• Human Ecology Research
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• v.61 no.3
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• pp.361-373
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• 2023

The purpose of this study is to develop and apply a teaching-learning plan of apartment house class to improve middle school students' empathy for home economics education and to verify its effect. The study was conducted in five stages: analysis, design, development, implementation, and evaluation using the ADDIE model. In the analysis phase, the study set learning objectives after analyzing how community housing is dealt with in the 2015 revised home economics curriculum and 12 current technology and home economics textbooks. In the design and development stage, in order to evaluate the validity of the experts and to improve the empathy ability, the study goal design, the composition of the learning elements, the development of the learning materials, and the pre and post-questionnaire for the students were developed. In the implementation stage, the empathic ability evaluation was carried out before and after the beginning of the first class by applying the teaching-learning process plan of the 8th class. In the evaluation stage, we examined whether the teaching-learning process developed in this study has a significant effect on empathy ability by evaluating the pre-post difference of empathy ability. As a result of examining the results of the pre - and post - evaluation of empathy ability for the results of this study, both cognitive and communicative factors were improved, and the apartment house class had a significant effect on the improvement of empathy ability.

• Journal of Creative Information Culture
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• v.4 no.2
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• pp.95-101
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• 2018

As a result of introducing robot assisted learning which utilizes social robots or telepresence robots in language learning or special education, research on technology acceptance model for robot-assisted learning is also being conducted. The unified theory of acceptance and use of technology (UTAUT) model of intelligent robot has been studied, but of tele-operated robot is insufficient. The purpose of this paper is to estimate the UTAUT model by pre-service teachers who experienced telepresence robot-assisted learning that can be done in future school. It is found that the estimated UTAUT model consists of more concise factors than social robots, and the importance of perceived enjoyment is higher. In other words, the pre-service teachers showed significant acceptance of tele-operated robots with enhanced enjoyment composed of its mobility, communication, and touchable appearance of the face and body.

• English Language & Literature Teaching
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• v.17 no.2
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• pp.93-114
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• 2011

The aim of this study is twofold. First, this study aimed to explore how Korean university students' English learning motivation is related to their English listening proficiency and study time. Second, it attempted to interpret the English learning motivation linking the two different motivation theories: self-determination theory and L2 motivational self system. The constructs of the students' L2 learning motivation were investigated with the data obtained through the questionnaire from 122 sophomore students. A factor analysis was conducted to extract the major factors of motivation. As a result, 6 factors were extracted: Intrinsic Pleasure, Identified Value Regulation, Intrinsic Accomplishment, Introjected Regulation, External Regulation, and Identified Regulation. The Interrelatedness among the assessment results on the L2 listening proficiency (pre and post test), listening study time, and motivation factors was measured by correlation coefficients. The statistical results indicated that pre-test scores were significantly related to Identified Regulation and Identified Value Regulation toward English learning, and post-test results had significant correlation with Intrinsic Accomplishment and Identified Regulation. However, no motivation subtypes showed statistical association with the students' listening study time. The results were attempted to be interpreted both under L2 motivational self system and self-determination framework to better illuminate the motivation theory with more explanatory power.

• Korean Journal of Health Education and Promotion
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• v.14 no.2
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• pp.113-123
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• 1997

This Study attempts to verify the effects of expository and inquiry instruction on learning attitude and academic achievement of health education in elementary school. For the accomplishment of the above purpose, specific problems were formulated as follows: The expository instruction is based on David Ausubel’s Advance Organizers and the inquiry instruction, Richard Suchman’s Inquiry Training in this study. To testify the above research problems, 247 students of six classes were randomly sampled from sixth graders of “Y” elementary school, located in Suwon city. One group was taught by expository instruction method and other group was taught by inquiry instruction method. The measurement tools used in this study were learning attitude test, pre-post academic achievement test, expository teaching-learning sheets and inquiry teaching-learning sheets. The experimental treatments had been lasted for eight weeks from June to October 1996. After the experimental treatments, to testify the effects of the experiment, the pre-test and post-test were administered and the results of the tests were compared by t-test. The conclusions were as follows; 1. There was a significant difference between expository and inquiry instruction(p〈.001). Inquiry instruction was more effective than expository instruction in changing learning attitude. 2. There was a significant difference between expository and inquiry instruction(p〈.001). expository instruction was more effective than Inquiry instruction in changing academic achievement. This study suggests that instructional method should be determined in accordance with the purpose of the lesson.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.33-38
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• 2021

Recently, transfer learning techniques with a base convolutional neural network (CNN) model have widely gained acceptance in early detection and classification of crop diseases to increase agricultural productivity with reducing disease spread. The transfer learning techniques based classifiers generally achieve over 90% of classification accuracy for crop diseases using dataset of crop leaf images (e.g., PlantVillage dataset), but they have ability to classify only the pre-trained diseases. This paper provides with an evaluation scheme on selecting an effective base CNN model for crop disease transfer learning with regard to the accuracy of trained target crops as well as of untrained target crops. First, we present transfer learning models called CDC (crop disease classification) architecture including widely used base (pre-trained) CNN models. We evaluate each performance of seven base CNN models for four untrained crops. The results of performance evaluation show that the DenseNet201 is one of the best base CNN models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4816-4834
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• 2020

This paper proposes transfer learning and fine-tuning techniques for a deep learning model to detect three distinct brain tumors from Magnetic Resonance Imaging (MRI) scans. In this work, the recent YOLOv4 model trained using a collection of 3064 T1-weighted Contrast-Enhanced (CE)-MRI scans that were pre-processed and labeled for the task. This work trained with the partial 29-layer YOLOv4-Tiny and fine-tuned to work optimally and run efficiently in most platforms with reliable performance. With the help of transfer learning, the model had initial leverage to train faster with pre-trained weights from the COCO dataset, generating a robust set of features required for brain tumor detection. The results yielded the highest mean average precision of 93.14%, a 90.34% precision, 88.58% recall, and 89.45% F1-Score outperforming other previous versions of the YOLO detection models and other studies that used bounding box detections for the same task like Faster R-CNN. As concluded, the YOLOv4-Tiny can work efficiently to detect brain tumors automatically at a rapid phase with the help of proper fine-tuning and transfer learning. This work contributes mainly to assist medical experts in the diagnostic process of brain tumors.

• Journal of Information Processing Systems
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• v.19 no.5
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• pp.673-687
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• 2023

Convolutional neural network-based deep learning technology is the most commonly used in image identification, but it requires large-scale data for training. Therefore, application in specific fields in which data acquisition is limited, such as in the military, may be challenging. In particular, the identification of ground weapon systems is a very important mission, and high identification accuracy is required. Accordingly, various studies have been conducted to achieve high performance using small-scale data. Among them, the ensemble method, which achieves excellent performance through the prediction average of the pre-trained models, is the most representative method; however, it requires considerable time and effort to find the optimal combination of ensemble models. In addition, there is a performance limitation in the prediction results obtained by using an ensemble method. Furthermore, it is difficult to obtain the ensemble effect using models with imbalanced classification accuracies. In this paper, we propose a transfer learning-based feature fusion technique for heterogeneous models that extracts and fuses features of pre-trained heterogeneous models and finally, fine-tunes hyperparameters of the fully connected layer to improve the classification accuracy. The experimental results of this study indicate that it is possible to overcome the limitations of the existing ensemble methods by improving the classification accuracy through feature fusion between heterogeneous models based on transfer learning.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• Journal of Korean Elementary Science Education
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• v.29 no.3
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• pp.326-340
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• 2010

TGT (teams-games-tournaments) cooperative learning is suggested as a method which enables both the individualized teaching-learning and the small group learning in students-centered open education. This study investigated the instructional effects of the pre-learning program applied by ICT-based TGT cooperative module for science museum excursion regarding of the earth and the moon on the science related attitude according to gender difference in elementary school science class. Three classes of third graders (N=87) at a elementary school were randomly assigned to the ICT-based TGT cooperative learning group, the ICT learning group, and traditional learning group. The students were taught about the planning of exploring the moon in the chapter of the earth and the moon, for 1 class hour. Prior to the instructions, the TOSRA(test of science related attitude) and achievement test were administered. Two-way ANCOVA results revealed that the scores of the ICT-based TGT cooperative learning group were significantly higher than the other learning groups for most of the TOSRA scales. However, there was a little significant difference among the three groups in the three distinct scales of TOSRA, Normality of Scientists, Leisure Interest in Science, and Career Interest in Science. Advantage/disadvantage and usefulness of ICT-based TGT cooperative learning were also discussed.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.37-46
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• 2019

The purpose of this study is to develop a flipped-learning based education model for vocational education and training. Based on previous studies, we have developed a training model that includes definitions of flipped-learning, major learning activities, and operational strategies in each occupation. In addition, delphi surveys were carried out to confirm the validity of the educational model for HRD and vocational education and training specialists. Finally, the flipped-learning model for vocational education and training consists of three stages: Pre-learning, Main-learning, and Post-learning. Pre-learning stage include Online lectures, Simple tasks, Main-learning stage include Active learning and Coaching-Debriefing lecture, Post-learning stage includes Individual reflection-Additional task performance activities. The educational model developed through this study was developed with the focus on improving the linkage and the performance of the goal of vocational education and training.