• Journal of Digital Convergence
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• v.6 no.3
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• pp.13-22
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• 2008

The purpose of this study is to explore 'Flow Experience' of those studying English speaking and listening in the e-Learning environment. The exploration of flow experience in this study is based on the literature research of Csikszentmihalyi's flow models and other studies. There have been many studies on flow experience focusing on arts, leisure and sports in accordance with Csikszentmihalyi's original theory, however, his flow theory has recently been adapted to the educational field. Nonetheless, it is in the e-learning environment, rather than the face-to-face traditional teaming environment, that there is not enough flow state measurement scale. Therefore, it is important to develop as a stepping stone a flow state scale for those who study English speaking and listening by the cyber-native-speaker on e-Learning environment to improve their satisfaction and achievement.

• Journal of Korean society of Dental Hygiene
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• v.16 no.4
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• pp.643-649
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• 2016

Objectives: The purpose of the study was to investigate the relationship between professor trust and learning flow in allied health students and to provide the basic data for the improvement of curriculum and teaching method. Methods: A self-reported questionnaire was filled out by 263 allied health students. The questionnaire consisted of 27 questions of professor trust by Likert 5 scale and 29 questions of learning flow by Likert 6 scale. Cronbach's alpha in this study was 0.97 in professor trust, 0.96 in learning flow. The data were analyzed using descriptive statistics, independent t-test, one-way ANOVA, correlation and stepwise multiple regressions. Results: The score was 3.72 in professor trust and 3.46 in learning flow. There were positive correlations between learning flow and professor trust including intimacy, professional, teaching skills and leadership. Stepwise multiple regression analysis revealed that learning flow was related with professor trust. Conclusions: The learning flow depends on professor trust. Based on the research, improvement directions that put strong emphasis on low section of professor trust and learning flow must be placed.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.28 no.2
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• pp.54-65
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• 2015

Objective : This Study was conducted in order to develop the Learning Satisfaction (LS)and Learning Flow Scale(LFS) for Korean Medical Ophthalmology & Otolaryngology & Dermatology and to investigate the reliability and validity. Methods : LF and LFS were administered to 44 students and 20 resident physicians . We examined the test-retest reliability, internal consistency and perfomed factor analysis. Result : 1. Test-retest reliability were reliable on LS and LFS for Korean Medical Ophathalmology & Otolaryngology.2. Test-retest reliability were unreliable comparatively on LS and LFS for Korean Medical Dermatology.3. internal consistency were all reliable in LS and LFS Korean Medical Ophthalmology & Otolaryngology & Dermatology.4. factor analysis were reliable only LS of Korean Medical Dermatology. Conclusions : LS and LFS has reliability to measure learning satisfaction and learning flow of Korean Medical students. But there is no evidence about validity of LS and LFS for Korean Medical Ophthalmology & Otolaryngology & Dermatology.

• Journal of Korean society of Dental Hygiene
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• v.16 no.5
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• pp.687-693
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• 2016

Objectives: The purpose of the study was to investigate the influence of instructor trust on learning flow and academic achievement in dental hygiene students. Methods: A self-reported questionnaire was completed by 202 dental hygiene students in Gyeongnam from July to August, 2016. A five point Likert scale questionnaire consisted of intimacy (8 items), professionalism (8 items), teaching ability (5 items), and leadership (6 items). Measurement of learning flow was adapted and modified by Kim et al. Academic achievement was adapted and modified by Noe and Schmitt. Cronbach's ${\alpha}$ of learning flow was 0.77 and that of academic achievement was 0.78 in the study. Data wee analyzed by t-test, one way ANOVA, $Scheff{\acute{e}}$ test, and Pearson's correlation using SPSS 23.0 program. Results: The scores of instructor trust, learning flow, and academic achievement were 4.37, 3.46, and 3.23, respectively. Academic achievement had a close positive correlation with instructor trust (r=0.46, p<0.001) and learning flow (r=0.63, p<0.001). Instructor trust had a positive correlation with learning flow (r=0.50, p<0.001). Learning flow took mediating effects on instructor trust and academic achievement. Conclusions: The trust between students and instructors had a great influence on learning flow and academic achievement. So the instructor should try to make the students concentrate on learning.

• Journal of Practical Engineering Education
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• v.6 no.1
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• pp.43-49
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• 2014

It is known that the ability and habit to "flow" could be one of the important indices to evaluate one's life. And the flow experience of university students who are in the important time transferring from the adolescence to the initial adult period will give an important effect firstly to study one's major and map out one's career path, finally to spread out the high quality life. This study investigated the university students who are in School of Mechatronic Engineering to experience the flow in the learning of PLC (Programmable Logic Controller) practice. For this, the flow condition was arranged. And in order to experience the flow in the learning of PLC, lecture plan was established, studying environment was changed and level of difficulty was controlled. Then the implications of the flow were explored by a 5-point Likert scale survey performed to 90 students.

• The Journal of the Korea Contents Association
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• v.11 no.8
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• pp.499-511
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• 2011

The purpose of this paper was to investigate differences in middle school student's after school satisfaction according to the type and the degree of flow of after school. For the purpose of this paper, a survey was conducted including a questionnaire consisting of a general characteristic of the study object, a general characteristic of the after school class, learning-flow scale, and after school satisfaction scale. The subjects were 382 students who were selected at random from middle school students. Cronbach' ${\alpha}$ and MANOVA were employed for data analysis. The results were as follows: First, the students with special-ability aptitude class were more satisfied for after school instructors than the students with subject class. Second, the students with the degree of high learning-flow showed higher satisfaction in after school class curriculum, instructors, and educational facility than the degree of low learning-flow students. Third, among the students who were high in learning-flow, the students with special-ability aptitude class were more satisfied with after-school class curriculum and educational facility compared to the students with subject class.

• Educational Technology International
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• v.16 no.2
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• pp.167-181
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• 2015

The purpose of this study was to examine the effects of using learner-created DST to communicate academic information on the creativity and flow of university students. The sample consisted of 100 undergraduate students who were assigned to either the DST group or the expository instruction group. The DST group created digital stories, and the expository group were taught using an expository instructional method. An achievement test, the Creativity Personality Scale (CPS), and the Flow State Scale (FSS) were used to collect data. The results showed that the achievement scores of the DST group were higher than those of the expository group, and the scores on the patience sub-factor of the CPS of the DST group significantly differed from those of the expository instruction group. Finally, the scores on the seven sub-factors of the FSS of the DST group differed significantly from those of the expository instruction group. The findings of this study suggest that the DST can be applied as teaching and learning method in a university class.

• The Journal of the Korea Contents Association
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• v.14 no.8
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• pp.319-328
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• 2014

The purpose of this study was to investigate circadian rhythms, learning flow, and fatigue of college students. The differences in learning flow and fatigue according to the type of circadian rhythms were also examined. The study used a descriptive survey research design. The convenience sample consisted of 224 college students from five universities. The data were collected from May to June in 2013 using the following instruments: Morningness-Eveningness Questionnaire-Korean (MEQ-K), Flow State Scale, and Fatigue Assessment Inventory. The participants in the study were mostly put in neither-type (51.8%), and only 4.0% in morning-type. The morning-type students had higher learning flow scores and lower fatigue scores than the night-type students. The types of circadian rhythms measured by MEQ-K were compared to the subjective types of circadian rhythm which was based on the participants' self evaluation. There was a difference in the measured type and the subjective type in 32.1% of the participants. The findings of this study suggest that an intervention program to help college students accurately recognize their circadian rhythms might be effective in improving learning flow and decreasing fatigue.

• Journal of Korean society of Dental Hygiene
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• v.15 no.6
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• pp.1091-1097
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• 2015

Objectives: The purpose of this study was to examine the relationship between educational satisfaction and learning participation of dental hygiene students. Method: A self-reported questionnaire was completed by 344 dental hygiene students in Gyeonggido, Chungcheongdo, and Gyeongsangdo from June 2 to 24, 2014, The questionnaire consisted of general characteristics of the subjects(3 items), choice reason of dental hygiene(7 items), educational satisfaction(22 items), and learning participation(11 items). The educational satisfaction and learning participation were assessed by Likert 5 points scale. Data were analyzed by a statistical package SPSS WIN 18.0. Results: Educational satisfaction included educational environments, teaching, educational content and educational effect. Learning participation included class flow, class participation and class readiness. Gyeongsangdo students tended to have higher score than other areas. The educational effect and teaching effect had more influence on learning participation. Conclusion: To improve the better dental hygiene education, it is important to prepare the effective educational methods and find out the influencing factors for class immersion.

• Journal of Korean Society of Water and Wastewater
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• v.35 no.1
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• pp.93-100
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• 2021

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.