• 산경연구논집
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• 제9권2호
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• pp.65-72
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• 2018

Purpose - One of the biggest problems in the e-learning distribution process is the lack of quality content and learners' discredit in e-learning content. In order to respond to the various demands of the corporate education field appropriately, it is necessary to search for directions of new e-learning models that are out of traditional e-learning contents. The purpose of this study is to identify recent trend issues related to corporate e-learning and to suggest directions for development. Research design, data, and methodology - Based on the literature review, trend issues that should be considered important in corporate e-learning were derived. Online survey was conducted to evaluate the importance-feasibility of each issue to 13 experts on e-learning and corporate education. The contents of the questionnaire are as follows: 1) recognition of importance and feasibility of trend issues to be considered important in the future corporate education field; 2) factors to be considered in developing future e-learning contents. Results - Six trends derived from a comprehensive literature review. The most important e-learning trends for corporate education field were 'mobile learning', 'micro learning', 'blended learning', 'social learning', 'adaptive learning', 'engaged learning'. As a result of evaluating the importance and feasibility of each issue, experts point out that 'mobile learning' and 'micro learning' should be actively considered for introduction and utilization at present. In addition, 'social learning' and 'blended learning' need to be actively considered in the near future. On the other hand, experts recognized that 'adaptive learning' and 'engaged learning' need to be prepared from a long-term perspective. Conclusions - There are two main reasons for this result. First, in corporate e-learning, it is important to 1) be able to update on time, 2) the connection with the workplace is important. Second, it requires realistic verification of the expected performance of the learning model. To be considered part of the future are as follows: First, the value and effectiveness of the new e-learning type should be studied. Seconds, e-learning contents should be developed through adopting SAM or Agile methodology. Through this process, we would be able to enhance the quality in e-learning content.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2009년도 정보통신설비 학술대회
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• pp.382-386
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• 2009

Online or sequential learning is one of the most basic and powerful method to train neuron network, and it has been widely used in disease detection, weather prediction and other realistic classification problem. At present, there are many algorithms in this area, such as MRAN, GAP-RBFN, OS-ELM, SVM and SMC-RBF. Among them, SMC-RBF has the best performance; it has less number of hidden neurons, and best efficiency. However, all the existing algorithms use signal normal distribution as kernel function, which means the output of the kernel function is same at the different direction. In this paper, we use multi-variable normal distribution as kernel function, and derive EKF learning formulas for multi-variable normal distribution kernel function. From the result of the experience, we can deduct that the proposed method has better efficiency performance, and not sensitive to the data sequence.

• International Journal of Advanced Culture Technology
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• 제7권2호
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• pp.209-217
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• 2019

Due to the development of artificial intelligence and image recognition technology that play important roles in the field of 4th industry, office automation systems and unmanned automation systems are rapidly spreading in human society. The proposed algorithm first finds the variances of the differences between the tile values constituting the learning characters and the experimental character and then recognizes the experimental character according to the distribution of the three learning characters with the smallest variances. In more detail, for 100 learning data characters and 10 experimental data characters, each character is defined as the number of black pixels belonging to 15 tile areas. For each character constituting the experimental data, the variance of the differences of the tile values of 100 learning data characters is obtained and then arranged in the ascending order. After that, three learning data characters with the minimum variance values are selected, and the final recognition result for the given experimental character is selected according to the distribution of these character types. Moreover, we compare the recognition result with the result made by a neural network of basic structure. It is confirmed that satisfactory recognition results are obtained through the processes that subdivide the learning characters and experiment characters into tile sizes and then select the recognition result using variances.

• 유통과학연구
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• 제21권7호
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• pp.33-40
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• 2023

Purpose: Higher Order Thinking Skills is one of the important aspects in education that must be mastered by the students in order to be qualified in competing at international level. Success in mastering HOTS among the students is always linked to preparation of a good and conducive learning environment. However, does this connection impacts the students' HOTS achievement? Therefore, this research is carried out in order to evaluate the relationship between HOTS and learning environment with the main focus on Accounting Principle Elective Subject (MPEI PP). Research design, data and methodology: Research in the form of correlation is implied in this study and it involves 59 Form 5 students that has learned all syllabus in Form 4's MPEI PP. Results: Evaluation of HOTS level is based on Taxonomy Bloom that covers applying skill, analysing skill, evaluating skill, and creating skill. Result from data analysis found that there is a very weak correlation (r = 0.02) between the two variables with regression equation of average grade point = 75.023 + (-.273) Learning Environment. Conclusion: Thus, a non-significant relationship between HOTS and learning environment is successfully proven through correlation and regression statistical analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• 유통과학연구
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• 제15권8호
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• pp.65-73
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• 2017

Purpose - Although e-learning has this advantage, currently many organizations have failed to recognize the necessity for basic e-learning educational training. It follows that practitioners working in the above organizations face the difficulty of having to find educational training processes of boosting their capabilities by themselves, rather than being able to utilize the educational training processes offered by e-learning. So of their own accord, learners have considered the necessity of information relating to being able to choose between high quality educational training processes. The purpose of this study is to propose opening e-learning content information for enabling an efficient choice of learning processes related to e-learning. Research design, data, and methodology - To pinpoint the items of e-learning content information, the study was initiated according to the following process. First, information relating to e-learning content (offered on e-learning websites) was researched. Second, based on the items of information which emerged from the research, selection and validity verification took place with 5 e-learning specialists as the subjects. Third, the opinions of adult learners at K University were collated relating to the items of information which emerged from the research. Results - The e-learning content information was comprised of 16 items in order to improve the choosing process for learner's e-learning contents. The analysis results showed that when learners were choosing e-learning processes, the most highly considered item was 'mobile support' (4.35). Following this (in order) were 'tuition fees' (4.30), 'certificate issuing' (4.23), and 'awareness of educational institution' (4.18). The least considered items were 'recruiting learners' (3.01) and 'tutor support' (3.18). Conclusions - The 16 items of e-learning content information in this study, were deemed to be helpful to learners in providing them with a choice of desirable e-learning process when this process was offered to them. Following this, there is a need for service institutions offering e-learning processes to make public the information suggested by this study. Research into educational methods additionally points to a necessity for not only e-learning forms, but also offline educational methods and a combination of blended learning to be offered and run parallel to e-learning.

• 유통과학연구
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• 제14권5호
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• pp.5-16
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• 2016

Purpose - This study was to empirically validate the mediating roles of psychological ownership and learning goal orientation in the relationships of managerial coaching behaviors and organizational citizenship behaviors/creative behaviors of employees. Research design, data, and methodology - A total of 270 employees in the Korean distribution industry were surveyed on-line, and the results were analyzed using confirmatory factor analysis and structural equational modeling. Results - The study confirmed prior research results that managerial coaching behaviors were related positively to employees' psychological ownership and learning goal orientation, both of which were associated positively with their organizational citizenship behaviors and creative behaviors respectively. It revealed the complete mediating effect of psychological ownership between managerial coaching and organizational citizenship behaviors and that of learning goal orientation between managerial coaching and creative behaviors. Conclusions - Psychological ownership was found to play an important role in the relationship between managerial coaching behaviors and organizational citizenship behaviors. It gives some practical implication regarding the higher turn-over intention rate of the distribution industry, in that promoting psychological ownership through managerial coaching behaviors could reduce the turn-over intention rate.

• 응용통계연구
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• 제27권3호
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• pp.445-459
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• 2014

일반적으로 특정한 작업에 익숙해진다는 것은 그 작업에 투입되는 노력에 비해 산출되는 성과가 보다 뚜렷해진다는 것을 의미한다. 동일한 양이나 정도의 노력을 들여 특정한 작업을 반복적으로 수행하게 되면 초기 시점보다 원하는 성과를 기대 이상으로 얻게 된다는 것을 의미한다. 이를 학습곡선효과(learning-curve effects)'라고 한다. 본 연구에서는 특정한 작업을 반복시행한 결과가 개수형인 형태로 측정되는 변수에 대해 (역)S자 형태를 가지는 통계적 모형을 적용하고자 한다. 다양한 모의실험 하에서의 모형의 성능을 평가하고 특정질환으로 인한 사망자 자료에 적합하였다.

• 유통과학연구
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• 제21권9호
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• pp.43-53
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• 2023

Purpose: This study aims to examine the demographic similarities and differences in objectives, methodology, and findings of previous studies in the context of gaining financial literacy using videos. This study employs a systematic review design. Research design, data and methodology: Based on the content analysis method, 15 articles were chosen from Scopus and Science Direct during 2015-2020. After formulating the research questions, the paper identification process, screening, eligibility, and quality appraisal are discussed in the methodology. The keywords for the advanced search included "Financial literacy," "Financial Education," and "Video". Results: The results of this study indicate the effectiveness of learning financial literacy using videos. Significant results were obtained when students interacted with the video content distribution. The findings of this study provide an overview and lead to a better understanding of the use of video in financial literacy. Conclusions: This study is important as a guide for educators in future research and practice planning. A systematic review on this topic is the research gap. Video learning was active learning that involved student-centered activities that help students engage with financial literacy. By conducting a systematic review, researchers and readers may also understand how extending an individual's financial literacy may change after financial education.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권2호
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.