• Proceedings of the KSPS conference
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• 1996.10a
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• pp.275-276
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• 1996

Koreans learning Mandarin Chinese are faced with serious pronunciation errors in vowels, consonants and tones, etc. Most of these pronunciation errors are found to be due the transference of the native Korean phonetic habits. Following are some of the most common pronunciation errors encountered by Koreans learning Chinese.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.281-285
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• 2005

Learning and evolving are two basics for data mining. As compared with classical learning theory based on objective function with minimizing training errors, the recently evolutionary computing has had an efficient approach for constructing optimal model without the minimizing training errors. The global search of evolutionary computing in solution space can settle the local optima problems of learning models. In this research, combining co-evolving algorithm into statistical learning theory, we propose an co-evolutionary computing for statistical learning theory for overcoming local optima problems of statistical learning theory. We apply proposed model to classification and prediction problems of the learning. In the experimental results, we verify the improved performance of our model using the data sets from UCI machine learning repository and KDD Cup 2000.

• Journal of Educational Research in Mathematics
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• v.14 no.3
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• pp.239-266
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• 2004

This article presents new perspectives for analysing and diagnosing students' mathematical errors on the basis of Pascaul-Leone's neo-Piagetian theory. Although Pascaul-Leone's theory is a cognitive developmental theory, its psychological mechanism gives us new insights on mathematical errors. We analyze mathematical errors in the domain of proof problem solving comparing Pascaul-Leone's psychological mechanism with mathematical errors and diagnose misleading factors using Schoenfeld's levels of analysis and structure and fuzzy cognitive map(FCM). FCM can present with cause and effect among preconceptions or misconceptions that students have about prerequisite proof knowledge and problem solving. Conclusions could be summarized as follows: 1) Students' mathematical errors on proof problem solving and LC learning structures have the same nature. 2) Structures in items of students' mathematical errors and misleading factor structures in cognitive tasks affect mental processes with the same activation mechanism. 3) LC learning structures were activated preferentially in knowledge structures by F operator. With the same activation mechanism, the process students' mathematical errors were activated firstly among conceptions could be explained.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.247-253
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• 2021

There are various programming errors that occur in the course of programming practice for beginners in computer programming. At this time, since it is difficult for learners to recognize errors by themselves, they correct program errors through the instructor's feedback. However, as students learn programming techniques in an online learning environment due to the COVID-19 pandemic, there is a limit to interaction between the students and the instructor in comparison with offline classes, so it is necessary for learners to develop their own ability to solve programming errors by themselves. Therefore, in this study, error cases in online programming classes using the Python language are analyzed and an online programming education method that can improve learners' ability to correct programming errors is proposed based on the analysis results.

• Speech Sciences
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• v.14 no.3
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• pp.31-46
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• 2007

This study attempts to investigate that the training of pitch manipulation would help Korean speakers reduce the intonation errors based on the review of many previous studies on Korean speakers' phonetic realization of intonation. The previous studies have indicated that Korean speakers have problems with pitch manipulation in their production of English word stress, sentence stress, and eventually intonation. To train Korean speakers phonetically realize English pitch patterns, a blended learning module was operated for two weeks: face-to-face instruction for six hours and e-learning instruction for three hours in total. This module was designed to help Korean speakers realize pitch as a distinctive phoneme. An acoustic assessment on five Korean female English speakers shows that the training of pitch manipulation helps Korean English speakers reduce the intonation errors indicated in the previous studies reviewed.

• School Mathematics
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• v.11 no.4
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• pp.643-664
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• 2009

This study was aimed to understand the problems that students experience during the self--directed study of a mathematics digital textbook and to find the implications for the design of digital textbook. For this study, we analyzed the process of self-directed learning on 'division of fractions with same denominator' using digital textbook by eight 6th graders. Students asked to use think aloud method while they study the unit. Their learning process was videotaped and analyzed by researchers after the experiment. After the self-directed learning, students filled out a test items and participated interview with a researcher. The result showed that students experienced several misconceptions and errors while using a digital textbook. The types of misconceptions and errors were cataegorized as "misconceptions and errors caused by a mathematics textbook" and "misconceptions and errors caused by a digital textbook". Especially, students showed several important misconceptions and errors because of the design factors. This implies we need to consider the causes of misconceptions for the design of a digital textbook.

• English Language & Literature Teaching
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• v.8 no.1
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• pp.39-53
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• 2002

Recently much research has investigated the role of attention in L2 learning, comparing the effects of explicit learning with those of implicit learning. With this background the research aims at examining the effects explicit focus on form has on L2 learning based on the acquisition of the English article system. The participants were 70 Korean college students who enrolled in English Composition classes. The experimental group received explicit focus on form including grammatical explanation, input enhancement, output practice, and negative evidence (corrective feedback) for two weeks, while the control group was exposed to sufficient input and negative evidence. Completion tasks were administered at the beginning and the end of the semester. In addition, errors in the use of English articles were analysed on their compositions both before and after the different treatments. The analyses of the results show that the explicit focus on form group improved significantly more than the control group, particularly for the definite article 'the', and some changes occurred in the distribution of article errors. These findings suggest that explicit teaching plays a more contributory role than implicit teaching in acquiring L2 knowledge in classroom-based L2 learning.

• Journal of Semiconductor Engineering
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• v.1 no.1
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• pp.31-37
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• 2020

We present an approximate DRAM architecture for energy-efficient deep learning. Our key premise is that by bounding memory errors to non-critical information, we can significantly reduce DRAM refresh energy without compromising recognition accuracy of deep neural networks. To validate the key premise, we make extensive Monte-Carlo simulations for several well-known convolutional neural networks such as LeNet, ConvNet and AlexNet with the input of MINIST, CIFAR-10, and ImageNet, respectively. We assume that the highest-order 8-bits (in single precision) and 4-bits (in half precision) are protected from retention errors under the proposed architecture and then, randomly inject bit-errors to unprotected bits with various bit-error-rates. Here, recognition accuracies of the above convolutional neural networks are successfully maintained up to the 10^-5-order bit-error-rate. We simulate DRAM energy during inference of the above convolutional neural networks, where the proposed architecture shows the possibility of considerable energy saving up to 10 ~ 37.5% of total DRAM energy.

• Asia Pacific Journal of Corpus Research
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• v.4 no.1
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• pp.25-42
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• 2023

The number of studies on data-driven learning (DDL) has increased in recent years, and DDL's overall effectiveness as an L2 (second language) teaching methodology has been reported to be high. However, the degree of its effectiveness in grammar instruction, particularly for the goal of correcting errors in L2 writing, is still unclear. To provide guidelines for focused grammar instruction with DDL in the Japanese classroom setting, we aimed to identify the typical grammatical errors made by Japanese learners in the Cambridge Learner Corpus First Certificate in English (CLC FCE) dataset. The results revealed that three error types (nouns, articles, and prepositions) should be addressed in DDL grammar instruction for Japanese English as a foreign language (EFL) learners. In light of the findings, pedagogical implications and suggestions for future DDL research and practice are discussed.

• Journal of The Korean Association of Information Education
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• v.22 no.2
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• pp.205-212
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• 2018

As a way to improve the software education ability of the pre - service teachers, we conducted programming learning using two types of programming tools (Python and Scratch) at the regular course time. In programming learning, various types of errors, which are factors that continuously hinder interest, achievement and creativity, were collected and analyzed by type. By using the analyzed data, it is possible to improve the ability of pre-service teachers to cope with the errors that can occur in the software education to be taught in the elementary school, and to improve the learning effect. In this study, logic error (37.63%) was the most frequent type that caused the most errors in programming in both conventional language that input text and language that assembles block. In addition, the detailed errors that show a lot of differences in the two languages are the errors of Python (14.3%) and scratch (3.5%) due to insufficient use of grammar and other errors.