• Journal of The Korean Association of Information Education
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• v.22 no.1
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• pp.151-158
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• 2018

The purpose of this study is to develop a system for predicting students' collaborative problem solving ability based on the ICT factors of PISA 2015 that affect collaborative problem solving ability. The PISA 2015 computer-based collaborative problem-solving capability evaluation included 5,581 students in Korea. As a research method, correlation analysis was used to select meaningful variables. And the collaborative problem solving ability prediction model was created by using the deep learning method. As a result of the model generation, we were able to predict collaborative problem solving ability with about 95% accuracy for the test data set. Based on this model, a collaborative problem solving ability prediction system was designed and implemented. This research is expected to provide a new perspective on applying big data and artificial intelligence in decision making for ICT input and use in education.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.1-15
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• 2019

One of the biggest difficulties in the vocational training field is the dropout problem. A large number of students drop out during the training process, which hampers the waste of the state budget and the improvement of the youth employment rate. Previous studies have mainly analyzed the cause of dropouts. The purpose of this study is to propose a machine learning based model that predicts dropout in advance by using various information of learners. In particular, this study aimed to improve the accuracy of the prediction model by taking into consideration not only structured data but also unstructured data. Analysis of unstructured data was performed using Word2vec and Convolutional Neural Network(CNN), which are the most popular text analysis technologies. We could find that application of the proposed model to the actual data of a domestic vocational training institute improved the prediction accuracy by up to 20%. In addition, the support vector machine-based prediction model using both structured and unstructured data showed high prediction accuracy of the latter half of 90%.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.33-40
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• 2017

The purpose of this study is to compare the differences in academic achievement, department satisfaction, and department preference according to the entrance admission for the computer education of G National University of Education. As a result of collecting and analyzing, for three years, the academic achievement data of the students who have excelled in the specific area and regular entering students, it is found that the achievement of the students who excelled in the specific area is relatively higher. The results of the questionnaire survey showed that students who excel in specific areas were more satisfied with their departments than those who entered regularly. The reason for the high academic achievement and academic satisfaction of the students in the specific area seems to be the high loyalty to the department by the students who are interested in the computer. Therefore, it can be seen that it is necessary to consider how to select students for each enriched course at the entrance examination of education universities. In addition, as the era of the fourth industrial revolution comes, the number of students wishing to pursue computer education is expected to increase.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.251-259
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• 2022

Recently, the number of mature students who apply nursing departments is increasing due to the increase in lifelong learning needs and the high employment rate of nursing jobs. This study aimed to investigate the effect of self-efficacy, satisfaction in major on college adjustment among mature nursing students. Data were collected from October to December, 2021 and a total of 148 mature student participated in this study. The result showed that the self-efficacy was 3.69±0.51, satisfaction in major was 4.07±0.50 and college adjustment was 3.42±0.50. As a result of analyzing factors, grade(β=.20, p=.001), self-efficacy(β=.43, p<.001) and satisfaction in major(β=.37, p<.001) were significant predictors of college adjustment, and the explanatory power was 54%. Mature nursing students are expected to increase in the future, and based on the results of this study, it is suggested that mature nursing students improve their college adjustment by providing intervention programs that can increase self-efficacy and satisfaction in major.

• Journal of The Korean Association For Science Education
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• v.24 no.6
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• pp.1158-1172
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• 2004

This study searched secondary school students' epistemological views and ontological views about nature and the root causes of such their views. The subjects were 156 secondary school students and data were gathered by the questionnaire developed based on preceding researches. As a result, many secondary school students had epistemological views of unknowable nature. There were various root causes of their epistemological views such as regularity and harmony of nature, predictable and circular natural phenomenon, causation, the relation between human and nature. On the other hand, a lot of secondary school students had ontological view of supernatural nature. Their religious beliefs were very powerful influence their supernatural ontological views. The nature is the object of science and the physical world. Because those views supply science educators basic backgrounds how leaners understand science class, secondary school students' epistemological views and ontological views are precious information. From now on, it is necessary to study relations between students' epistemological views and ontological views and their science class processes.

• Journal of The Korean Association For Science Education
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• v.18 no.1
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• pp.1-17
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• 1998

What does mean the statement that scientific reasoning is logical? In this study, we clarify the logical structure of the scientific explanation, prediction and the process of hypothesis testing. To simplify and identify the structure of scientific explanations and prediction more clearly, we used syllogism and presented various concrete examples. Especially, we showed that the logical structure of scientific explanation was well reflected in dynamics. Based on this analysis, it can be said that the deficit of students' understanding of dynamics is because that many scientific activities are focused on prediction rather than explanation. To explain the process of hypothesis testing, we reinterpreted the Wason's selection task as two stages: the process of prediction of experimental phenomena based on the presented hypothesis, and the process of the hypothesis testing based on the predicted experimental phenomena. And we suggested the reason of the logical fallacy of 'affirming the consequent' in science was because that many scientific relationships between the variables is one-to-one relationship, and compared this suggestion with the Lawon's multiple hypothesis theory. To check out the effect of content on the deductive reasoning, we reviewed some researches about psychology and psychology of science. And to understand the role of deductive reasoning in student's scientific activities, we reviewed researches about the analysis of students' responses in the task of conceptual change or evaluation of evidence and so on.

• Journal of Gifted/Talented Education
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• v.20 no.1
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• pp.31-59
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• 2010

Previous studies reported that gifted students' capacity on mathematics had high correlations with results of the performance assessment. However, there have been few studies that develop recommending tools through the assessment that can be used to identify mathematically gifted students or analyse their applications. Then it is difficult to use them to identify mathematically gifted students practically. Therefore, this study developed the tasks and evaluation tables for the tools. And one of them was applied for four students in Grade 1 of a middle school to simulate the assessment and characteristics assessment teachers showed were analysed. As the results, the extensive and specific information on the giftedness of the students was obtained through using the tool. The gifted capacity grasped from the order, speed, and attitudes of problem-solving was identified as observing the process of solving the task.

• Korean Journal of School Psychology
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• v.18 no.1
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• pp.31-48
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• 2021

The purpose of this study was to determine whether the retention of university freshmen could be predicted using their peer relationships in a specific department. In this study, retention was defined as a student staying enrolled in their university for a certain period of time. Social relationships are formed through interaction between people, so both students' self-perceptions and others' perceptions of them must be accounted for, so we used a social network analysis that did so. We examined social networks visualizations that allowed for a rich interpretation of numerical information. Participants in this study were freshmen who enrolled in an undergraduate program in 2017, 2018, or 2019. We used the name generator method to determine how quantitative friendship network variables predicted the academic retention up to the first semester of 2020. Cox proportional hazard model analysis showed that the weighted indegree centrality with intimacy positively predicted retention. The results of this study can be used to identify and conduct interventions for students who may be likely to disenroll. However all of the students did not participate in the department, it was difficult to examine their entire peer networks. Thus, this study's results cannot be generalized because the participants are students of a specific major, so further research is needed to produce more generalizable results.

• Journal of The Korean Association For Science Education
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• v.35 no.2
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• pp.259-265
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• 2015

This study examined high school students' acceptance of evolutionary theory, evolutionary knowledge, and epistemological belief. The Christian and non-Christian students' acceptance of evolutionary theory and evolution content knowledge were compared in relation to their 'scientific epistemological views' (domain-specific) and 'evolution in relation to nature of science' (context-specific). The Christian students' evolutionary knowledge was most predicted by the theory-laden exploration of science, while the non-Christian students' scores on evolutionary knowledge were most predicted by the scientific epistemological views. In addition, the Christian students' scores on scientific epistemological views and evolution in relation to evolution were not significantly related to each other, while the non-Christian students' scores on both variables were significantly related. Furthermore, 'evolution in relation to nature of science' is the strongest predictor of both Christian and non-Christian students' acceptance of evolution.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.597-606
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• 2020

Recently, as students' programming classes are being conducted online, interest in factors that can lead to the success of online programming classes is also increasing. Therefore, in this study, online programming classes were conducted for specialized high school students using a web-based simulation programming tool through TinkerCad. In these online programming classes, students' self-regulation ability and learning flow were set as variables that influence both learning engagement and teaching presence, and the predictive power of each was analyzed. As a result, it was found that both self-regulation ability and learning flow were predictive variables for learning engagement and teaching presence, and that learning flow played a mediating role between self-regulation ability, learning engagement, and teaching presence. This study is meaningful in that it suggested that self-regulation ability and learning flow should be considered more meaningfully in online programming classes, and a practical strategy for this is presented.