• Journal of The Korean Association For Science Education
• /
• v.13 no.2
• /
• pp.230-246
• /
• 1993

The purpose of this study was to investigate the computer-naive children's preconceptions of computer concept, anxieties for computer, the changes in preconceptions and anxieties by computer literacy teaching, and the effect of CASI(Computer Assisted Science Instruction) on the science achievement. For this study, 42 5th graders were sampled. They were divided into two groups, experimental group(male:10, female:11) and control group(male:12, female:9). Each group was randomly assigned in the elementary school. Preconceptions about computer were examined by individual interview. Computer anxiety score was measured by questionaires. The questionaires developed in this study consisted of total 21 items measured by Chronbach ${\alpha}$ (0.93) and Total Item Correlationtp(p=0.01, r = $0.40{\sim}0.72$). Computer literacy curriculum based on children's preconceptions was developed and then was treated for experimental group as a computer literacy course. Preconceptions of computer, computer anxiety, and CASI achievements were compared between experimental group and control group in pre and post test. The results of this study are as follows; 1) children's preconceptions of computer showed various non-scientific concepts as animism and obvious visiual thinking. 2) children's misconceptions and anxieties about computer did not show significant differences in terms of learning experience of computer. 3) computer literacy had an effect on eliminating children's misconception about computer. 4) computer literacy had an effect on diminishing children's computer anxiety. 5) children's misconceptions and anxieties about computer showed significant inter-correlation. 6) children's misconceptions and anxieties about computer were appeared negative effect on CASI achievements. As the results, children's misconception and anxieties about computer had an effect on CASI acheivements. Therefore before performing CASI, more systematic computer literacy might be taught in formal education.

• Proceedings of the Safety Management and Science Conference
• /
• 2009.11a
• /
• pp.15-24
• /
• 2009

Cycling that transform human energy into mechanical energy is one of the man-machine systems out of sports fields. Benchmarking means " improving ourselves by learning from others ", therefore benchmarking toward dominant cyclist is necessary on field. the goals of this study were to provide important factors on multi-disciplines (kinematics, physiology, power, psychology) for a tailored-training program that is suitable to individual characteristics. Two cyclist participated in this study and gave consent to the experimental procedure. one was dominant cyclist (years:21 yrs, height:177 cm, mass:70 kg), and the other was non-dominant cyclist(years:21, height:176, mass:70). Kinematic data were recorded using six infrared cameras (240Hz) and QTM (software). Physiological data (VO2max, AT) were acquired according to graded exercising test with cycle ergometer and power with Wingate test used by Bar-Or et. al ( 1977) and to evaluate muscle function with Cybex. Psychological data were collected with competitive state anxiety inventory (CSAI-2) that were devised by Martens et. al (1990) and with athletes' self-management questionnaire (ASMQ) of Huh (2003). It appears that the dominant's CV(coefficient of variability) was higher than non-dominant's CV in Sports Biomechanics domain, that the dominant's values for all factors ware higher than non-dominant's values in physical, and physiological domain, and their values between cognitive anxiety and somatic anxiety were contrary to each other in psychology. Further research on multi-disciplines may lead to the development of tailored-optimal training programs applicable with key factors to enhance athletic performance by means of research including athlete, coach and parents.

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

• Education of Primary School Mathematics
• /
• v.26 no.4
• /
• pp.333-348
• /
• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• The Mathematical Education
• /
• v.61 no.2
• /
• pp.257-271
• /
• 2022

As information and communication technologies are being developed so rapidly, education research is actively conducted to provide optimal learning for each student using big data and artificial intelligence technology. In this study, using the mathematics learning data of elementary school 5th to 6th graders conducting blended mathematics classes, we tried to find out what factors predict mathematics academic achievement and developed an artificial intelligence model that predicts mathematics academic performance using the results. Math learning propensity, LMS data, and evaluation results of 205 elementary school students had analyzed with a random forest model. Confidence, anxiety, interest, self-management, and confidence in math learning strategy were included as mathematics learning disposition. The progress rate, number of learning times, and learning time of the e-learning site were collected as LMS data. For evaluation data, results of diagnostic test and unit test were used. As a result of the analysis it was found that the mathematics learning strategy was the most important factor in predicting low-achieving students among mathematics learning propensities. The LMS training data had a negligible effect on the prediction. This study suggests that an AI model can predict low-achieving students with learning data generated in a blended math class. In addition, it is expected that the results of the analysis will provide specific information for teachers to evaluate and give feedback to students.

• Journal of The Korean Association of Information Education
• /
• v.20 no.5
• /
• pp.499-506
• /
• 2016

Multi-cultural students have a variety of problems, such as the lack of Korean communication skills, learning slump and psychological anxiety. In order to solve these problems, It is developed to design a software education program for learning attitude, friendship interaction and sociality. It is developed on the basis of the major steps in the ADDIE model, Use-Needs-Design-Implementation-Share for multi-cultural elementary school students. To analyze the effects of software education Program, we chose the 15 elementary school students of 4th, 5th and 6th grade and adapted the program. Then, we analyzed the educational effects through the results of pre to post tests. Consequently, the software education program developed for this research revealed that it affected the learning attitude, friendship, sociality and programming interest of multi-cultural students.

• Journal of The Korean Association of Information Education
• /
• v.9 no.3
• /
• pp.501-510
• /
• 2005

To facilitate interaction between home robot and humans, it's urgently needed to make in-depth research in Human-Robot Interaction(HRI). The purpose of this study was to examine how children interacted with a newly developed home robot named 'iRobi' in a bid to identify how the home robot affected their psychology and the effectiveness of learning through the home robot. Concerning the psychological effects of the home robot, the children became familiar with the robot, and found it possible to interact with it, and their initial anxiety was removed. As to its learning effect, the group that studied by using the home robot outperformed the others utilizing the other types of learning media (books, WBI)in attention, learning interest and academic achievement. Accordingly, home robot could serve as one of successful vehicles to expedite the psychological and educational interaction of children.

• The Korea Journal of Herbology
• /
• v.35 no.5
• /
• pp.33-39
• /
• 2020

Objectives : Increasing evidence supports the biological and pharmacological activities of essential oils on the central nervous system such as pain, anxiety, attention, arousal, relaxation, sedation and learning and memory. The purpose of present work is to investigate the protective effect and molecular mechanism of cypress essential oil (CEO) against scopolamine (SCO)-induced cognitive impairments in C57BL/6 mice. Methods : A series of behavior tests such as Morris water maze, passive avoidance, and fear conditioning tests were conducted to monitor learning and memory functions. Immunoblotting and RT-PCR were also performed in the hippocampal tissue to determine the underlying mechanism of CEO. Results : SCO induced cognitive impairments as assessed by decreased step-through latency in passive avoidance test, relatively low freezing time in fear conditioning test, and increased time spent to find the hidden platform in Morris water maze test. Conversely, CEO inhalation significantly reversed the SCO-induced cognitive impairments in C57BL/6 mice comparable to control levels. To elucidate the molecular mechanisms of memory enhancing effect of CEO we have examined the expression of brain-derived neurotrophic factor (BDNF) in the hippocampus. CEO effectively elevated the protein as well as mRNA expression of BDNF via activation of cAMP response element binding protein (CREB). Conclusions : Our findings suggest that CEO inhalation effectively restored the SCO-impaired cognitive functions in C56BL/6 mice. This learning and memory enhancing effect of CEO was partly mediated by up-regulation of BDNF via activation of CREB.

• International Journal of Computer Science & Network Security
• /
• v.23 no.10
• /
• pp.199-208
• /
• 2023

The emergence of COVID-19 virus has shaken almost every aspect of human life including but not limited to social, financial, and economic changes. One of the most significant impacts was obviously healthcare. Now though the pandemic has been over, its aftereffects are still there. Among them, a prominent one is people lifestyle. Work from home, enhanced screen time, limited mobility and walking habits, junk food, lack of sleep etc. are several factors that have still been affecting human health. Consequently, diseases like diabetes, high blood pressure, anxiety etc. have been emerging at a speed never witnessed before and it mainly includes the people at young age. The situation demands an early prediction, detection, and warning system to alert the people at risk. AI and Machine learning has been investigated tremendously for solving the problems in almost every aspect of human life, especially healthcare and results are promising. This study focuses on reviewing the machine learning based approaches conducted in detection and prediction of diabetes especially during and post pandemic era. That will help find a research gap and significance of the study especially for the researchers and scholars in the same field.

• Journal of the Korean Society of School Health
• /
• v.36 no.3
• /
• pp.113-125
• /
• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.