• Journal of Korean Elementary Science Education
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• v.41 no.2
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• pp.418-431
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• 2022

In this study, we analyzed the form, presentation, and interactivity of external representations presented in the matter units of elementary school science digital textbooks developed under the 2015 Revised National Curriculum. The analytic framework of the previous study was modified and supplemented. The matter units in the 3rd-6th grade science digital textbooks were analyzed by dividing them into "body texts" and "inquiries" area. The results revealed that visual-verbal and visual-nonverbal representations were presented the most. Conversely, audial-nonverbal representations were presented at a high frequency only in the body texts, and audial-verbal representations were presented at a low frequency in both the body texts and the inquiries. Regarding the presentation, when verbal and visual-nonverbal representations appeared together, visual-verbal and visual-nonverbal representations were primarily presented together. In some cases where visual-verbal, audial-verbal, and visual-nonverbal representations were presented together, information on visual-verbal and audial-verbal representations was presented redundantly. Audial-nonverbal representations unrelated to contents were presented along with other external representations, and the frequency was particularly high in the body texts. Regarding the contiguity, no visual-verbal and visual-nonverbal representations were presented on different pages, and no audial-verbal representations were presented asynchronously with visual-nonverbal representations. Regarding the interactivity, explanatory feedback and low-level manipulations were mainly presented. Based on the results, implications to improve digital textbooks are discussed from the perspective of multiple representation-based learning.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.687-692
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• 2023

We analyze the accuracy of emotion analysis of natural language processing deep learning model and propose to use it for emotional content development. After looking at the outline of the GPT-3 model, about 6,000 pieces of dialogue data provided by Aihub were input to 9 emotion categories: 'joy', 'sadness', 'fear', 'anger', 'disgust', and 'surprise'. ', 'interest', 'boredom', and 'pain'. Performance evaluation was conducted using the evaluation indices of accuracy, precision, recall, and F1-score, which are evaluation methods for natural language processing models. As a result of the emotion analysis, the accuracy was over 91%, and in the case of precision, 'fear' and 'pain' showed low values. In the case of reproducibility, a low value was shown in negative emotions, and in the case of 'disgust' in particular, an error appeared due to the lack of data. In the case of previous studies, emotion analysis was mainly used only for polarity analysis divided into positive, negative, and neutral, and there was a limitation in that it was used only in the feedback stage due to its nature. We expand emotion analysis into 9 categories and suggest its use in the development of emotional content considering it from the planning stage. It is expected that more accurate results can be obtained if emotion analysis is performed by additionally collecting more diverse daily conversations through follow-up research.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.542-549
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• 2023

Since the growing interest in surrogate modeling, there has been continuous research aimed at simulating nonlinear chemical processes using data-driven machine learning. However, the opaque nature of machine learning models, which limits their interpretability, poses a challenge for their practical application in industry. Therefore, this study aims to analyze chemical processes using Explainable Artificial Intelligence (XAI), a concept that improves interpretability while ensuring model accuracy. While conventional sensitivity analysis of chemical processes has been limited to calculating and ranking the sensitivity indices of variables, we propose a methodology that utilizes XAI to not only perform global and local sensitivity analysis, but also examine the interactions among variables to gain physical insights from the data. For the ammonia synthesis process, which is the target process of the case study, we set the temperature of the preheater leading to the first reactor and the split ratio of the cold shot to the three reactors as process variables. By integrating Matlab and Aspen Plus, we obtained data on ammonia production and the maximum temperatures of the three reactors while systematically varying the process variables. We then trained tree-based models and performed sensitivity analysis using the SHAP technique, one of the XAI methods, on the most accurate model. The global sensitivity analysis showed that the preheater temperature had the greatest effect, and the local sensitivity analysis provided insights for defining the ranges of process variables to improve productivity and prevent overheating. By constructing alternative models for chemical processes and using XAI for sensitivity analysis, this work contributes to providing both quantitative and qualitative feedback for process optimization.

• The Journal of the Convergence on Culture Technology
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• v.9 no.6
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• pp.967-971
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• 2023

Recently, a variety of AI-based platform services are available, and one of them is ChatGPT that processes a large quantity of data in the natural language and generates an answer after self-learning. ChatGPT can perform various tasks including software programming in the IT sector. Particularly, it may help generate a simple program and correct errors using C Language, which is a major programming language. Accordingly, it is expected that ChatGPT is capable of effectively using Verilog HDL, which is a hardware language created in C Language. Verilog HDL synthesis, however, is to generate imperative sentences in a logical circuit form and thus it needs to be verified whether the products are executed properly. In this paper, we aim to select small-scale logical circuits for ease of experimentation and to verify the results of circuits generated by ChatGPT and human-designed circuits. As to experimental environments, Xilinx ISE 14.7 was used for module modeling, and the xc3s1000 FPGA chip was used for module embodiment. Comparative analysis was performed on the use area and processing time of FPGA to compare the performance of ChatGPT products and Verilog HDL products.

• Journal of The Korean Association For Science Education
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• v.44 no.1
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• pp.39-55
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• 2024

This study aimed to examine research trends related to Natural Language Processing (NLP) in science education by analyzing 37 domestic and international documents that utilized NLP techniques in the field of science education from 2011 to September 2023. In particular, the study systematically analyzed the content, focusing on the main application areas of NLP techniques in science education, the role of teachers when utilizing NLP techniques, and a comparison of domestic and international perspectives. The analysis results are as follows: Firstly, it was confirmed that NLP techniques are significantly utilized in formative assessment, automatic scoring, literature review and classification, and pattern extraction in science education. Utilizing NLP in formative assessment allows for real-time analysis of students' learning processes and comprehension, reducing the burden on teachers' lessons and providing accurate, effective feedback to students. In automatic scoring, it contributes to the rapid and precise evaluation of students' responses. In literature review and classification using NLP, it helps to effectively analyze the topics and trends of research related to science education and student reports. It also helps to set future research directions. Utilizing NLP techniques in pattern extraction allows for effective analysis of commonalities or patterns in students' thoughts and responses. Secondly, the introduction of NLP techniques in science education has expanded the role of teachers from mere transmitters of knowledge to leaders who support and facilitate students' learning, requiring teachers to continuously develop their expertise. Thirdly, as domestic research on NLP is focused on literature review and classification, it is necessary to create an environment conducive to the easy collection of text data to diversify NLP research in Korea. Based on these analysis results, the study discussed ways to utilize NLP techniques in science education.

• Journal of The Korean Association For Science Education
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• v.44 no.2
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• pp.195-207
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• 2024

The purpose of this study was to explore high school science teachers' perceptions and practices regarding the implementation of achievement standards-based assessment (ASA) in their science teaching. To achieve this, semi-structured individual interviews were conducted with 20 science teachers who had implemented ASA. The participating teachers were asked to share their opinions on ASA implementation, the effects of ASA on changes in their teaching, and students' reactions to ASA. The results were as follows. Most of the teachers recognized that the initial intention behind ASA implementation began to be realized in schools only after course rankings were no longer required to be indicated. Some teachers felt that ASA allowed them to focus on students' progress, rather than evaluating them by achievement scores. It also helped some teachers identify students who were experiencing learning difficulties and offer appropriate support. In addition, some teachers acknowledged being able to reorganize their science lessons according to the essential goals of science subjects in the curriculum and provide more detailed feedback on students' achievements. However, some teachers expressed difficulties in setting an appropriate level of achievement for their lessons or in evaluating students' progress using qualitative methods. Lastly, the teachers expressed concerns about the remarkably lower motivation of some students for learning science after the indication of course ranking was no longer required.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• Journal of the Ergonomics Society of Korea
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• v.36 no.5
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• pp.385-394
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• 2017

Objective: The aim of this study is to investigate how to design effective virtual reality-based training (i.e., virtual training) in maritime safety and to present methods for enhancing interface fidelity by employing immersive interaction and 3D user interface (UI) design. Background: Emerging virtual reality technologies and hardware enable to provide immersive experiences to individuals. There is also a theory that the improvement of fidelity can improve the training efficiency. Such a sense of immersion can be utilized as an element for realizing effective training in the virtual space. Method: As an immersive interaction, we implemented gesture-based interaction using leap motion and Myo armband type sensors. Hand gestures captured from both sensors are used to interact with the virtual appliance in the scenario. The proposed 3D UI design is employed to visualize appropriate information for tasks in training. Results: A usability study to evaluate the effectiveness of the proposed method has been carried out. As a result, the usability test of satisfaction, intuitiveness of UI, ease of procedure learning, and equipment understanding showed that virtual training-based exercise was superior to existing training. These improvements were also independent of the type of input devices for virtual training. Conclusion: We have shown through experiments that the proposed interaction design results are more efficient interactions than the existing training method. The improvement of interface fidelity through intuitive and immediate feedback on the input device and the training information improve user satisfaction with the system, as well as training efficiency. Application: Design methods for an effective virtual training system can be applied to other areas by which trainees are required to do sophisticated job with their hands.

• Journal of Korean Home Economics Education Association
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• v.30 no.2
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• pp.117-136
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• 2018

The purpose of this study is restructuring the template to apply the 'Backward design' for Home Economics Instruction. This study reviewed the theory of Wiggins and McTighe, the advocates of backward design, and examined the template for version 2.0 of Wiggins and McTighe (2011). In addition, This study analyzed various previous researches using backward design, and drew implications for template restructuring for applying backward design on Home Economics Instruction. In addition, the validity of the template was verified through facial validity through the expert council. Through this process, the final 5 stages (1. Curriculum Analysis → 2. Instructional Design (Learning Experience Design ↔ Evaluation Design (Performance Task Planning) → 3. Instruction Flow → 4. Class guide for each class → 5. Evaluation record, and feedback)suggested templates for applying backward design. Future research will use the restructured assumptions and templates to develop teaching materials in the Home Economics areas of the 2015 revised curriculum. In future research, field research should be carried out on how the Home Economics instruction with backward design affects learners.