• Communications of Mathematical Education
• /
• v.36 no.3
• /
• pp.417-438
• /
• 2022

The purpose of this study is to suggest implications for mathematics teaching and learning when using AI-based educational platforms that support personalized mathematics learning. To this end, we selected five platforms(Knock-knock! Math Expedition, knowre, Khan Academy, MATHia, CENTURY) and analyzed how the AI-based educational platforms for mathematics reflect the three elements(PLP, PLN, PLE) to support personalized learning. The results of this study showed that although the characteristics of PLP, PLN, and PLE implemented on each platform varied, they were designed to form PLEs that allow learners to make their autonomous decisions about learning based on PLP and PLN. The significance of this study can be found in that it has improved the understanding and practicability of personalized mathematics learning with the AI-based educational platforms.

• The Journal of the Korea Contents Association
• /
• v.7 no.4
• /
• pp.192-198
• /
• 2007

Due to the development of information telecommunication technology, e-learning industry is rapidly expanding its scope along with its production technology. The recent trend of e-learning program is likely converted from Wmv(Window Media Video) of Microsoft to Flv(Flash video), which has less capacity but better quality than other image file. It has successfully drawn the users attention since Flv can operate at most OS environments and browsers let alone with window and Lenux without extra players and codec setup. However, there is no accurate data on comparative analysis between Wmv and Flv regarding capacity, quality and production time. Therefore, the study shows the comparative data analysis on Wmv and Flv so as to set out production platform up to its idiosyncrasy.

• The Journal of the Korea Contents Association
• /
• v.21 no.12
• /
• pp.517-535
• /
• 2021

The development of digital technology has not only brought many changes to our lives, but also many changes to the online education environment. The emergence of micro-learning is to meet the needs of individual learners who hopes to receive personalized learning content immediately when they need it. Therefore, Micro-learning can be said to be 'adaptive' education. This research attempts to explore the development principles of adaptive micro-learning through literature research and case analysis. The results of the research draw four aspects of the development principles, including adaptive learning environment, adaptive learning content, adaptive learning sequence and adaptive learning evaluation, as well as detailed elements of each aspect. Micro-learning is a new form of e-learning that reflects the needs of the current society. As exploratory research, this research attempts to point out the direction for future follow-up research.

• Journal of Digital Convergence
• /
• v.13 no.7
• /
• pp.9-21
• /
• 2015

Nowadays, within the paradigm shift of e-learning, MOOCs service has been expanded among a number of global leading universities and this has affected our domestic universities' e-learning to look for different possibilities and new challenges. In consideration of our domestic educational condition, it is required to contemplate how university e-learning can be changed and developed with focus on MOOCs from the perspective of convergence development. This study suggests plans for convergence development in university e-learning based on MOOCs with conceptual model. We conducted the studies on relevant literature of university e-learning and MOOCs, expert consultation, SWOT analysis, survey for those involved of e-learning centers, etc. Through this process, we developed a final plan which integrates 'open advanced education course service', 'teaching and learning curation service', 'teaching and learning practice service', 'creative teaching and instruction method development and sharing service', and 'cloud based educational platform support service', etc with the perspective of convergence development. Also we designed convergence development plan based on MOOCs. It is assumed that the result of this research provides advanced plans for development of university e-learning and the base for further discussion of introduction and application of MOOCs service in domestic university.

• Journal of Internet Computing and Services
• /
• v.21 no.6
• /
• pp.33-39
• /
• 2020

The paper is to build recommendation systems leveraging Deep Learning and Big Data platform, Spark to predict item ratings of the Amazon e-commerce site. Recommendation system in e-commerce has become extremely popular in recent years and it is very important for both customers and sellers in daily life. It means providing the users with products and services they are interested in. Therecommendation systems need users' previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. We developed the recommendation models in Amazon AWS Cloud services to predict the users' ratings for the items with the massive data set of Amazon customer reviews. We also present Big Data architecture to afford the large scale data set for storing and computation. And, we adopted deep learning for machine learning community as it is known that it has higher accuracy for the massive data set. In the end, a comparative conclusion in terms of the accuracy as well as the performance is illustrated with the Deep Learning architecture with Spark ML and the traditional Big Data architecture, Spark ML alone.

• Journal of Power Electronics
• /
• v.10 no.6
• /
• pp.643-646
• /
• 2010

An interactive web-based teaching tool for a power semiconductor course at the University of Central Florida is presented in this paper. A novel approach is introduced using Technology Aided Design Tools (TCAD) to generate time-lapsed 2D semiconductor device cross-section embedded in a webpage using $Adobe^{(R)}$ Flash (web design tool) platform to create interactive movies that demonstrate complex device physical phenomenon. Students can step through the interactive movies forward, backward, pausing, or looping. Each step represents a giving bias condition. Current-voltage plots are represented along with the semiconductor device and a visual point is placed on the IV curve to indicate the current bias conditions. The changes are then reflected in the 2D cross-section movie area and the IV plot. This tool was implemented in a classroom setting to augment the lectures or for discovery learning.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.12 no.6
• /
• pp.672-680
• /
• 2019

This paper proposes "A Smart Farm Environment Optimization and Yield Prediction Platform based on IoT and Deep Learning" which gathers bio-sensor data from farms, diagnoses the diseases of growing crops, and predicts the year's harvest. The platform collects all the information currently available such as weather and soil microbes, optimizes the farm environment so that the crops can grow well, diagnoses the crop's diseases by using the leaves of the crops being grown on the farm, and predicts this year's harvest by using all the information on the farm. The result shows that the average accuracy of the AEOM is about 15% higher than that of the RF and about 8% higher than the GBD. Although data increases, the accuracy is reduced less than that of the RF or GBD. The linear regression shows that the slope of accuracy is -3.641E-4 for the ReLU, -4.0710E-4 for the Sigmoid, and -7.4534E-4 for the step function. Therefore, as the amount of test data increases, the ReLU is more accurate than the other two activation functions. This paper is a platform for managing the entire farm and, if introduced to actual farms, will greatly contribute to the development of smart farms in Korea.

• Communications of Mathematical Education
• /
• v.36 no.1
• /
• pp.1-21
• /
• 2022

The purpose of this study is to analyze the current situation of EduTech, which is proposed as a way to build a flexible learning environment regardless of time and place according to the use of digital technology in mathematics subjects. The process of designing classes to use the EduTech platform, which is still in the development introduction stage, in public education is still difficult, and research to observe its effects and characteristics is also in its early stages. However, in the stage of preparing for future education, it is a meaningful process to grasp the current situation and point out the direction in preparation for the future in which EduTech will be actively applied to education. Accordingly, the current situation and utilization trends of EduTech at home and abroad were confirmed, and the functions and roles of EduTech platforms used in mathematics were analyzed. As a result of the analysis, the EduTech platform was pursuing learners' self-directed learning by constructing its functions so that they could be useful for individual learning of learners in hierarchical mathematics education. In addition, we have confirmed that the platform is evolving to be useful for teachers' work reduction, suitable activities, and evaluations learning management. Therefore, it is necessary to implement instructional design and individual customized learning support measures for students that can efficiently utilize these platforms in the future.

• ETRI Journal
• /
• v.46 no.4
• /
• pp.633-647
• /
• 2024

In e-commerce platforms, sentiment analysis on an enormous number of user reviews efficiently enhances user satisfaction. In this article, an automated product recommendation system is developed based on machine and deep-learning models. In the initial step, the text data are acquired from the Amazon Product Reviews dataset, which includes 60 000 customer reviews with 14 806 neutral reviews, 19 567 negative reviews, and 25 627 positive reviews. Further, the text data denoising is carried out using techniques such as stop word removal, stemming, segregation, lemmatization, and tokenization. Removing stop-words (duplicate and inconsistent text) and other denoising techniques improves the classification performance and decreases the training time of the model. Next, vectorization is accomplished utilizing the term frequency-inverse document frequency technique, which converts denoised text to numerical vectors for faster code execution. The obtained feature vectors are given to the modified convolutional neural network model for sentiment analysis on e-commerce platforms. The empirical result shows that the proposed model obtained a mean accuracy of 97.40% on the APR dataset.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.5
• /
• pp.24-32
• /
• 2011

According to accelerate the convergence of defense and information technology, there is a need for innovative change in Military e-Learning service system. In other words, It has increased the need for system integration based on standards and interoperability to develop into a network-centric information and knowledge. In this study, It would like to introduce an integrated direction Military e-Learning service system on the SOA-based video content services in the operating system for the operating model. SOA is taking advantage in integration and expansion of the unit with a process. Using it, define of video services platform architecture and define of business model based on the Imprimatur model. Based on this, it define the role of actors for video content service in each step of the operating model, that is Production model, Brokerage model and consumption model. In the operating system, it define the functions and data to control and handle the needed functionality for video content services based on the operational model.