• 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 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.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.2
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• pp.11-24
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• 2022

For the questions asked for LMS-based online evaluation the professor directly set exam questions, or use the automatic question-taking method according to the level of difficulty using the question bank divided by category. Among them, it is important to manage the difficulty of questions in an objective and efficient way, above all, in the automatic question-taking method according to difficulty. Because the questions presented to the evaluators may be different. In this paper, we propose an difficulty re-adjustment algorithm that considers not only the correct rate of a problem but also the time taken to solve the problem. For this, a logistic regression classification algorithm was used of machine learning, and a reference threshold was set based on the predicted probability value of the learning model and used to readjust the difficulty of each item. As a result, it was confirmed that there were many changes in the difficulty of each item that depended only on the existing correct rate. Also, as a result of performing group evaluation using the adjustment difficulty problem, it was confirmed that the average score improved in most groups compared to the difficulty problem based on the percentage of correct answers.

• The KIPS Transactions:PartD
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• v.12D no.3 s.99
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• pp.471-480
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• 2005

Most questions made for remote examinations on web-based education system use methods of making questions using fixed questions or randomly using item pools or automatically using degree of difficulty. Particularly, automatically selection methods using degree of difficulty is the kernel of a question that objectivity of the first degree of difficulty for questions and an effective questions selection using degree of difficulty and mediateness degree of difficulty based result of examination. This paper is use automatically selection methods for examination on web-based education system. Firstly, we present new question selection algorithms as regards degree of difficulty and distribution between all units. Secondly, we present new algorithms of mediateness degree of difficulty as regards education ability of students for adjust the degree of difficulty. We identified this algorithms is more effective as compared with previously algorithms on web-based education system.

• Korean Journal of Cognitive Science
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• v.33 no.1
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• pp.77-93
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• 2022

Previous studies have reported that a plausible reference point has a greater anchoring effect than an extreme reference point. It is also known that the anchoring effect decreases when the individual's level of knowledge related to a given item is high. However, there has been no study examining the interaction of the plausibility of the reference point and the difficulty of the given question. Therefore, in this study, the effect of the reference plausibility and the difficulty of the questions on the anchoring effect were examined. The relationship between the response confidence and the anchoring effect was also examined. To do so, easy and difficult questions, plausible and extreme reference points were selected through preliminary research. The experiment was conducted following the 'standard anchoring task procedure'. As results, the extremity of the reference point and the difficulty of the question affected the size of the anchoring effect respectively. The difficulty of the question also affected the confidence of the response. Specifically, when a plausible reference point was presented and when a difficult question was presented, the anchoring effects were increased. In addition, the lower the confidence in one's performances, the greater the influence of the reference point when an extreme reference point was presented. These results show that the plausibility of the given reference point and the difficulty of the item have different effects on the magnitude of the anchoring effect and the degree of confidence. The results of this study support the attitude change perspective regarding the anchoring effect, which suggests that the anchoring effect varies depending on the characteristics of the reference point and the individual's knowledge.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.4
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• pp.47-55
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• 2004

Most questions made for remote examinations on web-based education system use methods of making questions using fixed questions or randomly using item pools or automatically using degree of difficulty. Particularly, automatically selection methods using degree of difficulty is the kernel of a question that objectivity of examination questions by degree of difficulty adjustment based result of examination. This paper is use automatically selection methods for examination on web-based education system. Therefore we present new algorithms of mediateness degree of difficulty as regards education ability of students for adjust the degree of difficulty. We identified this algorithms is more effective as compared with previously algorithms on web-based education system

• The Journal of Korean Association of Computer Education
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• v.22 no.3
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• pp.55-65
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• 2019

In programming language learning system of flip learning methods, the evaluation of cyber lectures generally proceeds from online to multiple choice questions. In this case, the questions are randomly extracted from the question bank and given to individual learners. In order for these evaluation results to be reflected in the grades, the equity of the examination question is more important than anything else. Especially in the programming language subject, the degree of difficulty that learners think can be different depending on the type of problem. In this paper, we classify the types of multiple-choice problems into two categories, and manage the difficulty level by each type. And we propose a question selection algorithm that considers both difficulty level and type of question. Considering the characteristics of the programming language, experimental results show that the proposed algorithm is more efficient and fair than the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.08a
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• pp.160-164
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• 2008

Most questions made for remote examinations on E-learning databank based selection system use methods of making questions automatically using degree of difficulty. This methods is the kernel of a question selection that degree of difficulty as make test questions, and then needs continuous management for degree of difficulty. This paper present improved algorithms for dynamically adjustment of degree of difficulty based on examination result that is more efficient set of question. We identified this algorithms is more effective as compared with previously algorithms on web-based education system.

• 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 Korea Society of Industrial Information Systems
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• v.9 no.3
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• pp.11-18
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• 2004

It is making researches in questions selection method for examination in web-based education system. Most questions made for these remote examinations use methods of making questions using fixed questions or randomly using item pools or automatically using degree of difficulty. This paper proposes a new examination questions selection algorithm in web-based education system for engineer test. Generally, Engineer test is characterized by adequate examination questions selection for degree of difficulty and equally between all units. Therefore this algorithm selected examination questions equally well as regards degree of difficulty and distribution between all units. This algorithm providers more effective education examination method as compared with previous algorithm.