• Journal of The Korean Association For Science Education
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• v.26 no.1
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• pp.16-24
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• 2006

The purpose of this study was to compare student-student verbal interaction from two type's experiments; problem-solving and task-solving. For this study, five 3rd grade middle school students were selected and their verbal interactions recorded via voice and video; and later transcribed. The student-student verbal interactions were classified as questions, explanations, thoughts, or metacognition fields, which were separated into deep versus surface learning approaches. For the problem-solving experiment, findings revealed that the number of verbal interactions is more than doubled and in particular, the number of verbal interactions using deep-approach is more than quadrupled from the point of problem-recognition to problem-solution. As for the task-solving experiment, findings showed that verbal interactions remained evenly distributed throughout the entire experiment. Finally, it was also discovered that students relied upon a more deep learning approach during the problem-solving experiment than the task-solving experiment.

• Korean Journal of Human Ecology
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• v.15 no.4
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• pp.551-561
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• 2006

The purpose of the study is to examine the influence of children's familiarity with a task and teachers' feedback on their problem solving performances. It was assumed that children's' problem solving performance would be different depending on the children's familiarity with a task and the feedback from teachers. The study also examined whether children's' problem solving competence would be different depending on their gender and age. The experiment was conducted with two experimental tools. The subjects were 58 children who were 5 to 6-year-old, enrolled in kindergartens in Koyang city in Kyunggi province. The collected data were processed with SPSS 11.0 program to get the average and the standard deviations, and with one-way ANOVA and two-way ANOVA with repeated measures. The results of the experiment are as follows; First, children's' problem solving competence was different depending on their age. Older children showed higher performance than younger children, while there's no difference in children's performance depending on their gender. Second, the teachers' feedback didn't influence children's problem solving performance. Third, children showed higher performance when familiar tasks were provided, compared to when typical tasks were provided. Finally, this study found that children's task familiarity has an influence on their problem solving performance.

• The Journal of Korean Association of Computer Education
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• v.16 no.4
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• pp.13-21
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• 2013

Recently, programming education is being actively performed in education field with development of educational programming language and teaching and learning methods for elementary students. However, programming education have limit to apply to the overall computer science curriculum, because it is performed by more than 5th grade and focused on the utilization of programming tools than problem-solving process. It is necessary to expand the range of students and educational content considered with problem-solving process for encouraging programming education in computer science. In this study, we suggest the easy-to-use programming tool for lower grade(1st grade) and robot programming task based on improvement of student's thinking ability. We use Tangible User Interface(TUI) for elementary student's(1st grade) convenience of programming and developed the robot programming task for improvement of logical thinking. As a result of this experiment, tangible programming tool can be used easily in elementary students(1st grade) and developed robot programming task is effective in improvement of logical thinking.

• Asia pacific journal of information systems
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• v.15 no.3
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• pp.41-64
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• 2005

The effects of information quality and the importance of information have been reported in the information Systems(IS) literature. However, little has been learned about the impact of data quality(DQ) on decision performance. Recognizing with this problem, this study explores the effects of contextual DQ on decision performance. To examine them, a laboratory experiment was conducted. Based on two levels of contextual DQ and two levels of task complexity, this study had a $2{\times}2$ factorial design. The dependent variables used to measure the outcomes of decision performance were problem-solving accuracy and time. The results demonstrated that the effects of contextual DQ on decision performance were significant. The findings suggest that decision makers can expect to improve their decision performance by enhancing contextual DQ. This research not only extends a body of research examining the effects of factors that can be tied to human decision-making performance, but also provides empirical evidence to validate and extend DeLone and McLean's IS success model.

• Journal of Gifted/Talented Education
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• v.22 no.1
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• pp.173-196
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• 2012

The relationship of the teacher's recommendation and performance in gifted programs for the selected student by teacher's observations and nominations was analyzed in this study. The teacher's recommendation for 9 students selected by teacher's observations and nominations in institute of Science gifted Education of C National University of Education was analyzed for this purpose. The students were categorized into 4 groups depending on the description style and contents of the teacher's recommendation and 1 student was selected from each group for analysis. It was shown that the student, a1 who was described with cognitive characteristics of the gifted in episode style in the teacher's recommendation showed the aggressive task adherence and problem solving ability. The student, a2 who was described with emotional and social characteristics in episode style attended at the class in active attitude, but the student solved the problem by the assistance of the colleagues or the teacher. The student, b1 who was listed superficially in the teacher's recommendation showed the excellent problem solving ability based on the task adherence, experiment design ability and experiment manipulation ability. The student, b2 who was listed in superficially in the teacher's recommendation attended at the class in positive and upright attitude, but the task solving was lagged behind. It is concluded from the above results that the description on the cognitive area is necessary for the teacher's recommendation to have the usefulness in selecting gifted students.

• Journal of The Korean Association For Science Education
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• v.33 no.7
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• pp.1471-1485
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• 2013

The purpose of this study was to develop a HAM (Heuristic Attention Model) by analyzing the difference between eye movements according to the science achievement of elementary school students on discrimination task. Science achievement was graded by the results of the Korea national achievement test conducted in 2012 for a random sampling of classes. As an assessment tool to check discrimination task, two discrimination measure problems from TSPS (Test of Science Process Skill, developed in 1994) which were suitable for an eye tracking system were adopted. The subjects of this study were 20 students from the sixth grade who agreed to participate in the research. SMI was used to collect EMD (eye movement data). Experiment 3.2 and BeGaze 3.2 programs were used to plan experiments and analyze EMD. As a result, eye movements of participants in discrimination tasks varied greatly in counts and duration of fixation, first fixation duration, and dwell time, according to students' science achievement and difficulty of the problems. By the analysis of EMD, strategies of the students' problem-solving could be found. During problem solving, subjects' eye movements were affected by visual attention; bottom-up attention, top-down attention and convert attention, and aflunter attention. In conclusion, HAM was developed, and it is believed to help in the development of a science learning program for underachievers.

• Journal of the Korea Society of Computer and Information
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• v.27 no.8
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• pp.1-7
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• 2022

With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.146-152
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• 2002

Mold level control has been a major control task for continuous casting plants. The system involves nonlinearities such as stick-slip friction in the sliding gate, time-delay, friction force variations between molten steel and the inner wall of mold, and nozzle logging/unclogging. These complex problems should be solved to control mold level for steel cast. In this paper, we propose a neural fuzzy mold level control technique for solving these complex problems and give experiment studies to show the mold level control in continuous casting process.

• Korean Journal of Cognitive Science
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• v.13 no.2
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• pp.47-59
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• 2002

Three experiments were conducted to investigate whether different types (common vs. uncommon) of prior-examples entail different effects in creative problem solving, and whether types/levels (rich or lean. common or uncommon) of background knowledge interact with types of prior-examples. It was found that the example types and the types/levels of background knowledge do interact and have some differential effects on generating novel and useful ideas. In Experiment 1 and 2. uncommon examples had a positive effect - generating many novel and useful ideas. regardless of background knowledge types. while common examples had positive effects, only when the background knowledge was somewhat uncommon In Experiment 3 it was also found that types (irrelevant,. single common. single uncommon, or multiple common + uncommon) of background knowledge seemed to influence differently on the ease of finding solutions: when background knowledge is diverse or not directly related to the task problem, uncommon prior examples produced much greater number of novel ideas than it was with single common or sin91e uncommon background knowledge. Implications of the present study were discussed. in relation to mental sets and fixation.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.4
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• pp.145-152
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• 2020

MRC (Machine reading comprehension) is the AI NLP task that predict the answer for user's query by understanding of the relevant document and which can be used in automated consult services such as chatbots. Recently, the BERT (Pre-training of Deep Bidirectional Transformers for Language Understanding) model, which shows high performance in various fields of natural language processing, have two phases. First phase is Pre-training the big data of each domain. And second phase is fine-tuning the model for solving each NLP tasks as a prediction. In this paper, we have made the Patent MRC dataset and shown that how to build the patent consultation training data for MRC task. And we propose the method to improve the performance of the MRC task using the Pre-trained Patent-BERT model by the patent consultation corpus and the language processing algorithm suitable for the machine learning of the patent counseling data. As a result of experiment, we show that the performance of the method proposed in this paper is improved to answer the patent counseling query.