• Journal of Science Education
• /
• v.37 no.2
• /
• pp.389-404
• /
• 2013

This study aimed to explore elementary teachers' beliefs about science teaching, science learning and the nature of science and consistency among these beliefs. Data was collected by using an open questionnaire and semi-structured individual interview. Teachers' beliefs were classified as traditional beliefs and constructivist beliefs. Traditional beliefs were further divided into content knowledge-centered beliefs and procedural knowledge-centered beliefs. The result showed that a relatively large number of teachers among the total 30 teachers had traditional beliefs about science teaching, science learning, and the nature of science(respectively 60.0%, 66.7%, 83.3%). Most of traditional beliefs were identified as content knowledge-centered beliefs. The proportion of teachers with consistent beliefs for all three aspects was 40.0%, the proportion of those with consistent beliefs for two of them (those with related beliefs) was 53.3%, the proportion of those with different beliefs for them (those with divergent belief) was 6.7%. Most of the teachers with the consistent beliefs had the content knowledge-centered beliefs of traditional beliefs. Although constructivism has been widely emphasized in science education from the 1980's, the rate of the teachers with the consistent beliefs in constructivism was as low as 6.7%.

• Journal of The Korean Association For Science Education
• /
• v.20 no.2
• /
• pp.250-261
• /
• 2000

In this study, a concept learning strategy emphasizing social consensus during discussion (SCS) was developed. The instructional effects of this strategy were compared with those of cognitive conflict strategy (CCS) and traditional instruction in the aspects of students' achievement, conceptions, communication apprehension, perceptions of science learning environment, and perceptions of small group discussion. There were no significant differences in the scores of an achievement test. For the students of low communication competency, however, the scores of the CCS group were significantly higher than those of the traditional group. The adjusted mean of the SCS group was higher than those of the other groups in a conceptions test. The social consensus strategy was also found to be more effective in learning concept for those who were more competent in communicating. No significant differences were found in the communication apprehension. The scores of three groups did not differ significantly in the subcategories of 'personal relevance' and 'students' negotiation' of the test of the perceptions of science learning environment. However, the students in the SCS group scored higher in 'participation'. The students in the SCS group perceived small group discussions more positively.

• Journal of Broadcast Engineering
• /
• v.20 no.5
• /
• pp.676-686
• /
• 2015

A 4D light field image is represented in traditional 2D spatial domain and additional 2D angular domain. The 4D light field has a resolution limitation both in spatial and angular domains since 4D signals are captured by 2D CMOS sensor with limited resolution. In this paper, we propose a dictionary learning-based superresolution algorithm in 4D light field domain to overcome the resolution limitation. The proposed algorithm performs dictionary learning using a large number of extracted 4D light field patches. Then, a high resolution light field image is reconstructed from a low resolution input using the learned dictionary. In this paper, we reconstruct a 4D light field image to have double resolution both in spatial and angular domains. Experimental result shows that the proposed method outperforms the traditional method for the test images captured by a commercial light field camera, i.e. Lytro.

• Education of Primary School Mathematics
• /
• v.14 no.2
• /
• pp.135-145
• /
• 2011

When mathematicians solve the new problems, they present the solutions to their colleagues for getting the approval. If the solution is accepted, it will be theorems. This phenomenon also happens to classrooms in elementary and secondary school. That is main reason to emphasize mathematical communication activities in mathematics education. This study is aimed to develop teaching and learning model for the improvement of mathematical communication ability, applicate the teaching and learning model to two groups and analyze for mathematical thoughts. This study is a case study of 3rd grader's activities. Eight students, four are group applied the teaching and learning model and four are traditional group. The results have been drawn as follows: First, students in the teaching and learning model group induced richer interactions for student's understanding and investigation when we compare to those of traditional group. Second, students in the teaching and learning model group have the chance to explain their thoughts. And we can observe students to clear on their thought through speaking and discussing. This model makes students to enhance organizing, forming and clearing in their mathematical thoughts and is effective to estimate of students thought for teacher.

• Journal of radiological science and technology
• /
• v.30 no.3
• /
• pp.197-203
• /
• 2007

The education of radiological technology in Korea is provided lots of information but are low effectiveness of studying due to attach importance to traditional lecture preparing for the national exam of radiological technologist. With a critique about traditional education, a new method of teaching, PBL(Problem based learning) can meet with the workplace through problems and see the real world of occupation objectively taking part in a self-directed learning and cooperative discussion process. And when become a radiological technologist as a member of current society can build up solving problems and ability of communicative competence. We suggest problem-based learning for the education of radiological technologist, hope to see make for cultivating radiological technologist of ability and improve the quality of education.

• The Transactions of the Korea Information Processing Society
• /
• v.4 no.8
• /
• pp.2070-2079
• /
• 1997

In this paper, we proposed the FNCAC (fuzzy neural call admission control) scheme of the ATM networks which used the benefits of fuzzy logic controller and the learning abilities of the neural network to solve the call admission control problems. The new call in ATM networks is connected if QoS(quality of service) of the current calls is not affected due to the connection of a new call. The neural network CAC(call admission control) system is predictable system because the neural network is able to learn by the input/output pattern. We applied the fuzzy inference on the learning rate and momentum constant for improving the learning speed of the fuzzy neural network. The excellence of the proposed algorithm was verified using measurement of learning numbers in the traditional neural network method and fuzzy neural network method by simulation. We found that the learning speed of the FNCAC based on the fuzzy learning rules is 5 times faster than that of the CAC method based on the traditional neural network theory.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.11
• /
• pp.5135-5142
• /
• 2011

A flexible mechanism is proposed in this paper to improve the dynamic response performance of a traffic flow control system in an urban area. The roads, vehicles, and traffic control systems are all modeled as intelligent systems, wherein a wireless communication network is used as the medium of communication between the vehicles and the roads. The necessary sensor networks are installed in the roads and on the roadside upon which reinforcement learning is adopted as the core algorithm for this mechanism. A traffic policy can be planned online according to the updated situations on the roads, based on all the information from the vehicles and the roads. This improves the flexibility of traffic flow and offers a much more efficient use of the roads over a traditional traffic control system. The optimum intersection signals can be learned automatically online. An intersection control system is studied as an example of the mechanism using Q-learning based algorithm, and simulation results showed that the proposed mechanism can improve the traffic efficiency and the waiting time at the signal light by more than 30% in various conditions compare to the traditional signaling system.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.374-388
• /
• 2022

Cloud computing has been one of the most critical technology in the last few decades. It has been invented for several purposes as an example meeting the user requirements and is to satisfy the needs of the user in simple ways. Since cloud computing has been invented, it had followed the traditional approaches in elasticity, which is the key characteristic of cloud computing. Elasticity is that feature in cloud computing which is seeking to meet the needs of the user's with no interruption at run time. There are traditional approaches to do elasticity which have been conducted for several years and have been done with different modelling of mathematical. Even though mathematical modellings have done a forward step in meeting the user's needs, there is still a lack in the optimisation of elasticity. To optimise the elasticity in the cloud, it could be better to benefit of Machine Learning algorithms to predict upcoming workloads and assign them to the scheduling algorithm which would achieve an excellent provision of the cloud services and would improve the Quality of Service (QoS) and save power consumption. Therefore, this paper aims to investigate the use of machine learning techniques in order to predict the workload of Physical Hosts (PH) on the cloud and their energy consumption. The environment of the cloud will be the school of computing cloud testbed (SoC) which will host the experiments. The experiments will take on real applications with different behaviours, by changing workloads over time. The results of the experiments demonstrate that our machine learning techniques used in scheduling algorithm is able to predict the workload of physical hosts (CPU utilisation) and that would contribute to reducing power consumption by scheduling the upcoming virtual machines to the lowest CPU utilisation in the environment of physical hosts. Additionally, there are a number of tools, which are used and explored in this paper, such as the WEKA tool to train the real data to explore Machine learning algorithms and the Zabbix tool to monitor the power consumption before and after scheduling the virtual machines to physical hosts. Moreover, the methodology of the paper is the agile approach that helps us in achieving our solution and managing our paper effectively.

• Journal of the Institute of Convergence Signal Processing
• /
• v.24 no.3
• /
• pp.147-152
• /
• 2023

This paper is a study of a linear algebra course taught in a high school on-demand course. Compared to the regular course, flipped learning was added to the course, and applications to signal processing related problems were covered in consideration of students' career aspirations. Overall, the class was a mixture of traditional lectures and flipped learning. Flipped learning was implemented twice. The flipped class consisted of pre-class, in-class and post-class. To verify the effectiveness of the course, a survey was conducted and most of the evaluation items were above 4. The topics of the flipped learning were Markov chains and least squares problem, which are very important in the field of signal processing.

• Steel and Composite Structures
• /
• v.52 no.3
• /
• pp.293-312
• /
• 2024

Researchers are actively investigating the potential for utilizing alternative materials in construction to tackle the environmental and economic challenges linked to traditional concrete-based materials. Nevertheless, conventional laboratory methods for testing the mechanical properties of concrete are both costly and time-consuming. The limitations of traditional models in predicting the tensile strength of concrete composited with geopolymer have created a demand for more advanced models. Fortunately, the increasing availability of data has facilitated the use of machine learning methods, which offer powerful and cost-effective models. This paper aims to explore the potential of several machine learning methods in predicting the tensile strength of geopolymer concrete under different curing conditions. The study utilizes a dataset of 221 tensile strength test results for geopolymer concrete with varying mix ratios and curing conditions. The effectiveness of the machine learning models is evaluated using additional unseen datasets. Based on the values of loss functions and evaluation metrics, the results indicate that most models have the potential to estimate the tensile strength of geopolymer concrete satisfactorily. However, the Takagi Sugeno fuzzy model (TSF) and gene expression programming (GEP) models demonstrate the highest robustness. Both the laboratory tests and machine learning outcomes indicate that geopolymer concrete composed of 50% fly ash and 40% ground granulated blast slag, mixed with 10 mol of NaOH, and cured in an oven at 190°F for 28 days has superior tensile strength.