• Journal of Practical Engineering Education
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• v.5 no.2
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• pp.101-107
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• 2013

Clickers that are personal response systems are a technology used to promote active learning and most research on the benefits of using clickers has shown that students become engaged and enjoy using them. But, existing clickers consisting of hardware devices and aggregation software provide simple response and aggregation function and it costs a lot. In this paper, in order to resolve the limitation of the existing clickers, we've designed and implemented the Smart Clicker consisting of a smartphone application for students and a web application & a MFC program for professors. Students can answer professor's questions with O/X or numbers or text and even ask questions with text messaging by using Smart Clicker in the classroom. Professors can see students' answers or questions immediately and check up students' response participation rate on the web page. Besides, the Smart Clicker will help professors actively engage students during the entire class period and gauge their level of understanding of the material being presented, and provide prompt feedback to student questions. As a result, we expect that quality of education will be increased.

• Journal of Biomedical Engineering Research
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• v.41 no.2
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• pp.107-119
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• 2020

The deep neural networks (DNN) that can replicate the behavior of the human expert who recognizes the characteristics of ECG waveform have been developed and studied to analyze ECG. However, although the existing DNNs can not provide the explanations for their decisions, those trials have attempted to determine whether patients have certain diseases or not and those decisions could not be accepted because of the absence of relating theoretical basis. In addition, these DNNs required a lot of training data to obtain sufficient accuracy in spite of the difficulty in the acquisition of relating clinical data. In this study, a small-sized continuous data processing DNN (C-DNN) was suggested to determine the simple characteristics of ECG wave that were not required additional explanations about its decisions and the C-DNN can be easily trained with small training data. Although it can analyze small input data that was selected in narrow region on whole ECG, it can continuously scan all ECG data and find important points such as start and end points of P, QRS and T waves within a short time. The star and end points of ECG waves determined by the C-DNNs were compared with the results performed by human experts to estimate the accuracies of the C-DNNs. The C-DNN has 150 inputs, 51 outputs, two hidden layers and one output layer. To find the start and end points, two C-DNNs were trained through deep learning technology and applied to a parameter acquisition algorithms. 12 lead ECG data measured in four patients and obtained through PhysioNet was processed to make training data by human experts. The accuracy of the C-DNNs were evaluated with extra data that were not used at deep learning by comparing the results between C-DNNs and human experts. The averages of the time differences between the C-DNNs and experts were 0.1 msec and 13.5 msec respectively and those standard deviations were 17.6 msec and 15.7 msec. The final step combining the results of C-DNN through the waveforms of 12 leads was successfully determined all 33 waves without error that the time differences of human experts decision were over 20 msec. The reliable decision of the ECG wave's start and end points benefits the acquisition of accurate ECG parameters such as the wave lengths, amplitudes and intervals of P, QRS and T waves.

• Journal of the Korean association of regional geographers
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• v.8 no.3
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• pp.380-395
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• 2002

Since the mid 1990s, in response to rapid changes in Geography subject. Geographic Information Systems (GIS) has been in central position in the UK geography curriculum. This paper discusses the roles of GIS for Geography subject curriculum and addresses main development within UK Geography curriculum since the 1990s, and investigates appropriate GIS curriculum that encourages teaching and learning of geography subject within the curriculum. To obtain these research purposes. this paper starts with the brief description of the Geography subject in the National Curriculum for England (1998) with the recent changes of Geography subject in the national exams (GSCE and A level) in the UK. This result represents a clear situation of Geography subject in the UK school education and also provides a new motivation that brings new challenges of information technology driven curriculum within the Geography subject. In turn, the interactive relationship of Geography and GIS within the current Geography curriculum is described by which the discussion of relevant GIS skills within Geography curriculum is followed. To propose the case studies that show the use of GIS for Geography education at school, Key Stages 2, 3, and 4 examples are discussed. Finally, this paper concludes with the issues that GIS benefits encourage geography teaching and learning and that potential applications can support not only the development of new teaching tools and learning strategies in geography education at schools, but also contribute to extend geographical skills and capabilities to collaborate with other subjects in school education in Korea.

• Journal of the Korean earth science society
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• v.23 no.4
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• pp.309-323
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• 2002

This qualitative study investigated preservice teachers' understandings of the ontology and epistemology underlying constructivist notions of learning through four in-depth interviews. Of the sixteen participants in a larger study, five significantly changed ontological and epistemological beliefs and eleven did not. This study focused on these eleven teachers who have hardly changed their philosophical beliefs throughout the teacher education program. Ten teachers who consistently maintained the scientific realist beliefs were presented as a composite case (Young's case). Among the eleven teachers, there was one outlier who had consistently maintained an idealist and relativist epistemological position from the beginning of the study and was subjected to another case analysis (Ben's case). These cases corroborated the assertion that each individual's deeply entrenched ontological and epistemological beliefs are very hard to change. For researchers, this study offers insights into the reasons that preservice teachers give for non-changes in their thinking about learning to teach. The study also examines preservice teachers' perceived constraints in implementing their ideal pedagogies and the influence of the teacher education program on their pedagogical beliefs changes. The benefits and influences of the M.Ed. program's theoretical coursework and the field experiences on these teachers' learning-to-teach experiences are addressed with rich data. The implications for teacher educators as well as for the instructional practices of preservice teacher education programs are discussed. This research emphasize necessity of the field-based teacher education program and the need of empowering experienced school teachers as teacher educators in teacher preparation and professional development.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.336-345
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• 2019

To extend the potential benefits of error, the current study examined factors that affect students' error perception in classroom. An experimental design was used to measure relations of classroom goal structure, feedback, and social relationships on students' perception of error. A total 316 fourth, fifth, and sixth graders attending elementary schools participated as part of their regular class curriculum. Self-reported questionnaires were administered to measure students' perception of errors and relationships with teacher and peers, then students were manipulated by classroom goal structure and feedback. Results from multiple regression suggest that students' perception of learning from error has affected by relationships with peers at the most, then relationships with teacher and the type of feedback. Students' perception of risk taking for error also affected by relationships with peers and teacher, then the classroom goal structure. However, no classroom goal structure and feedback affect on their perception of thinking about error to improve their learning as well as error strain. These results imply how classroom climate should be structured to improve perception of errors to improve student's learning.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.71-81
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• 2019

To extend the potential benefits of error, the current study examined factors that affect students' error perception in the classroom. An experimental design was used to measure relations of classroom goal structure, feedback, and social relationships on students' perception of error. A total of 316 fourth-, fifth-, and sixth-grade elementary students participated as part of their regular class curriculum. Self-reported questionnaires were administered to measure students' perception of errors and relationships with teacher and peers, and then students were manipulated by classroom goal structure and feedback. Multiple regression analysis results suggested that students' perception of learning from error was affected mostly by relationships with peers, followed by relationships with teacher and the type of feedback. Students' perception of risk taking for error was also affected by relationships with peers and teacher, followed by the classroom goal structure. However, classroom goal structure and feedback did not affect their perception of thinking about error to improve their learning as well as error strain. These results imply how the classroom climate should be structured to improve perception of errors to improve student's learning.

• KNOM Review
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• v.23 no.2
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• pp.18-28
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• 2020

With the development of the virtual community, the benefits that IT technology provides to people in fields such as healthcare, industry, communication, and culture are increasing, and the quality of life is also improving. Accordingly, there are various malicious attacks targeting the developed network environment. Firewalls and intrusion detection systems exist to detect these attacks in advance, but there is a limit to detecting malicious attacks that are evolving day by day. In order to solve this problem, intrusion detection research using machine learning is being actively conducted, but false positives and false negatives are occurring due to imbalance of the learning dataset. In this paper, a Random Oversampling method is used to solve the unbalance problem of the UNSW-NB15 dataset used for network intrusion detection. And through experiments, we compared and analyzed the accuracy, precision, recall, F1-score, training and prediction time, and hardware resource consumption of the models. Based on this study using the Random Oversampling method, we develop a more efficient network intrusion detection model study using other methods and high-performance models that can solve the unbalanced data problem.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.288-304
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• 2022

Walking is an eco-friendly and sustainable means of transportation that promotes health and endurance. Despite the positive health benefits of walking, pedestrian safety is a serious problem in Korea. Therefore, it is necessary to investigate with various studies to reduce pedestrian-vehicle crashes. In this study, the cognitive characteristics affecting pedestrian-vehicle crashes were considered by applying deep learning semantic segmentation. The main results are as follows. First, it was found that the risk of pedestrian-vehicle crashes increased when the ratio of buildings among cognitive factors increased and when the ratio of vegetation and the ratio of sky decreased. Second, the humps were shown to reduce the risk of pedestrian-related collisions. Third, the risk of pedestrian-vehicle crashes was found to increase in areas with many neighborhood roads with lower hierarchy. Fourth, traffic lights, crosswalks, and traffic signs do not have a practical effect on reducing pedestrian-vehicle crashes. This study considered existing physical neighborhood environmental factors as well as factors in cognitive aspects that comprise the visual elements of the streetscape. In fact, the cognitive characteristics were shown to have an effect on the occurrence of pedestrian- related collisions. Therefore, it is expected that this study will be used as fundamental research to create a pedestrian-friendly urban environment considering cognitive characteristics in the future.

• The Journal of Korean Academic Society of Nursing Education
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• v.2
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• pp.75-94
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• 1996

This survey study was aimed at identifying the degree of educational need of the KNOU(Korea National Open University) nursing students defined as admission purposes, satisfaction of distance learning education, learning methods, and courses after graduation. Among randomly assigned 1000 students, 320 KNOU nursing students who allowed to participate in the study completed the questionnaires. The data were analyzed using descriptive statistics, chi-square test, and t-test, Results of this study were as follows : 1. The admission purposes of the KNOU nursing students were 'in order to get a bachelor's degree(83.8%)', 'to acquire knowledge for task(61.3%)', or 'to be admitted for the graduate school (53.1%)' etc. Comparing the admission purposes by age, tow items- 'to explore new possibilities for myself' and 'excellent curriculum' showed statistically significant differences. These two items were also found to show significant differences by marital status. 2. For the media maintenance, the results showed that students use their own cassett radios(96.3%), VTR(49.4%), TV only for the study (44.1%), personal computer (3.31%), or joining Hitel (6.3%). 3. Listening rates of the radio lecture were 'over 80%(9.1%)', '50-80%(9.1%)', '20-50%(18.1%)', 'below 20%(30%)' and 'never(33.1%)', And record lecture showed listening rates as follows : 'over80%(17.2%)', '50-80%(15.9%)', '20-50%(24.4%)', 'below 20%(27,2%)' and 'never(14.4%)'. 4. The difficulties with KNOU life were 'listening radio lectures(38.8%)', studying by following teaching schedules (37.8%)', 'isolated self-study(10.3%)', and 'appearance in the attending classes(8.1%)'. 5. As for satisfaction with teaching methods, the data showed that 81.2% of the respondents were satisfied (or very satisfied) with 'attending classes' and 75%, with 'paper lectures'. On the other hand some of respondents were very dissatisfied with 'recorded lecture(12.8%)' and 'radio lecture(10.9%)' 6. The results also showed that the students want to have 'video conferencing lecture(77.2%)', 'cable TV(64.1%)' and 'CD ROM program' to improve learning effects. 7. Concerning learning attitudes, 48.8% of the students reported 'study mainly for examnination', and only 4.1% answered 'study every day with plan'. The learning attitude showed significant differences by marital status and age. The students also evaluated themelves as 'study very hard(5.9%)', 'study hard in general(41.6%)', 'study a little(40.3%)' and 'study little(11.9%)'. 8. The students responded the most effective learning material was the 'textbook (92.2%)'. 9. For the purposes of using the local center, the results showed 'for the attending classes(76.3%)', 'for the use of references(14.7%)', and 'for the study group(66.7%)'. 10. The results revealed that 20.3% of the respondents had ever experienced unregistration or temporary withdrawal, and 53.4% among them did not register more than one time. The most common reason for the unregistration was 'due to family affairs or their job (70.8%)'. 11. 88.1% of the respondents answered 'they will graduate without fail'. 12. Regarding the benefits from the KNOU graduation, respondents indicated 'graduate school admission(38.1%)', 'self-confidence in social life(17.5%)', and understanding social problems (10.9%)'. 13. 64.4% of the students showed that they have intention to enter the graduate school. The item 'changing work place' showed statistically significant differences by marital status and age.