• 대한공업교육학회지
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• v.40 no.2
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• pp.239-258
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• 2015

The purpose of this study is to provide basic data that can be used in a reasonable assessment of the learning outcomes of STEAM. It presented a learning outcome evaluation method, relative weights of key competencies standard that a learner should cultivate. For this study, a pairwise comparison questionnaire about the key competencies was conducted on the STEAM professionals, and AHP was applied to analyze the priority of main factors of key competencies. The results of this study are as follows. First, the importance of capabilities of convergence accomplishment and capabilities of convergent cognition, in the first layer of key competencies, were 39.4% and 36.8%, respectively. In the education evaluation of the STEAM, capabilities of convergence accomplishment and capabilities of convergent cognition showed similar level of importance, and were considered more important factor than capabilities of convergence attitude (23.8%). Second, the relative importance of capabilities of problem solving (20.0%) was highest in the second layer of key competencies, and followed by capabilities of creative thinking (18.3%), responsibility (15.3%), and understanding convergence knowledge (11.0%). Third, it will be a foundation of a competency evaluation, which reasonably evaluates, based on the relative weights, whether to accomplish educational objectives of the STEAM program In addition, this results is expected to become a guide to develop an education program that can improve the teaching and learning process and raise the learning outcome, as well as an education evaluation of the STEAM.

• The Mathematical Education
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• v.62 no.3
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• pp.435-455
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• 2023

This study primarily focused on the development of an Explainable Artificial Intelligence (XAI) model to discern and analyze papers with significant impact in the field of mathematics education. To achieve this, meta-information from 29 domestic and international mathematics education journals was utilized to construct a comprehensive academic research network in mathematics education. This academic network was built by integrating five sub-networks: 'paper and its citation network', 'paper and author network', 'paper and journal network', 'co-authorship network', and 'author and affiliation network'. The Random Forest machine learning model was employed to evaluate the impact of individual papers within the mathematics education research network. The SHAP, an XAI model, was used to analyze the reasons behind the AI's assessment of impactful papers. Key features identified for determining impactful papers in the field of mathematics education through the XAI included 'paper network PageRank', 'changes in citations per paper', 'total citations', 'changes in the author's h-index', and 'citations per paper of the journal'. It became evident that papers, authors, and journals play significant roles when evaluating individual papers. When analyzing and comparing domestic and international mathematics education research, variations in these discernment patterns were observed. Notably, the significance of 'co-authorship network PageRank' was emphasized in domestic mathematics education research. The XAI model proposed in this study serves as a tool for determining the impact of papers using AI, providing researchers with strategic direction when writing papers. For instance, expanding the paper network, presenting at academic conferences, and activating the author network through co-authorship were identified as major elements enhancing the impact of a paper. Based on these findings, researchers can have a clear understanding of how their work is perceived and evaluated in academia and identify the key factors influencing these evaluations. This study offers a novel approach to evaluating the impact of mathematics education papers using an explainable AI model, traditionally a process that consumed significant time and resources. This approach not only presents a new paradigm that can be applied to evaluations in various academic fields beyond mathematics education but also is expected to substantially enhance the efficiency and effectiveness of research activities.

• Journal of Digital Convergence
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• v.18 no.4
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• pp.243-250
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• 2020

Systematic farming can be planned and managed if long-term agricultural weather information of the plantation is available. Because the greatest risk factor for crop cultivation is the weather. In this study, a method for long-term predicting of agricultural weather using the GloSea5 and machine learning is presented for the cultivation of Chinese cabbage. The GloSea5 is a long-term weather forecast that is available up to 240 days. The deep neural networks and the spatial randomforest were considered as the method of machine learning. The longterm prediction performance of the deep neural networks was slightly better than the spatial randomforest in the sense of root mean squared error and mean absolute error. However, the spatial randomforest has the advantage of predicting temperatures with a global model, which reduces the computation time.

• Education of Primary School Mathematics
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• v.16 no.2
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• pp.107-122
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• 2013

The purpose of the study is finding the effective teaching and learning methods on the concepts of figures through exploring the change of students' cognitive structures before and after the peer teaching activities. The difference of the peer teacher's and student's cognitive structures was investigated for the activities. Three teams, six students of 5th grade, were selected from the S elementary school in Boyeon. To figure out the students' cognitive structures, pre and post in-depth interviews were conducted and analyzed. Both peer teachers' and learners' cognitive structures were changed. Peer teachers' cognitive structures were changed more positively than peer learners. A consistent systematic planation and continuous teacher support and effort are needed for the activities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.90-113
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• 2021

Electronic Records Management System (ERMS) is a computer program or set of applications that is utilized for keeping up to date records along with their storage. ERMS has been extensively utilized for enhancing the performance of academic institutions. The system assists in the planning and decision-making processes, which in turn enhances the competencies. However, although ERMS is significant in supporting the process of decision-making, the majority of organizations have failed to take an initiative to implement it, taking into account that are some implementing it without an appropriate framework, and thus resulted in the practice which does not meet the accepted standard. Therefore, this study identifies the factors influencing the adoption of ERMS among employees of HLI in Yemen and the role of such adoption in the decision-making process, using the Unified Theory of Acceptance and Use of Technology (UTAUT) along with Technology, Organization and Environment (TOE) as the underpinning theories. The study conducts a cross-sectional survey with a questionnaire as the technique for data collection, distributed to 364 participants in various Yemeni public Higher Learning Institutions (HLI). Using AMOS as a statistical method, the findings revealed there are significant and positive relationships between technology factors (effort expectancy, performance expectancy, IT infrastructure and security), organizational factors (top management support, financial support, training, and policy),environmental factors (competitiveness pressure, facilitating conditions and trust) and behavioral intention to adopt ERMS, which in return has a significant relationship with the process of decision-making in HLI. The study also presents a variety of theoretical and empirical contributions that enrich the body of knowledge in the field of technology adoption and the electronic record's domain.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.4
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• pp.510-518
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• 2022

The diagnostic evaluation conducted at the national level is very important to detect underachievers in school early. This study used an artificial intelligence method to find the characteristics of underachievers that affect learning development for middle school students. In this study an artificial intelligence model was constructed and analyzed to determine whether the Busan Education Longitudinal Data in 2020 by entering data from the first year of middle school in 2019. A predictive model was developed to predict basic middle school Korean, English, and mathematics education with machine learning algorithms, and it was confirmed that the accuracy was 78%, 82%, and 83%, respectively, in the prediction for the next school year. In addition, by drawing an achievement prediction decision tree for each middle school subject we are analyzing the process of prediction. Finally, we examined what characteristics affect achievement prediction.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.181-186
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• 2022

Poisson-Boltzmann equation (PBE) is used to model problems arising from various disciplinary including bio-pysics and colloid chemistry. Therefore, to predict a numerical solution of PBE is an important issue. The authors proposed deep learning based methods to solve PBE while the computational time to generate finite element method (FEM) solutions were bottlenecks of the algorithms. In this work, we shorten the generation time of FEM solutions in two directions. First, we experimentally find certain penalty parameter in a bilinear form. Second, we applied algebraic multigrids methods to the algebraic system so that condition number is bounded regardless of the meshsize. In conclusion, we have reduced computation times to solve algebraic systems for PBE. We expect that algebraic multigrids methods can be further employed in various disciplinary to generate deep learning samples.

• Communications of Mathematical Education
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• v.36 no.1
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• pp.89-105
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• 2022

In this research, in order to obtain teaching/learning implications for effective use of questions when teaching number and operation area, the types of questions presented in chapters of number and operation area of 2015 revised elementary math textbooks and the function of questions were compared and analyzed by grade cluster. As a result of this research, the types of questions presented in chapters of number and operation area showed a high percentage of occurrences in the order of reasoning questions, factual questions, and open questions not calling for reasoning in common by grade cluster. And reasoning questions were predominant in all grade clusters. In addition, in all grade clasters, the proportion of questions acting as a function to help guess, invention, and solving problems and questions acting as a function to help mathematical reasoning were relatively high. As such, it can be inferred that the types and functions of the questions presented in chapters of number and operation area are related to the characteristics of the learning content by grade cluster. This research will be able to contribute to the preparation of advanced teaching/learning plans by providing reference materials in the questions when teaching number and operation area.

• Education of Primary School Mathematics
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• v.16 no.2
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• pp.123-145
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• 2013

This study was expected to yield the meaningful conclusions from the experimental group who took lessons based on inductive activities using GeoGebra at the beginning of proof learning and the comparison one who took traditional expository lessons based on deductive activities. The purpose of this study is to give some helpful suggestions for teaching proof to mathematically gifted elementary students. To attain the purpose, two research questions are established as follows. 1. Is there a significant difference in proof abilities between the experimental group who took inductive lessons using GeoGebra and comparison one who took traditional expository lessons? 2. Is there a significant difference in proof attitudes between the experimental group who took inductive lessons using GeoGebra and comparison one who took traditional expository lessons? To solve the above two research questions, they were divided into two groups, an experimental group of 10 students and a comparison group of 10 students, considering the results of gift and aptitude test, and the computer literacy among 20 elementary students that took lessons at some education institute for the gifted students located in K province after being selected in the mathematics. Special lesson based on the researcher's own lesson plan was treated to the experimental group while explanation-centered class based on the usual 8th grader's textbook was put into the comparison one. Four kinds of tests were used such as previous proof ability test, previous proof attitude test, subsequent proof ability test, and subsequent proof attitude test. One questionnaire survey was used only for experimental group. In the case of attitude toward proof test, the score of questions was calculated by 5-point Likert scale, and in the case of proof ability test was calculated by proper rating standard. The analysis of materials were performed with t-test using the SPSS V.18 statistical program. The following results have been drawn. First, experimental group who took proof lessons of inductive activities using GeoGebra as precedent activity before proving had better achievement in proof ability than the comparison group who took traditional proof lessons. Second, experimental group who took proof lessons of inductive activities using GeoGebra as precedent activity before proving had better achievement in the belief and attitude toward proof than the comparison group who took traditional proof lessons. Third, the survey about 'the effect of inductive activities using GeoGebra on the proof' shows that 100% of the students said that the activities were helpful for proof learning and that 60% of the reasons were 'because GeoGebra can help verify processes visually'. That means it gives positive effects on proof learning that students research constant character and make proposition by themselves justifying assumption and conclusion by changing figures through the function of estimation and drag in investigative software GeoGebra. In conclusion, this study may provide helpful suggestions in improving geometry education, through leading students to learn positive and active proof, connecting the learning processes such as induction based on activity using GeoGebra, simple deduction from induction(i.e. creating a proposition to distinguish between assumptions and conclusions), and formal deduction(i.e. proving).

• Journal of Educational Research in Mathematics
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• v.26 no.2
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• pp.205-224
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• 2016

Nowadays technological instruments are advanced to measure brain waves called EEG. Also, it is important to find some facts that cause students to have mathematic anxiety (MA) and to provide remedy programs to lessen their MA in order to help students cure MA that could contribute to negative self-efficacy toward mathematics and mathematical learning. To find how they change the MA level, a small group of 11 high school students in Suwon city participated for ten weeks at the remedy program based on students' levels of MA diagnosed by MASS instrument (Ko, & Yi, 2011) and proofread by 8 advisors who worked in related research areas. The results showed that the remedy program was effective to lessen students' MA and it should provide a long term period since some negative experiences were accumulated for a long time of his or her past schooling by others such as teachers, peers, and parents. EEG showed that students got better scores on a percent of correct answers and a reaction time and some student' EEG from a group HMA became smaller heights and width in comparison of the other groups.