• Journal of Practical Engineering Education
• /
• v.12 no.2
• /
• pp.363-369
• /
• 2020

This study intends to examine effects of python education for adolescents. 6 primary studies were chosen through careful search process and investigated through meta-analysis. Research findings were as follows. The total effect size was 0.684. Second, the effect sizes of dependent variables were academic achievement 0.871, cognitive domain 0.625, and affective domain 0.428 in order. Third, for cognitive domain, the effect sizes were self-efficacy 0.833, problem-solving 0.283, computing thinking 0.276, and coding competency 0.251 in order. Fourth, for affective domain, the effect sizes were learning interest 0.560 and programming interest 0.417 in order. Fifth, regarding school level, the effect sizes were middle school 0.851, high school 0.585, and college 0.435 in order. Finally, for subject areas, the effect sizes were mathematics 1.057, design 0.595, information 0.585, and software 0.28 in order.

• Journal of the Korea Convergence Society
• /
• v.11 no.11
• /
• pp.103-114
• /
• 2020

This paper's research objective is to test educational content with different dimensions of knowledge during a graphic basic design course, while also proposing teaching plans and opinions for courses in flipped classrooms as well as enhancing educational efficiency. It categorizes educational content of courses based on the dimensions of knowledge in the learning objectives of Bloom's taxonomy. 120 students are divided into four experimental groups to respectively under go flipped classroom learning by using different dimensions of knowledge involved in course content. Course pretests and post tests are used to obtain and analyze experimental data. Among this knowledge, factual and conceptual knowledge obtained during extra curricular independent learning as well as programmed and meta-cognitive knowledge obtained during in-class learning from a flipped classroom can stimulate student's learning initiative and also enhance learning efficiency. According to research results and student feedback, this paper will propose targeted categorization methods for course content and also suggest educational strategies for these courses' flipped classroom model.

• Journal of The Korean Association of Information Education
• /
• v.16 no.3
• /
• pp.337-352
• /
• 2012

In this paper, a smart education system is developed and implemented for ADHD students. Usually, ADHD students lack of self efficacy. Self efficacy is very important factor for improving social adaptability and learning effect of ADHD students. In the proposed system, MLE concept is adapted. MLE concept is used to improve self efficacy of ADHD students. The purpose of the proposed system is to help ADHD students have high study capability. The proposed system is applied an ADHD student. The following results are obtained. First, the system can improve study interests. In turn, the system is helpful to improve concentration and learning effect. Second, based on successful study experience, self efficacy is improved. Third, study achievement is improved by changing cognitive structure that is due to development of meta-cognition.

• Imaging Science in Dentistry
• /
• v.51 no.3
• /
• pp.237-242
• /
• 2021

Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords(deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.35 no.11
• /
• pp.155-162
• /
• 2019

The construction industry is the highest safety accident causing industry as 28.55% portion of all industries' accidents in Korea. In particular, falling is the highest accidents type composed of 60.16% among the construction field accidents. Therefore, we analyzed the factors of major disaster affecting the fall accident and then derived feature importances by considering various variables. We used data collected from Korea Occupational Safety & Health Agency (KOSHA) for learning and predicting in the proposed model. We have an effort to predict the degree of occupational fall accidents by using the machine learning model, i.e., Adaboost, short for Adaptive Boosting. Adaboost is a machine learning meta-algorithm which can be used in conjunction with many other types of learning algorithms to improve performance. Decision trees were combined with AdaBoost in this model to predict and classify the degree of occupational fall accidents. HyOperpt was also used to optimize hyperparameters and to combine k-fold cross validation by hierarchy. We extracted and analyzed feature importances and affecting fall disaster by permutation technique. In this study, we verified the degree of fall accidents with predictive accuracy. The machine learning model was also confirmed to be applicable to the safety accident analysis in construction site. In the future, if the safety accident data is accumulated automatically in the network system using IoT(Internet of things) technology in real time in the construction site, it will be possible to analyze the factors and types of accidents according to the site conditions from the real time data.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.1
• /
• pp.170-180
• /
• 2011

The purposes of this study are to find out the effectiveness of the tutoring program and the level of self-regulated learning ability in tutoring program attending students. This study has been performed for team-tutoring program attending students(n=183) who are in 15 department, D college in Daegu. Following results were drawn thought correlation analyses of variables obtained during the survey period. The results were as follows; There was statistically significant difference among perception controlling ability, purpose controlling ability, and action controlling ability. In over 20 ages, organization, meta-perception stratagem, arrangement, self-effectiveness, controlling learning time ability were higher than of lower 20 ages(p<0.05). In tutors, demonstration, organization, meta-perception stratagem, arrangement, checking, purpose intentions, self- effectiveness, achievement, controlling action, help requirement ability were higher than tutees(p<0.05). As results, the tutoring program was effective for both tutors and tutees in college students. Further studies in an education program for students in all years should be implemented to examine tutoring effects. Implementation of tutoring should address the frustrations and difficulties encountered by the students to facilitate better outcomes.

• The Journal of the Korea Contents Association
• /
• v.16 no.7
• /
• pp.640-650
• /
• 2016

This study was conducted in order to develop an education program based on simulation with problem-based learning, to apply it to nursing students, and to examine its effects on the students' learning motivation, learning strategy, and academic achievement. The subjects of this study were 69 seniors majoring in nursing. Education based on simulation with problem-based learning was applied to the students from September to October in 2015, and then a questionnaire survey was conducted on their learning motivation, learning strategy, and academic achievement. According to the results of this study, the education based on simulation with problem-based learning reduced the nursing students' other-directed motivation (external motivation), increased their self-regulation motivation (identified motivation, intrinsic motivation), and improved their use of resource management strategies. In addition, academic achievement (academic performance, and educational satisfaction) was in a positive correlation with identified motivation and learning strategies (cognitive strategy, meta cognitive strategy, and resource management strategy). In conclusion, education based on simulation with problem-based learning was found to be an effective education strategy for enhancing nursing students' autonomous motivation and improving their use of resource management strategies. Thus, it is necessary to promote the application of simulation with problem-based learning in various care situations and to study factors and parameters influencing learning related variables.

• Journal of Digital Convergence
• /
• v.19 no.11
• /
• pp.373-379
• /
• 2021

This study intends to test the effects of blended learning and case based learning on learning strategies, critical thinking disposition and academic self-efficacy for undergraduate nursing students. A one group pre-post design was applied to adult nursing of 23 nursing students. Data were collected between March 2 and April 30, 2021. Data were analyzed by using SPSS/WIN 23.0. The results showed that learning strategies(t=-2.43, p=.019) and sub-factor cognitive strategy (t=-2.22, p=.031), meta cognitive strategy(t=-2.59, p=.013) and resource management strategy (z=-2.46, p=.014) were significantly higher than levels before blended learning and case based learning. Critical thinking disposition(t=-1.14, p=.262) and academic self-efficacy(t=-.34, p=.734) were higher than levels before but was no significantly. In conclusion, It was confirmed that blended learning and case based learning is an effective educational program that improves learning strategies of nursing students. In the future, it is necessary to develop a program in which blended learning and case based learning can improve critical thinking disposition and academic self-efficacy, and to verify the effectiveness.

• Journal of Digital Convergence
• /
• v.10 no.11
• /
• pp.427-432
• /
• 2012

The article herein suggested a compound repository and a descriptive method to develop a compound knowledge process. A data target saved in a compound knowledge repository suggested in this article includes all compound knowledge meta data and digital resources, which can be divided into the three following factors according to the purpose: user roles, functional elements, and service ranges. The three factors are basic components to describe abstract models of repository. In this article, meta data of compound knowledge are defined by being classified into the two factors. A component stands for the property about a main agent, activity unit or resource that use and create knowledge, and a context presents the context in which knowledge object are included. An agent of the compound knowledge process performs classification, registration, and pattern information management of composite knowledge, and serves as data flow and processing between compound knowledge repository and user. The agent of the compound knowledge process consists of the following functions: warning to inform data search and extraction, data collection and output for data exchange in an distributed environment, storage and registration for data, request and transmission to call for physical material wanted after search of meta data. In this article, the construction of a compound knowledge repository for recommendation system to be developed can serve a role to enhance learning productivity through real-time visualization of timely knowledge by presenting well-put various contents to users in the field of industry to occur work and learning at the same time.

• The Journal of the Korea Contents Association
• /
• v.10 no.8
• /
• pp.1-10
• /
• 2010

For virtual education, the multimodal learning environment with haptic feedback, termed 'immersive textbook', is necessary to enhance the learning effectiveness. However, the learning contents for immersive textbook are not widely available due to the constraints in creation and playback environments. To address this problem, we propose a framework for producing and displaying the multimodal contents for immersive textbook. Our framework provides an XML-based meta-language to produce the multimodal learning contents in the form of intuitive script. Thus it can help the user, without any prior knowledge of multimodal interactions, produce his or her own learning contents. The contents are then interpreted by script engine and delivered to the user by visual and haptic rendering loops. Also we implemented a prototype based on the aforementioned proposals and performed user evaluation to verify the validity of our framework.