners gain mastery and develop cognitive skills through social interaction with more proficient others and their environment. This study relied particularly on participant perceptions through questionnaire survey and Anonymous Online class Report of the course. This study showed various advantages for tutors such as learning through teaching and becoming more autonomous and responsible for their own learning. Non-threatening and highly motivating learning atmosphere are parts of benefits for tutees. Other advantages for tutees included improved level of academic self-confidence, and motivation. This study also revealed several drawbacks associated with the problem of inaccuracy in students' production and students' demand for more direct teacher role. (182 words).