• 대한공업교육학회지
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• v.42 no.2
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• pp.89-107
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• 2017

The purpose of this study was to analyze the perception of academic difficulties of middle school students and their influences variables in technology subject. The subjects of this study were middle school students in the whole country. A total of 420 students were selected considering the grade and area for the survey. The data were collected by mail and analyzed using SPSS 22.0K. The results of this study were as follows : First, The academic difficulties perceived by middle school students in the technology subject were recognized as normal. Second, Middle school students perceived it as a lack of experience (opportunity) in the technology domain as a cause of academic difficulties. Third, In the learning process, the female students were more difficult to understand the related terms and theories than the male students. In the problem solving activities, the female students were more difficult than the male students in identifying problems, designing solutions, selecting solutions, modeling, testing solutions, and correcting improvements. Fourth, the academic difficulty in learning technology lesson was difficult in terms of terminology, theory, and practice activities in the order of Second, Third, and 1st grade. Fifth, lack of understanding knowledge, confusion of contents knowledge, lack of technical interest, shortage of class hours, lack of understanding of evaluation criteria were all influenced by the difficulty of technical subject learning. Sixth, the academic difficulty influencing the problem solving process was explained by understanding comprehension, content knowledge confusion, technical interest, class time, understanding of evaluation criteria.

• Journal of Korea Game Society
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• v.15 no.3
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• pp.151-160
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• 2015

This paper presents English vocabulary memorization system using forgetting curve to automatically adjust the vocabulary difficulty to match learner's level. Our system will decide the appropriate repetition cycle, depending on the number of memorizing words through the forgetting curve, then requires an iterative learning. No matter what learners know or do not know, words are reviewed. To save time by reviewing some words which have the highest probability that learners forget. And it provides vocabulary based on learner level, which makes learner maintain their interest and achievement. A general system provides vocabularies which difficulty matches with evaluated ones, or randomly provides some vocabularies without consideration of users' level. But we apply the "Glicko" system which is being used in the online chess game ranking system to adjust the vocabulary's difficulty. We utilize the system used in the one-by-one player system to our vocabulary-human system. As a result, learners's level and the vocabularies's difficulty is measured in the review process. Moreover it maximizes the performance of English vocabulary memorization by applying feedbacks from practice testing and distributed learning.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.3
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• pp.643-660
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• 2013

The numbers of learning underachievers in college are gradually increasing. As a result, the need for extracurricular programs to increase learning in college is also growing. The purpose of this study was to analyze factors of learning difficulty and develop a model of learning consulting for college underachievers. This study also aimed to evaluate this model's validity. Using both 56 subscription forms of college underachievers and three sets of focus group interviews at B university, we found that students had difficulties in goal and career setting, management of grades and tests, learning methods, time management, failure overcome ability, lack of learning habit sustaining power and learning motivation, and so on. We developed a model of learning consulting for college underachievers based on these factors and applied the model to evaluate it's validity, testing it on 31 underachievers currently enrolled in college, five times every week. Let we say in conclusion that this model of learning consulting had positive effects on changing college underachiever's character, emotion, motivation, and behavior towards learning.

• Journal of Digital Convergence
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• v.13 no.8
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• pp.1-8
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• 2015

This paper focuses on analyzing Korean language learning effect through the STEAM class using a robot which is targeted on multicultural elementary school students. For the purpose of it, the degree of difficulty and interest of how students feel has been measured. By using the programing tool of Korean language entering base, they learn the programming commands like as variable, data type, branching statement, loop statement, etc in Korean, the effect of Korean learning has been measured. It has been examined two interviews at the beginning and the end of the second semester to measure the effect of Korean language learning. As a result of this research, It can be realized that multicultural children who have similar linguistic characteristics and cultural sphere understood Korean language easily when they take the Korean language class by utilizing a robot, and the class had an effect on the acquisition of Korean language for multicultural children.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.3
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• pp.747-756
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• 2019

Students studying interpretation perform additional study and training in addition to regular class. In simultaneous interpreting and consecutive interpreting, interpreter should memorize speaker's announcement because of different language structure. To improve short-term memory, students perform memory training that requires a pair of students. Therefore, they can not perform self-learning, and therefore, efficiency of studying decreases. To resolve this problem, computer based short-term memory training system has been proposed. Student can perform self-learning by changing words in text to special character in the training system. However, efficiency of studying decreases because computer has low portability. Since the number of words is larger than the number of words to be switched into special character, learning difficulty decreases. To resolve this problem, smart device based short-term memory training system has been proposed. Student can perform smart device based training system without space constraints. Since the proposed training system increases the number of words to be changed into special character, learning difficulty increases. We implemented and evaluated the functionalities of the proposed training system.

• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• Educational Technology International
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• v.20 no.2
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• pp.137-168
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• 2019

This study examined the effects of information volume and distribution on learners' cognitive load and recall in a mobile augmented reality (AR) environment. Information volume refers to the degree of information users are provided in a learning task, while information distribution indicates the way in which information is distributed, either in a virtual or real format. Sixteen undergraduate students participated in the study, which employed a 2 × 3 randomized block factorial design with repeated measures. Information volume and distribution were independent variables, and factors in learners' cognitive load (mental effort, perceived ease of use, and perceived task difficulty) and recall test scores were the dependent variables. Information volume had significant main effects on perceived ease of use and task difficulty, and recall test scores, while information distribution had significant main effects on perceived task difficulty and test scores. A detailed discussion and implications are provided.

• Journal of Fisheries and Marine Sciences Education
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• v.28 no.4
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• pp.971-983
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• 2016

This study is a preliminary research to develop an individual learning coaching program for college students who received academic probation. It aims to test applicability as a program in the future, and t revise it and make up for its problems. To achieve the research aim, this study, based on self-directed learning, constructed the program dividing it into two kinds: the first basic learning special lecture and the second individual learning coaching. This program used those students in universities in Gyeongbok province who received two or more scholastic probations from the second semester of 2012 to the second semester of 2014, and who were judged by professors who had interviewed them as those who needed learning coach. The researcher of this study introduced those students about the program and schedule. And, this study did in-depth analysis for the six students who attended the first and second parts of the program. It was found that those students understood the importance and necessity of self-directed learning, and tried to apply it to actual learning process and overcome learning difficulty. Based on such findings, this study suggested the development of a program for students who receive academic probation and ideas on how to develop it.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.549-557
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• 2001

Morphological analysis and POS tagging require a dictionary for the language at hand . In this fashion though it is impossible to analyze a language a dictionary. We also have difficulty if significant portion of the vocabulary is new or unknown . This paper explores the possibility of learning morphology of an agglutinative language. in particular Korean language, without any prior lexical knowledge of the language. We use unsupervised learning in that there is no instructor to guide the outcome of the learner, nor any tagged corpus. Here are the main characteristics of the approach: First. we use only raw corpus without any tags attached or any dictionary. Second, unlike many heuristics that are theoretically ungrounded, this method is based on statistical methods , which are widely accepted. The method is currently applied only to Korean language but since it is essentially language-neutral it can easily be adapted to other agglutinative languages.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.1-13
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• 2018

Deep learning (DL) models have been widely applied to AI applications such image recognition and language translation with big data. Recently, DL models have becomes larger and more complicated, and have merged together. For the accelerated training of a large-scale deep learning model, model parallelism that partitions the model parameters for non-shared parallel access and updates across multiple machines was provided by a few distributed deep learning frameworks. Model parallelism as a training acceleration method, however, is not as commonly used as data parallelism owing to the difficulty of efficient model parallelism. This paper provides a comprehensive survey of the state of the art in model parallelism by comparing the implementation technologies in several deep learning frameworks that support model parallelism, and suggests a future research directions for improving model parallelism technology.