• 한국초등과학교육학회지:초등과학교육
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• 제34권2호
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• pp.183-193
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• 2015

Teacher guidebooks are practical and commonly used resources for teachers to deliver the goals and contents of science curriculum in classroom teaching. Thus, the alignment of teacher guidebooks and science curriculum could be critical to undertake the effectiveness of curriculum implication in science classrooms. This study is to investigate how the learning objectives of science curriculum are implicated in teacher guidebooks by analyzing the dimensions of knowledge and cognitive process in learning objectives in both documents. Grade 3~4 learning objectives (82 objectives in the curriculum, 459 in the teacher guidebook, 541 in total) in 2009 Revised science curriculum and teacher guidebooks were coded and analyzed based on the Revised Bloom's Taxonomy. The analysis focused on how the knowledge dimensions and cognitive processes of the curriculum were emphasized and restructured in the teacher guidebooks to examine the coalition between the two important documents. The study found: 1) the learning objectives in Grade 3~4 in both documents were skewed to certain knowledge dimension (conceptual) and cognitive process (understand); 2) there was a high coalition between unit objectives and lesson objectives in the teacher guidebooks, however, relatively low coalition between the curriculum and the teacher guidebooks; and 3) learning objectives in the curriculum were delivered in teacher guidebooks in various patterns (similar, detailed, additional, in portion, and the same), and 'detailed' and 'additional' were frequently shown. There also appeared new objectives in the teacher guidebooks, which were not present in the curriculum. The findings in this study could provide some suggestions to the current project of developing 2015 Science Curriculum in regard to understanding the dimensions of knowledge and cognitive process of learning objectives and their alignments with textbooks and teacher guidebooks.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.285-292
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• 2023

With the wide spread of smart farms and the advancements in IoT technology, it is easy to obtain additional data in addition to crop images. Consequently, deep learning-based crop disease diagnosis research utilizing multimodal data has become important. This study proposes a crop disease diagnosis method using multimodal supervised contrastive learning by expanding upon the multimodal self-supervised learning. RandAugment method was used to augment crop image and time series of environment data. These augmented data passed through encoder and projection head for each modality, yielding low-dimensional features. Subsequently, the proposed multimodal supervised contrastive loss helped features from the same class get closer while pushing apart those from different classes. Following this, the pretrained model was fine-tuned for crop disease diagnosis. The visualization of t-SNE result and comparative assessments of crop disease diagnosis performance substantiate that the proposed method has superior performance than multimodal self-supervised learning.

• Journal of Information Science Theory and Practice
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• 제4권3호
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• pp.6-27
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• 2016

There has been a growing interest and enthusiasm for the application of virtual worlds in learning and training. This research proposes a design framework of a virtual world-based learning environment that integrates two unique features of the virtual world technology, immersion and interactivity, with an instructional strategy that promotes self-regulatory learning. We demonstrate the usefulness and assess the effectiveness of our design in the context of information security learning. In particular, the information security learning module implemented in Second Life was incorporated into an Introduction to Information Security course. Data from pre- and post- learning surveys were used to evaluate the effectiveness of the learning module. Overall, the results strongly suggest that the virtual world-based learning environment enhances information security learning, thus supporting the effectiveness of the proposed design framework. Additional results suggest that learner traits have an important influence on learning outcomes through perceived enjoyment. The study offers useful design and implementation guidelines for organizations and universities to develop a virtual world-based learning environment. It also represents an initial step towards the design and explanation theories of virtual world-based learning environments.

• 공학교육연구
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• 제19권6호
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• pp.57-62
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• 2016

In this work, we present a case study of using the assessments for the enhancement of students' learning motivation in engineering education. The assessments, given in between summative assessments such as midterms and finals, may have a component of formative evaluation, which are reported as very effective tools as the sources of feedback to improve teaching and learning. We studied how the students' performance is improved by additional tests in engineering education. Also, we examined the factors of successful results of the cooperative learning model, Student Teams-Achievement Division, which is based on imposing a number of tests, achieved in our previous work.

• 산업경영시스템학회지
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• 제5권6호
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• pp.69-78
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• 1982

Traditional CVP (Cost-Volume-Profit) analysis employs linear cost and revenue functions within some specified time period and range of operations. Therefore CVP analysis is assumption of constant labor productivity. The use of linear cost functions implicity assumes, among other things, that firm's labor force is either a homogenous group or a collection homogenous subgroups in a constant mix, and that total production changes in a linear fashion through appropriate increase or decrease of seemingly interchangeable labor unit. But productivity rates in many firms are known to change with additional manufacturing experience in employee skill. Learning curve is intended to subsume the effects of all these resources of productivity. This learning phenomenon is quantifiable in the form of a learning curve, or manufacturing progress function. The purpose d this study is to show how alternative assumptions regarding a firm's labor force may be utilize by integrating conventional CVP analysis with learning curve theory, Explicit consideration of the effect of learning should substantially enrich CVP analysis and improve its use as a tool for planning and control of industry.

• 대한한의학회지
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• 제40권1호
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• pp.78-85
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• 2019

Objectives:　The purpose of this study is to evaluate the development of the near poor Families' Children via learning disability indices, frontal executive function. Methods: Seventeen children (10 boys, 7 girls, 6.6-11.9 years) from the near poor Families' were enrolled in this study. Children were evaluated for a learning disability and frontal executive function. Results: In Learning disability indices, 3 children showed low scores in subscales and 2 children showed low scores in learning quotient. In Frontal executive function, 3 children showed low scores in CCTT (Children's Color Trails Test) and 11children showed low scores in STROOP (Stroop Color and Word Test). Conclusions: Intensive management, educational programs, and additional neuropsychological tests will be needed in children with low learning scores.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3855-3867
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• 2022

Name separation (splitting full names into surnames and given names) is not a tedious task in a multiethnic country because the procedure for splitting surnames and given names is ethnicity-specific. Malaysia has multiple main ethnic groups; therefore, separating Malaysian full names into surnames and given names proves a challenge. In this study, we develop a two-phase framework for Malaysian name separation using deep learning. In the initial phase, we predict the ethnicity of full names. We propose a recurrent neural network with long short-term memory network-based model with character embeddings for prediction. Based on the predicted ethnicity, we use a rule-based algorithm for splitting full names into surnames and given names in the second phase. We evaluate the performance of the proposed model against various machine learning models and demonstrate that it outperforms them by an average of 9%. Moreover, transfer learning and fine-tuning of the proposed model with an additional dataset results in an improvement of up to 7% on average.

• 대한수학회지
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• 제59권2호
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• pp.217-233
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• 2022

Although machine learning shows state-of-the-art performance in a variety of fields, it is short a theoretical understanding of how machine learning works. Recently, theoretical approaches are actively being studied, and there are results for one of them, margin and its distribution. In this paper, especially we focused on the role of margin in the perturbations of inputs and parameters. We show a generalization bound for two cases, a linear model for binary classification and neural networks for multi-classification, when the inputs have normal distributed random noises. The additional generalization term caused by random noises is related to margin and exponentially inversely proportional to the noise level for binary classification. And in neural networks, the additional generalization term depends on (input dimension) × (norms of input and weights). For these results, we used the PAC-Bayesian framework. This paper is considering random noises and margin together, and it will be helpful to a better understanding of model sensitivity and the construction of robust generalization.

• 공학교육연구
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• 제15권6호
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• pp.34-42
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• 2012

Problem solving by homework assignment is a process of practicing what were discussed in classrooms and thus is considered as an essential part of learning procedure in engineering education. We introduced the concept of cooperative learning, Student Teams-Achievement Division(STAD), to improve the students' learning efficiency by in-class problem solving. The instructor explained fundamental concepts, and lecture materials were handed out to compensate for the time of in-class team activity. Brief tests were given after every chapter, and team-based additional credits were given for the improvement comparing the average of previous tests of each student. This attempt of modified STAD was evaluated to have brought about a significant improvement in students' academic achievement, in addition to activating classroom atmosphere.

• 전자공학회논문지B
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• 제31B권8호
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• pp.211-218
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• 1994

In this paper we develope a hybrid learning rule to improve the robustness of multi-layer Perceptions. In most neural networks the activation of a neuron is deternined by a nonlinear transformation of the weighted sum of inputs to the neurons. Investigating the behaviour of activations of hidden layer neurons a new learning algorithm is developed for improved robustness for multi-layer Perceptrons. Unlike other methods which reduce the network complexity by putting restrictions on synaptic weights our method based on error-backpropagation increases the complexity of the underlying proplem by imposing it saturation requirement on hidden layer neurons. We also found that the additional gradient-descent term for the requirement corresponds to the Hebbian rule and our algorithm incorporates the Hebbian learning rule into the error back-propagation rule. Computer simulation demonstrates fast learning convergence as well as improved robustness for classification and hetero-association of patterns.