• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.805-806
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• 2006

In many of ITS(Intelligent Tutoring System), they only evaluate student level or simple some student character. We propose student model for considering many student characteristics. Our student model contains student level and student's weak problem type, domain field, problem situation. We can provide optimum problem to individual student by student model.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.107-111
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• 2005

When a pedagogical agent system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. In this paper, the student model for interactive edutainment applications is proposed. This model is based on Bayesian Networks to expose constructs and parameters of rules and propositions pertaining to game and problem solving activities. This student model could be utilized as the emotion generation model for student and agent as well.

• Journal of the Korean Home Economics Association
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• v.48 no.5
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• pp.129-143
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• 2010

In order to improve the quality of student teachers' practicum, this study analysed the current situation of student teachers' field practice in child care centers. The study also assessed the practicum implementation case of H Cyber University. First, this research examined the characteristics of practicum implementation in universities. Second, a variety of strategies based on a blended model, on/off-line lectures, professor-student interaction, university-daycare centers collaboration, e-mentoring, and student teachers' portfolios that reduce the problems of student teachers' training, were explored. Finally, the practicum implementation case that adopted these factors was assessed. The results from this case study implied that student teachers' practicums based on a blended model can be an alternative method for problem-solving in existing student teachers' training in universities.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.135-144
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• 2015

This study aims to propose an evaluation model that enables cooperative learning using e-Learning system. Even if the teacher and the student are not in the same place at the same time, the team project deliverable submitted by the student to the online system can be viewed by the teacher, enabling the teacher to assess the student not only based on the project but also in many other aspects. The proposed e-learning cooperative learning model allows the development of assessment factors, using such factors in assessment of the student's activities which are performed through the e-learning system, and the feedback of the results to the student so that the student is further motivated for learning. The teacher performs a comprehensive assessment of such factors, which is considered in conjunction with the student's assessment. Implementing the cooperative learning model proposed in this study in various e-learning systems such as Moodle is expected to motivate the student for learning, produces better cooperative learning results, provides greater convenience of assessment to the teacher, and improves fairness of assessment by showing the student's activities in real time.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1285-1296
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• 2013

The fundamental concerns of this paper are to analyze the effects of student course evaluation using subject characteristic and student characteristic variables. We use a 2-level hierarchical linear model since the data structure of subject characteristic and student characteristic variables is multilevel. Four models we consider are as follows; (1) null model, (2) random coefficient model, (3) mean as outcomes model, (4) intercepts and slopes as outcomes model. The results of the analysis were given as follows. First, the result of null model was that subject characteristics effects on course evaluation had much larger than student characteristics. Second, the result of conditional model specifying subject and student level predictors revealed that class size, grade, tenure, mean GPA of the class, native class for level-1, and sex, department category, admission method, mean GPA of the student for level-2 had statistically significant effects on course evaluation. The explained variance was 13% in subject level, 13% in student level.

• International Journal of Computer Science & Network Security
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• v.21 no.2
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• pp.188-192
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• 2021

In this paper, we apply data mining techniques and machine learning algorithms using R software, which is used to predict, here we applied a regression model to test some factor on the dataset for which we assumed that it effects student performance. Model was built on an existing dataset which contains many factors and the final grades. The factors tested are the attention to higher education, absences, study time, parent's education level, parent's jobs, and the number of failures in the past. The result shows that only study time and absences can affect the students' performance. Prediction of student academic performance helps instructors develop a good understanding of how well or how poorly the students in their classes will perform, so instructors can take proactive measures to improve student learning. This paper also focuses on how the prediction algorithm can be used to identify the most important attributes in a student's data.

• Journal of the Korean Society of Earth Science Education
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• v.9 no.1
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• pp.15-26
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• 2016

The purpose of this study was to develop the student-centered elementary science textbook model and explore the applicability of the school. For this study we conducted a literature survey and analysis of domestic and foreign books, surveys, and then developed a textbook model of student-centered instruction. We have selected the three elementary school, three grades, fifty-seven students to apply the model developed textbooks. Textbook model of Earth was developed as a center of student activity. Applying the results of development of textbooks in the field, students were interested about the student-centered textbooks and they were felt that the development of textbooks were textbook that students can study on their own. Through this research it could confirm that it should be provided feedback to causes of the reflective thinking of students in the textbook for the development of student-centered textbook.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.7
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• pp.159-169
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• 1994

In this paper, we develop a prototype of ITS(Intelligent Tutoring Systems) system: UNIX TUTOR. It is designed for the purpose of teaching the UNIX beginners the principal concepts of UNIX and the shell commands using the communication between the student and the system. UNIX TUTOR engages the student in a two-way conversation that is mixed-initiative dialogue and attempts to teach the student UNIX via the Socratic method of guided discovery and the Coaching method interchangeably. And the student model is based on both the overlay model and the buggy model together. Thus TUTOR aims at teaching the students effectively whose levels of learning are different using various explanations which are determined by the student model. Because the knowledge representation for UNIX-TUTOR is based on the frame structure and the production rules it is easy to represent the complicated constructs. UNIX TUTOR is implemented on the SPARC station using X/Motif and C for cp command among 10 ones which were selected.

• Communications for Statistical Applications and Methods
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• v.30 no.5
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• pp.501-516
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• 2023

Knowledge distillation (KD) refers to extracting knowledge from a large and complex model (teacher) and transferring it to a relatively small model (student). This can be done by training the teacher model to obtain the activation function values of the hidden or the output layers and then retraining the student model using the same training data with the obtained values. Recently, relational KD (RKD) has been proposed to extract knowledge about relative differences in training data. This method improved the performance of the student model compared to conventional KDs. In this paper, we propose a new method for RKD by introducing a new loss function for RKD. The proposed loss function is defined using the area difference between the teacher model and the student model in a specific hidden layer, and it is shown that the model can be successfully compressed, and the generalization performance of the model can be improved. We demonstrate that the accuracy of the model applying the method proposed in the study of model compression of audio data is up to 1.8% higher than that of the existing method. For the study of model generalization, we demonstrate that the model has up to 0.5% better performance in accuracy when introducing the RKD method to self-KD using image data.

• Journal of the Korea Society of Computer and Information
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• v.29 no.10
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• pp.35-45
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• 2024

In this paper, we propose Multi-Scale Feature Knowledge Distillation for 3D Object Detection (M3KD), which extracting knowledge from the teacher model, and transfer to the student model consider with multi-scale feature map. To achieve this, we minimize L2 loss between feature maps at each pyramid level of the student model with the correspond teacher model so student model can mimic the teacher model backbone information which improves the overall accuracy of the student model. We apply the class logits knowledge distillation used in the image classification task, by allowing student model mimic the classification logits of the teacher model, to guide the student model to improve the detection accuracy. In KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) dataset, our M3KD (Multi-Scale Feature Knowledge Distillation for 3D Object Detection) student model achieves 30% inference speed improvement compared to the teacher model. Additionally, our method achieved an average improvement of 1.08% in 3D mean Average Precision (mAP) across all classes and difficulty levels compared to the baseline student model. Furthermore, when integrated with the latest knowledge distillation methods such as PKD and SemCKD, our approach achieved an additional 0.42% and 0.52% improvement in 3D mAP, respectively, further enhancing performance.