• Journal of Information Technology Services
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• v.20 no.5
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• pp.147-158
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• 2021

The digital transformation and COVID-19 are also causing major changes in teaching-learning methods. The biggest change is the spread of remote training and the emergence of various innovative learning platforms. Distance education has been criticized for not meeting technology trends and field demands..However, the problem of distance education is being solved through a system that supports various interactions and collaborations and supports customized learning paths. The researcher conducted a case study on domestic and foreign learning platforms that provide non-face-to-face ICT education. Based on the case study results, the researcher presented the functional characteristics of a learning platform that effectively supports non-face-to-face learning. In common, these sites faithfully supported the basic functions of the information system. In addition to learning progress check and learning guidance, some innovative learning platforms were providing differentiated functions in practice support, performance management, mentoring, learning data analysis, curation provision, and CDP support. Most learning platforms supported one-way, superficial interaction. If the platform effectively supports a variety of learning experiences and provides an integrated learning experience thanks to the development of IT technology, user satisfaction with the learning platform, intention to continue learning, and achievement will increase.

• Communications of Mathematical Education
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• v.30 no.3
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• pp.309-333
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• 2016

This study is part of app development research based on user centered design which targets silver generation learners. The mathematical contents provided by Senior math application focus on Numeracy issues. In order to finalize the user interface and the mathematical contents which is for developing the mathematic application, teaching experiment was carried out through 9 senior learners. Also CIPP program evaluation model was used for monitoring the result of this teaching experiment. Factors such as 'educational objectives' 'requirement analysis' 'educational environment' 'curriculum' 'learning content' 'learning matter' 'interaction' 'program administration' 'supporting environment' 'satisfaction' 'study result' 'substantiality of learning' were checked and as a result the Senior Mathematic application was developed through these feedbacks.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.11
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• pp.153-161
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• 2013

Recently, there has been a tremendous increase in the availability of educational materials for foreign language learning. As part of this trend, there has been an increase in the amount of electronically mediated materials available. However, conventional educational contents developed using computer technology has provided typically one-way information, which is not the most helpful thing for users. Providing the user's convenience requires additional off-line analysis for diagnosing an individual user's learning. To improve the user's comprehension of texts written in a foreign language, we propose an intelligent learning tool based on the analysis of the user's eyeball movements, which is able to diagnose and improve foreign language reading ability by providing necessary supplementary aid just when it is needed. To determine the user's learning state, we correlate their eye movements with findings from research in cognitive psychology and neurophysiology. Based on this, the learning tool can distinguish whether users know or do not know words when they are reading foreign language sentences. If the learning tool judges a word to be unknown, it immediately provides the student with the meaning of the word by extracting it from an on-line dictionary. The proposed model provides a tool which empowers independent learning and makes access to the meanings of unknown words automatic. In this way, it can enhance a user's reading achievement as well as satisfaction with text comprehension in a foreign language.

• Information Systems Review
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• v.10 no.3
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• pp.39-64
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• 2008

As digital convergence evolves, it is expected that the market of IP-based services like VoIP and IPTV will be expanded. In particular, IPTV market is expected to attract consumers' attention through various interactive services offering a variety of experiences to consumers. Interactivity sets apart old media from new one in terms of how to mediate effects of user satisfaction. The object of this study is to investigate (1) multi-dimensional Interactivities in an interactive medium based on IP and relationship among them, and (2) significant factors affecting cognitive absorption of interactive media users. This study aims to provide implications on how to develop strategies for IP-based media including e-learning system.

• Journal of the Ergonomics Society of Korea
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• v.36 no.5
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• pp.385-394
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• 2017

Objective: The aim of this study is to investigate how to design effective virtual reality-based training (i.e., virtual training) in maritime safety and to present methods for enhancing interface fidelity by employing immersive interaction and 3D user interface (UI) design. Background: Emerging virtual reality technologies and hardware enable to provide immersive experiences to individuals. There is also a theory that the improvement of fidelity can improve the training efficiency. Such a sense of immersion can be utilized as an element for realizing effective training in the virtual space. Method: As an immersive interaction, we implemented gesture-based interaction using leap motion and Myo armband type sensors. Hand gestures captured from both sensors are used to interact with the virtual appliance in the scenario. The proposed 3D UI design is employed to visualize appropriate information for tasks in training. Results: A usability study to evaluate the effectiveness of the proposed method has been carried out. As a result, the usability test of satisfaction, intuitiveness of UI, ease of procedure learning, and equipment understanding showed that virtual training-based exercise was superior to existing training. These improvements were also independent of the type of input devices for virtual training. Conclusion: We have shown through experiments that the proposed interaction design results are more efficient interactions than the existing training method. The improvement of interface fidelity through intuitive and immediate feedback on the input device and the training information improve user satisfaction with the system, as well as training efficiency. Application: Design methods for an effective virtual training system can be applied to other areas by which trainees are required to do sophisticated job with their hands.

• The Journal of the Korea Contents Association
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• v.14 no.2
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• pp.131-145
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• 2014

This study is an empirical analysis on the relationship between social presence and online community users' continuance intention. Based on Bhattacherjee(2001)'s expectation-confirmation model (ECM) of IT continuance model, we test the influence of social presence on one's intention to continue using online communities. This study sampled 132 online community users. Research hypotheses are tested using the structural equation modelling(SEM) approach. The results of this study demonstrate that user satisfaction is influenced by perceived usefulness and perceived enjoyment. But, the confirmation of expectation did not affect user satisfaction. And, social presence has direct effects on perceived usefulness and perceived enjoyment. Further, social presence has a positive effect on users' continuance intention through mediating effect of perceived usefulness. This study suggests that perceived usefulness should be taken into account when carrying out the operating strategy of online communities.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.39-54
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• 2013

Recent explosive increase of electronic commerce provides many advantageous purchase opportunities to customers. In this situation, customers who do not have enough knowledge about their purchases, may accept product recommendations. Product recommender systems automatically reflect user's preference and provide recommendation list to the users. Thus, product recommender system in online shopping store has been known as one of the most popular tools for one-to-one marketing. However, recommender systems which do not properly reflect user's preference cause user's disappointment and waste of time. In this study, we propose a novel recommender system which uses data mining and multi-model ensemble techniques to enhance the recommendation performance through reflecting the precise user's preference. The research data is collected from the real-world online shopping store, which deals products from famous art galleries and museums in Korea. The data initially contain 5759 transaction data, but finally remain 3167 transaction data after deletion of null data. In this study, we transform the categorical variables into dummy variables and exclude outlier data. The proposed model consists of two steps. The first step predicts customers who have high likelihood to purchase products in the online shopping store. In this step, we first use logistic regression, decision trees, and artificial neural networks to predict customers who have high likelihood to purchase products in each product group. We perform above data mining techniques using SAS E-Miner software. In this study, we partition datasets into two sets as modeling and validation sets for the logistic regression and decision trees. We also partition datasets into three sets as training, test, and validation sets for the artificial neural network model. The validation dataset is equal for the all experiments. Then we composite the results of each predictor using the multi-model ensemble techniques such as bagging and bumping. Bagging is the abbreviation of "Bootstrap Aggregation" and it composite outputs from several machine learning techniques for raising the performance and stability of prediction or classification. This technique is special form of the averaging method. Bumping is the abbreviation of "Bootstrap Umbrella of Model Parameter," and it only considers the model which has the lowest error value. The results show that bumping outperforms bagging and the other predictors except for "Poster" product group. For the "Poster" product group, artificial neural network model performs better than the other models. In the second step, we use the market basket analysis to extract association rules for co-purchased products. We can extract thirty one association rules according to values of Lift, Support, and Confidence measure. We set the minimum transaction frequency to support associations as 5%, maximum number of items in an association as 4, and minimum confidence for rule generation as 10%. This study also excludes the extracted association rules below 1 of lift value. We finally get fifteen association rules by excluding duplicate rules. Among the fifteen association rules, eleven rules contain association between products in "Office Supplies" product group, one rules include the association between "Office Supplies" and "Fashion" product groups, and other three rules contain association between "Office Supplies" and "Home Decoration" product groups. Finally, the proposed product recommender systems provides list of recommendations to the proper customers. We test the usability of the proposed system by using prototype and real-world transaction and profile data. For this end, we construct the prototype system by using the ASP, Java Script and Microsoft Access. In addition, we survey about user satisfaction for the recommended product list from the proposed system and the randomly selected product lists. The participants for the survey are 173 persons who use MSN Messenger, Daum Caf$\acute{e}$, and P2P services. We evaluate the user satisfaction using five-scale Likert measure. This study also performs "Paired Sample T-test" for the results of the survey. The results show that the proposed model outperforms the random selection model with 1% statistical significance level. It means that the users satisfied the recommended product list significantly. The results also show that the proposed system may be useful in real-world online shopping store.

• International Journal of Contents
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• v.10 no.1
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• pp.54-61
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• 2014

In this paper, we propose a pioneering concept of DPD(Disaster Prevention Design) to realize a securable society in the future. Features of danger in the future society are expected to be diverse, abrupt occurring, large scale, and complicated ways. Due to increment of dangers with their features of uncertainty, interactivity, complexity, and accumulation, human-oriented design concept naturally participates in activities to prevent our society against disasters effectively. We presented DPD is an essential design activity in order to cope with dangers expected in the future societies as well as realize securable environments. DPD is also an integrated design aids including preemptive protections, rapid preparing, recovery, and interactive cooperation. We also expect these activities of DPD is effective for generation of new values in the market, satisfaction of social needs, expansion of design industry, and a novel chance for development in the future society. Throughout this paper, we submit various aspects of DPD concepts including definition, classification, scope, necessity, strategy, influencing elements, process, and its principle. We expect these concepts will be the seed and/or basement of DPD research for the future works. For the direction of study for DPD in the future, we emphasize alarm system for preemptive protection rather than recovery strategy for the damage occurred. We also need to research about progressive prevention techniques and convergence with other areas of design. In order to transfer the concept of product design from facility-oriented mechanism to human-oriented one, we should develop new kinds of city basis facilities, public-sense design concepts referred to social weak-party, e-Learning content design preparing disasters, and virtual simulation design etc. On the other hand, we have to establish laws and regulations to force central and/or provincial governments to have these DPD strategies applying their regional properties. Modern design activities are expanding to UI(user interface) content design area overcoming the conventional design concept of product and/or service. In addition, designers are recognized as art directors or life stylists who will change the human life and create the social value. DPD can be divided into prevention design, preparedness design, response design, and recovery design. Five strategies for successful DPD are Precaution-oriented, Human-oriented, Sense-oriented, Legislation, and Environment Friendly Strategies.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.