• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.307-315
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• 2022

The purpose of this study was to design a training program for pre-service elementary teachers, incorporating the concepts of extended reality technologies. This program contained the basic skills necessary for them to utilize in their future classrooms. To accomplish this, 12 undergraduate students of various majors enrolled in one of Korea's national universities of education were selected as research subjects. For a total of 6 times over 6 weeks, they participated in a training program learning the basic concepts of virtual, augmented, and mixed reality, as well as creating their own education software to use in simulated classes. To improve the quality of future research efforts, this study found it would be beneficial to: 1) expand the relevant support equipment, 2) provide students with preliminary, background knowledge of text-based programming, 3) introduce short-term, more intensive training, and 4) improve the survey methods for this research.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.167-176
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• 2023

In this paper, we designed and implemented a graph assessment module that can evaluate graphs in an programming assessment system based on text and numbers. The assessment method of the graph assessment module is self-evaluation that outputs two graphs generated by codes submitted by learners and by answers, automatic-evaluation that converts each graph image into an array, and gives feedback if it is wrong. The data used to generate the graph can be inputted directly or used from external data, and the method of generatng graph that can be evaluated is MATLAB style in matplotlib, and the graph shape that can be evaluated is presented in mathematics and curriculum. Through expert review, it was confirmed that the content elements of the assessment module, the possibility of learning, and the validity of the learner's needs were met. The graph assessment module developed in this study has expanded the evaluation area of the programming automatic asssessment system and is expected to help students learn data visualization.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.167-173
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• 2021

The purpose of this study is to discuss the types of algorithms and data categories in AI education for elementary school students. The study surveyed 11 pre-elementary teachers after providing education and practice on various data, artificial intelligence algorithm, and AI education platform for 15 weeks. The categories of data and algorithms considering the elementary school level, and educational tools were presented, and their suitability was analyzed. Through the questionnaire, it was concluded that it is most suitable for the teacher to select and preprocess data in advance according to the purpose of the class, and the classification and prediction algorithms are suitable for elementary AI education. In addition, it was confirmed that Entry is most suitable as an AI educational tool, and materials that explain mathematical knowledge are needed to educate the concept of learning of AI. This study is meaningful in that it specifically presents the categories of algorithms and data with in AI education for elementary school students, and analyzes the need for related mathematics education and appropriate AI educational tools.

• Steel and Composite Structures
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• v.48 no.2
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• pp.179-190
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• 2023

Many recent attempts have sought accurate prediction of pile pullout resistance (P_ul) using classical machine learning models. This study offers an improved methodology for this objective. Adaptive neuro-fuzzy inference system (ANFIS), as a popular predictor, is trained by a capable metaheuristic strategy, namely equilibrium optimizer (EO) to predict the P_ul. The used data is collected from laboratory investigations in previous literature. First, two optimal configurations of EO-ANFIS are selected after sensitivity analysis. They are next evaluated and compared with classical ANFIS and two neural-based models using well-accepted accuracy indicators. The results of all five models were in good agreement with laboratory P_uls (all correlations > 0.99). However, it was shown that both EO-ANFISs not only outperform neural benchmarks but also enjoy a higher accuracy compared to the classical version. Therefore, utilizing the EO is recommended for optimizing this predictive tool. Furthermore, a comparison between the selected EO-ANFISs, where one employs a larger population, revealed that the model with the population size of 75 is more efficient than 300. In this relation, root mean square error and the optimization time for the EO-ANFIS (75) were 19.6272 and 1715.8 seconds, respectively, while these values were 23.4038 and 9298.7 seconds for EO-ANFIS (300).

• Computers and Concrete
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• v.32 no.2
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• pp.149-163
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• 2023

One of the main design parameters traditionally utilized in projects of geotechnical engineering is the uniaxial compressive strength. The present paper employed three artificial intelligence methods, i.e., the stochastic fractal search (SFS), the multi-verse optimization (MVO), and the vortex search algorithm (VSA), in order to determine the compressive strength of concrete (CSC). For the same reason, 1030 concrete specimens were subjected to compressive strength tests. According to the obtained laboratory results, the fly ash, cement, water, slag, coarse aggregates, fine aggregates, and SP were subjected to tests as the input parameters of the model in order to decide the optimum input configuration for the estimation of the compressive strength. The performance was evaluated by employing three criteria, i.e., the root mean square error (RMSE), mean absolute error (MAE), and the determination coefficient (R²). The evaluation of the error criteria and the determination coefficient obtained from the above three techniques indicates that the SFS-MLP technique outperformed the MVO-MLP and VSA-MLP methods. The developed artificial neural network models exhibit higher amounts of errors and lower correlation coefficients in comparison with other models. Nonetheless, the use of the stochastic fractal search algorithm has resulted in considerable enhancement in precision and accuracy of the evaluations conducted through the artificial neural network and has enhanced its performance. According to the results, the utilized SFS-MLP technique showed a better performance in the estimation of the compressive strength of concrete (R²=0.99932 and 0.99942, and RMSE=0.32611 and 0.24922). The novelty of our study is the use of a large dataset composed of 1030 entries and optimization of the learning scheme of the neural prediction model via a data distribution of a 20:80 testing-to-training ratio.

• Geomechanics and Engineering
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• v.36 no.5
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• pp.465-474
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• 2024

Uniaxial compressive strength (UCS) is a critical geomechanical parameter that plays a significant role in the evaluation of rocks. The practice of indirectly estimating said characteristics is widespread due to the challenges associated with obtaining high-quality core samples. The primary aim of this study is to investigate the feasibility of utilizing the gene expression programming (GEP) technique for the purpose of forecasting the UCS for various rock categories, including Schist, Granite, Claystone, Travertine, Sandstone, Slate, Limestone, Marl, and Dolomite, which were sourced from a wide range of quarry sites. The present study utilized a total of 170 datasets, comprising Schmidt hammer (SH), porosity (n), point load index (Is(50)), and P-wave velocity (Vp), as the effective parameters in the model to determine their impact on the UCS. The UCS parameter was computed through the utilization of the GEP model, resulting in the generation of an equation. Subsequently, the efficacy of the GEP model and the resultant equation were assessed using various statistical evaluation metrics to determine their predictive capabilities. The outcomes indicate the prospective capacity of the GEP model and the resultant equation in forecasting the unconfined compressive strength (UCS). The significance of this study lies in its ability to enable geotechnical engineers to make estimations of the UCS of rocks, without the requirement of conducting expensive and time-consuming experimental tests. In particular, a user-friendly program was developed based on the GEP model to enable rapid and very accurate calculation of rock's UCS, doing away with the necessity for costly and time-consuming laboratory experiments.

• Clinical Endoscopy
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• v.55 no.1
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• pp.113-121
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• 2022

Background/Aims: We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods. Methods: We studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image. Results: The accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001) Conclusions: Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.

• Journal of Science Education
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• v.41 no.3
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• pp.334-350
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• 2017

The purposes of this study are to develop scientific-inquiry based on STEAM education program and to investigate the effects of the program on middle-school students' interests, self-efficacy, and career choice about science, technology/engineering, and mathematics. In order to develop this program, the literature investigation and previous studies were conducted, so that finally the developmental direction was based on scientific inquiry and the developmental theme and model were selected. A total 92 first-graders in G middle-school of Daegu city were participated in this study. A single group pre-post test paired t-test was conducted to figure out changes of students' interest, self-efficacy, and career choices before or after applying this program. In addition, in-depth interviews were conducted with 14 students to find their specific responses. The results of this study were as follows. First, STEAM education program on the theme of 'RC Airplane' was developed on the basis of the 'ADBA' model. Second, the developed STEAM educational program not only results a decisive difference statistically but also has significant effects on middle-school students' interests, self-efficacy, and career choice in science, technology/engineering, and mathematics, who are involved in the free-semester program, across the overall affective domain. In conclusion, the STEAM educational program in this study could affect significant meanings to middle-school students during the free-semester. It could contribute to facilitate middle-school students' education for happiness and to grow the creative STEAM talents.

• Journal of Science Education
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• v.36 no.2
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• pp.381-393
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• 2012

This study was analyzed that what kind of elements for STEAM, except scientific commonsense, are contained in 2009 revised chemistry textbooks I for high school students. So first, elements of STEAM in textbooks were examined by following three sections; by publishing company, each unit and area of textbook. For reference, new sub-elements of STEAM were set because existing elements of STEAM is incongruent with current textbooks. As a result, most chemistry textbooks included elements of STEAM properly for inter-related learning with the other fields. Every textbook had its unique learning methods for utilizing elements of STEAM and they were unified as one way. Depending on textbooks, learning methods were little bit different from the others. Also, detailed elements of STEAM contained in textbooks were classified just 14 types. And they were even focused on a few elements according to sort of textbook. Thus, it seemed that there was a certain limitation of current education of STEAM in chemistry Field. By the unit, according to the curriculum, contained elements of STEAM were different. Almost all elements of STEAM were located in I section. Consequently, it is difficult to include elements of STEAM if mathematics or history were not existed in curriculum. Lastly, by the area, most of all elements of STEAM were included in reference section. Almost all elements of STEAM were focused on art and culture. Thus, STEAM was used for utilization about chemical knowledge in substance. Otherwise, convergence training for approach method was not enough in chemical knowledge.

• Journal of Gifted/Talented Education
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• v.24 no.4
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• pp.561-576
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• 2014

The purpose of this study is to investigate the perception of the mothers of science gifted in respect to giftedness compared to the "Scale for Rating the Behavioral Characteristics of Superior Students-R(SRBCSS-R)". For that, a survey of 18 mothers of elementary school science gifted and 32 mothers of middle school science gifted was conducted in relation to giftedness. The words and frame of this survey were analyzed using the Semantic Network Analysis. The results are as follows : The mothers of Elementary school science gifted perception were found to have a connected giftedness with reading, science, making something, etc.. On the other hand, the mothers of middle school science gifted perception were found to have a connected giftedness with problem, solving problem, mathematics, etc. in words analysis. The mothers of Elementary school science gifted have a strong connection with category on creativity, motivation, etc.. On the other hand, the mothers of middle school science gifted were more inclined towards the category on learning, motivation, etc. in frame analysis. That is to say, the mothers of science gifted are perceptive about giftedness respect to some elements as the "Scale for Rating the Behavioral Characteristics of Superior Students-R" on the giftedness. Therefore, a correct understanding about giftedness in respect to the mothers of science gifted is required and parent education is needed for appropriate science gifted education.