• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3266-3285
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• 2023

With the popularity of online learning, intelligent tutoring systems are starting to become mainstream for assisting online question practice. Surrounded by abundant learning resources, some students struggle to select the proper questions. Personalized question recommendation is crucial for supporting students in choosing the proper questions to improve their learning performance. However, traditional question recommendation methods (i.e., collaborative filtering (CF) and cognitive diagnosis model (CDM)) cannot meet students' needs well. The CDM-based question recommendation ignores students' requirements and similarities, resulting in inaccuracies in the recommendation. Even CF examines student similarities, it disregards their knowledge proficiency and struggles when generating questions of appropriate difficulty. To solve these issues, we first design an enhanced cognitive diagnosis process that integrates students' affection into traditional CDM by employing the non-compensatory bidimensional item response model (NCB-IRM) to enhance the representation of individual personality. Subsequently, we propose an affection-enhanced personalized question recommendation (AE-PQR) method for online learning. It introduces NCB-IRM to CF, considering both individual and common characteristics of students' responses to maintain rationality and accuracy for personalized question recommendation. Experimental results show that our proposed method improves the accuracy of diagnosed student cognition and the appropriateness of recommended questions.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.145-152
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• 2009

Applications such as study guides and adaptive tutoring must rely on a fine grained student model to tailor their interaction with the user. They are useful for Computer Adaptive Testing (CAT), for example, where the test items can be administered in order to maximize the information. I study how to design learner tailoring question process for recommendation. And this process can be applied the CAT and I use the formal language such as CSP in each process development for efficient process design. I use the item difficulty of item response theory for question recommendation process and learner can choice the difficulty step for learning change to control the difficulty of question in next learning. Finally, this method displayed the structural difference to compare between existent and this process.

• 전자공학회논문지 IE
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• v.45 no.4
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• pp.68-73
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• 2008

In this research, we proposed a learner tailoring question recommendation system for web based learning evaluation system. For teaming evaluation process, this system used the item difficulty Each question was stored and managed to the question bank. Item difficulty was recalculated during teaming process and feedback in next course. For learner tailoring question recommendation, learner could choice the teaming part and set the learning difficulty. In application result of proposal method, almost learner could improve learning score by controling teaming difficulty.

• Journal of The Korean Association of Information Education
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• v.14 no.3
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• pp.405-416
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• 2010

Knowledge search system is to find the question-answer documents for user question. Even highly qualified question-answer documents could be far different from those that a user want to find. The reason for this failure is that user frequently fails to make user's question to express his/her intension precisely. In this paper, we show our newly developed knowledge search system that recommends additional question-answer documents to include the contents that user want to find with high probability.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.73-83
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• 2011

The question answering system shows the answers that are input by other users for user's question. In spite of many researches to try to enhance the satisfaction level of answers for user question, there is a essential limitation. So, the question answering system provides users with the method of recommendation of another questions that can satisfy user's intention with high probability as an auxiliary function. The method using the fuzzy relational product operator was proposed for recommending the questions that can includes largely the contents of the user's question. The fuzzy relational product operator is composed of the Kleene-Dienes operator to measure the implication degree by contents between two questions. However, Kleene-Dienes operator is not fit to be the right operator for finding a question answers pair that semantically includes a user question, because it was not designed for the purpose of finding the degree of semantic inclusion between two documents. We present a novel fuzzy implication operator that is designed for the purpose of finding question answer pairs by considering implication relation. The new operator calculates a degree that the question semantically implies the other question. We show the experimental results that the probability that users are satisfied with the searched results is increased when the proposed operator is used for recommending of question answering system.

• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.21-28
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• 2018

To help users who are experiencing difficulties finding the right learning course corresponding to their level of proficiency, we developed a recommendation model for personalized learning course for Intelligence Tutoring System(ITS). The Personalized Learning Course Recommendation model for ITS analyzes the learner profile and extracts the keyword by calculating the weight of each word. The similarity of vector between extracted words is measured through the cosine similarity method. Finally, the three courses of top similarity are recommended for learners. To analyze the effects of the recommendation model, we applied the recommendation model to the Women's ability development center. And mean, standard deviation, skewness, and kurtosis values of question items were calculated through the satisfaction survey. The results of the experiment showed high satisfaction levels in accuracy, novelty, self-reference and usefulness, which proved the effectiveness of the recommendation model. This study is meaningful in the sense that it suggested a learner-centered recommendation system based on machine learning, which has not been researched enough both in domestic, foreign domains.

• Asia Marketing Journal
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• v.17 no.4
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• pp.43-61
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• 2016

This paper has identified some theoretical reasons and empirical evidence for negative scores that occur in Japan and Korea or unstable NPS scores that can be experienced. A psychological analysis of NPS results sheds light on the validity of the negative NPS scores that are often found in Japan and Korea. Usually customer experience surveys utilize a "single stimulus" such as the "company" or the "company's products / services." However, in the case of the "recommendation to friend" question of the NPS system there are two stimuli namely the "company product/service" and the influence of "friends." Hence, the survey outcomes from this question can be very different when compared with other single stimulus questions such as "overall satisfaction" or "repurchase." Japanese and Korean people may have a positive attitude towards the company but they will provide low NPS scores because they are reflecting that they would not run the risk of ruining their relationships with their friends by making a recommendation. As a result, in the NPS system these people will be labeled as "detractors" when in fact they are "ambivalent customers." Using several Japanese and Korean based marketing research industry examples and case studies, different strategies are proposed to address the issue of negative scores in the NPS system in Japan and Korea. The Customers Psyche appears to be the key determinant factors for both types of behavioural items (items with a single stimulus as well as items with two stimuli).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.4
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• pp.251-258
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• 2005

Automatic document classification may differ significantly according to the characteristics of documents that are subject to classification, as well as classifier's performance. This research identifies e-mail document's characteristics to apply a three-step preprocessing algorithm that can minimize e-mail document's atypical characteristics. In the first 5go, uncertain based sampling algorithm that used Mean Absolute Deviation(MAD), is used to address the question of selection learning document for the rule generation at the time of classification. In the subsequent stage, Weighted vlaue assigning method by attribute is applied to increase the discriminating capability of the terms that appear on the title on the e-mail document characteristic level. in the third and last stage, accuracy level during classification by each category is increased by using Naive Bayesian Presumptive Algorithm's Dynamic Threshold. And, we implemented an E-Mail Recommendtion System using a three-step preprocessing algorithm the enable users for direct and optimal classification with the recommendation of the applicable category when a mail arrives.

• Journal of Korea Multimedia Society
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• v.21 no.11
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• pp.1353-1361
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• 2018

With the availability of real-time educational data collection and analysis techniques, the education paradigm is shifting from educator-centric to data-driven lectures. However, most offline and online education frameworks collect students' feedback from question-answering data that can summarize their understanding but requires instructor's attention when students need additional help during lectures. This paper proposes a content restructure recommendation framework based on collected student feedback. We list the types of student feedback and implement a web-based framework that collects both implicit and explicit feedback for content restructuring. With a case study of four-week lectures with 50 students, we analyze the pattern of student feedback and quantitatively validate the effect of the proposed content restructuring measured by the level of student engagement.

• Journal of Information Technology Services
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• v.13 no.3
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• pp.309-321
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• 2014

Recently, due to the advent of ubiquitous computing and the spread of intelligent portable device such as smart phone, iPad and PDA has been amplified, a variety of services and the amount of information has also increased fastly. It is becoming a part of our common life style that the demands for enjoying the wireless internet are increasing anytime or anyplace without any restriction of time and place. And also, the demands for e-commerce and many different items on e-commerce and interesting of associated items are increasing. Existing collaborative filtering (CF), explicit method, can not only reflect exact attributes of item, but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers' searching effort to find out the items with high purchasability, it is necessary for us to analyse the segmentation of customer and item based on customer data and purchase history data, which is able to reflect the attributes of the item in order to improve the accuracy of recommendation. We propose the method of recommendation system using association rule and weighted preference so as to consider many different items on e-commerce and to refect the profit/weight/importance of attributed of a item. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.