• Journal of Internet Computing and Services
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• v.7 no.6
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• pp.131-141
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• 2006

In many papers, item difficulty does apply to the E-Learning system for advance learning effect. But there have to need item revision difficulty for more correct item difficulty. Also, there have to support self-directed learning process which learner can make plan and operate learning progress. In this research, I developed self-directed E-Learning system using item revision difficulty. For efficiency of system development, il is implemented and composited by component based development. In the applied result, it was able to support more correct item revision difficulty to learner. And I displayed efficiency of operation the component based self-directed learning system.

• Journal of Internet Computing and Services
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• v.10 no.3
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• pp.151-157
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• 2009

A lot of E-Learning system is supplying the existent item difficulty based learning information to learner. And learner is doing learning contents according to the fixed learning course. It is difficult for learner to get efficient learning effect. Because learner has to belong to fixed item difficulty and learning course even thought learner has different degree that understand studying in learning course. This research proposed the learner adaptive E-learning system that is able to control the item difficulty and learning course to analyze the understanding degree of learner in learning course. In this result, learner is able to improve learning effect to get rid of fixed learning course using bi-directed learning such as off-line learning.

• Journal of Korean Elementary Science Education
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• v.33 no.1
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• pp.172-180
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• 2014

The first purpose of this study is to distinguish difficult chapters in 'Speed of objects' chapter and find the factors which give difficulty to the teachers and students. Also, it attempts to compare the students' assessment scores with the degree of difficulty in teaching and also with the degree of difficulty in learning. This report is expected to help science teachers develop their PCK(Pedagogical Content Knowledge) for teaching the chapter professionally. 15 teachers who had taught the 'Speed of Objects' chapter and their 386 students took part in the survey to acquire information about the difficulties in teaching and learning. 386 students also received a test to examine their understandings of the chapter. The results of this study are as follow; First, the degree of teachers' and students' difficulty is only affected by the contents, and the degree of onerousness felt by teachers is higher than that of students. Second, The topics caused higher difficulty to teachers were 'Understanding the meaning of motion(2nd lesson)', 'Understanding the meaning and unit of speed(5th lesson)', 'Changing unit of speed(6th lesson)', 'Drawing a distance-time graph(7th lesson)', and 'Understanding the relative motion(10th). The topics that led higher difficulty to students were the contents of 5th, 6th, and 7th lessons. Third, the 'Speed of Objects' chapter can be divided into 4 types of difficulty according to the degree of teaching and learning; 'Strong difficulty', 'Learning difficulty', 'Weak difficulty', and 'Teaching difficulty'. Last, students showed low achievement to the tasks that were related with 'Strong difficulty' and 'Teaching difficulty'.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.371-378
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• 2009

A lot of learning systems are applying and verify evaluating the item difficulty to increase learner's learning effect. But most of this methods ware calculated the item difficulty when it analyze the learning result before or after learning. So, it is hard to support learning contents with changing item difficulty during learning to learner. In this research, we proposed the method that system can support learning contents to next learning to fit leaner's level immediately as apply to calculate item difficulty during the proceed learning. Through this method, learner could supported learning contents by calculated difficulty through pre-test and it caused this method was helped learner to increase learning effect.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.83-91
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• 2011

Much research has been conducted on the e-learning systems for recommending a learning content to a student based on the difficulty of it. The difficulty is one of the most important factors for selecting a learning content. In the existing learning content recommendation systems, the difficulty of a learning content is determined by the creator. Therefore, it is not easy to apply a standard rule to the difficulty as it is determined by a subjective method. In this paper, we propose an ontology-based method for determining the difficulty of a learning content in order to provide an objective measurement. Previously, ontologies and knowledge maps have been used to recommend a learning content. However, their methods have the same problem because the difficulty is also determined by the creator. In this research, we use an ontology representing the IS-A relationships between words. The difficulty of a learning content is the sum of the weighted path lengths of the words in the learning content. By using this kind of difficulty, we can provide an objective measurement and recommend the proper learning content most suitable for the student's current level.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.87-88
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• 2012

• Science of Emotion and Sensibility
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• v.17 no.1
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• pp.3-12
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• 2014

Quick and accurate learning of diverse electronic products has become an important daily task. In particular, software occupies core status in the control and operation of the products. This research tested the effects of learning method, schedule, and task difficulty in the learning of software. Using 2 (learning method: experiential vs. verbal) ${\times}$ 2 (learning schedule: spaced vs. massed) ${\times}$ 2 (difficulty: easy vs. difficult) between-subjects design, Experiment 1 tested participants' learning of file control using Windows Movie Maker. There was no effect of learning schedule on task completion time, but participants in experiential learning were faster in the completion of evaluation task compared with those in verbal learning condition. Importantly, as task difficulty increases participants in verbal condition showed markedly lower performance than those in experiential condition, which suggests that experiential learning is more effective with more difficult learning task. That is, in case of learning simple operation of software verbal learning using linguistic manual or instruction could be sufficient; on the other hand in case of learning complex operation learning from experience or tutorial mode would be more effective. Additional studies which manipulated task difficulty (Expt. 2) and inter-trial learning interval (Expt. 3) did not produce meaningful results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.203-208
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• 2011

Most of the online learning systems provide information that is based on the item difficulty what is custumized to learner. And the same learning process is performing to the same learning level learners. But, the degree of understanding of the same learning contents can be different even if the learner's level is same. Therefore, it is difficult to represent an effective learning experience because the learning is progressed by the determined difficulty of learning and the learning process even thought the provided content is difficult to understand. So we can control the learning difficulty during learning in order to escape from a uniform learning that online learning is provided. In this paper, we proposed a contents reorganization method for Self-Directed Learning. In this way, learners can understand their own level and customize the difficulty of learning. And then we expect the higher learning effect and satisfaction degree.

• 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.

• English Language & Literature Teaching
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• v.15 no.4
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• pp.75-94
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• 2009

This study was designed to assess demotivation factors and compare the factor between two secondary school student groups. It furthermore examined how the factors related to students' L2 proficiency. A 31-item questionnaire was completed by 407 junior (JH) and senior high school (SH) students. Five factors were extracted through the principal axis factoring: Teachers' competence and teaching styles, Dissatisfaction with English classes and grading system, Difficulty of learning English, Lack of motivation and interest in learning English, and Inadequate learning contents. Although both JH and SH students did not perceive their English teacher as a strong attribution of demotivation, Difficulty of learning English and Dissatisfaction with English classes and grading system were the two strongest demotivating factors. When compared the overall mean scores of each factor between JH and SH groups, significant differences were found in all factors except Factor 4, with SH students reporting stronger demotivation. JH students attributed their demotivation to Difficulty of learning English, while SH students attributed that to Dissatisfaction with English classes and grading system. Both groups tended to attribute their demotivation to external forces. The study also showed that several demotivation factors related negatively to L2 proficiency.