• Journal of the Korean Data and Information Science Society
• /
• v.16 no.2
• /
• pp.349-357
• /
• 2005

Principal component analysis(PCA) was carried out to evaluate the water quality with the monitering data collected from 1997 to 2003 along the coastal area of Ulsan, Korea. To enhance evaluation and to complement descriptive power of traditional PCA, optimal scaling was applied to transform the original data into optimally scaled data. Cluster analysis was also applied to classify the monitering stations according to their characteristics of water quality.

• 대한예방의학회:학술대회논문집
• /
• 1994.02b
• /
• pp.332-335
• /
• 1994

Compared to other variables being considered in therapeutics, patient compliance has long been given minor attention although it affects every aspect of medical care; Limited methodology of compliance measurement, in particular, has hampered major progress in research, and pre-conceptions have been reiterated. However, there is a recent surge in interest derived from new data revealed by reliable methods, i.g. continuous medication (compliance) monitoring. The visualization of dynamics in drug regimen compliance over time offers unique opportunities, both to scientific drug evaluation and therapeutics in medical practice. New perspectives related to the descriptive and explanatory side of the problem are outlined by giving examples from various therapeutic fields.

• International Journal of Internet, Broadcasting and Communication
• /
• v.16 no.1
• /
• pp.36-46
• /
• 2024

This study is a descriptive survey to determine the effects of fundamentals of nursing skills education with peer evaluation on self-leadership, self-directed learning ability, and learning attitude. The factors affecting self-directed learning ability were peer evaluation, self-leadership, and learning attitude (F=118.81, p<.001), with an explanatory power of 50.4%. The factors affecting learning attitude were peer evaluation, self-leadership, and self-directed learning ability (F=48.89, p<.001), with an explanatory power of 29.5%. Based on the results of this study, we believe that it is necessary to apply various teaching methods such as peer evaluation and promote self-leadership to improve self-directed learning and learning attitude.

• Journal of the Ergonomics Society of Korea
• /
• v.32 no.3
• /
• pp.237-243
• /
• 2013

Objective: The present study developed an estimation method of boundary zone representative humanoids(hereafter, EBZ method) using descriptive statistics on the design target population. Background: The boundary zone method(hereafter, BZ method) generates representative humanoids at a boundary zone that statistically accommodates a designated percent of the design target population; however, the BZ method has a practical limitation because it requires a large scale anthropometric database on the design target population. Method: The EBZ method developed in the present study consisted of 3 steps. In the first step, the boundary zone of accommodating a designated percent(e.g., 90%) is formed under the assumption of normal distributions for anthropometric sizes. In the second step, cases that fall within the boundary zone are estimated using descriptive statistics(mean, standard deviation, and covariance) on the design target population. In the last step, K-mean cluster analysis is conducted for the cases, and representative humanoids are selected from each of clusters. Results: Evaluation results showed that mean accommodation percent of the EBZ method was 90.9%(range: 90.8~91.1%) which is similar to the target percent(90%). In addition, standard deviation of accommodation percent for 100 repetitions was 0.1%. Lastly, the number of representative humanoids generated by the EBZ method(n = 20) was similar to the BZ method(n = 16). Conclusion: The EBZ method can generate representative humanoids which accommodate a designated percent of the design target population using descriptive statistics. Application: The EBZ method can be utilized in the generation of humanoids for ergonomic design and evaluation of products when the large scale anthropometric database on the design target population is not existed.

• The Mathematical Education
• /
• v.63 no.2
• /
• pp.165-186
• /
• 2024

Despite the growing attention on artificial intelligence-based automated scoring technology as a support method for the introduction of descriptive items in school environments and large-scale assessments, there is a noticeable lack of foundational research in mathematics compared to other subjects. This study developed an automated scoring model for two descriptive items in first-year middle school mathematics using the Random Forest algorithm, evaluated its performance, and explored ways to enhance this performance. The accuracy of the final models for the two items was found to be between 0.95 to 1.00 and 0.73 to 0.89, respectively, which is relatively high compared to automated scoring models in other subjects. We discovered that the strategic selection of the number of evaluation categories, taking into account the amount of data, is crucial for the effective development and performance of automated scoring models. Additionally, text preprocessing by mathematics education experts proved effective in improving both the performance and interpretability of the automated scoring model. Selecting a vectorization method that matches the characteristics of the items and data was identified as one way to enhance model performance. Furthermore, we confirmed that oversampling is a useful method to supplement performance in situations where practical limitations hinder balanced data collection. To enhance educational utility, further research is needed on how to utilize feature importance derived from the Random Forest-based automated scoring model to generate useful information for teaching and learning, such as feedback. This study is significant as foundational research in the field of mathematics descriptive automatic scoring, and there is a need for various subsequent studies through close collaboration between AI experts and math education experts.

• Journal of the Korean Home Economics Association
• /
• v.44 no.11
• /
• pp.67-78
• /
• 2006

Self-concept is a multi-dimensional characteristic including self-image and self-evaluation. Self-image is the descriptive side of self-concept and is composed of the real self-image and the ideal self-image. Self-evaluation is the appraisable side self-concept and is composed of the internal self-concept, external self-concept and total score of self-concept. The purposes of this study were to determine the correlation of self-image and self-evaluation and to identify the effects of self-image and self-evaluation on consumption value. The data were collected from 237 college women residing in Gwangju using a questionnaire to investigate real/ideal self-image, self-evaluation and consumption value. The results were analyzed with factor analysis, Pearson's correlation and multiple regression analysis using statistical program SPSS 10.0. The results of this research were as follows. 1. The real self-image and ideal self-image had a significant correlation with internal self-concept, external self-concept and total score of self-concept. It was ensured that self-image and self-evaluation are one side of the multidimensional self-concept. 2. The real/ideal self-image and internal/external self-evaluation had a significant influence on consumption value. The real self-image and external self-evaluation were the most important variables explaining the consumption value.

• Journal of the Korean Society of Clothing and Textiles
• /
• v.26 no.1
• /
• pp.83-94
• /
• 2002

This study was intended to identify fabric impact on decision-making process for clothing purchase, to determine evaluation factors of clothing, and to analyze consumer's preference for fabric on clothing purchase. 396 questionnaires distributed to college students were analyzed by descriptive statistics, oneway ANOVA, correlation, factor analysis and multidimensional scaling. The results are as follows; (1) Fabric impacted on the pre-purchase evaluation and the post-purchase process. (2) 4 factors - physical properties, outer-consciousness, self-satisfaction, and appropriateness- were determined as evaluation factors. Though fabric was included in the physical properties, fabric presented high correlations with other evaluative elements. (3) The most preferred fabric was being composed of natural fiber in fiber contents and having softness in sensation. As a result of multidimensional scaling, 2 dimensions of fabric sensation were developed as 'soft-hard'and 'thin-thick'.

• Proceedings of the ESK Conference
• /
• 1997.10a
• /
• pp.372-375
• /
• 1997

Classivication scheme of usability element for the evaluation of consumer electronic product was developed in this study. Using hierachical structuring and clustering methods, usability element of consumer products interface is developed both for the performance perspective and for the image/appeal perspective of a product. Perfoormance element included variables such as simplicity, directness, learn- ability, flexibility, user support and effectiveness. Image/Appeal element included variables such as sensibility, descriptive impression, evaluation of appeal, and attitude towards the product. The classifi- cation scheme developed in this study is found to be comprehensive and robust relative to other existing paradigms. They can be effectively used and applied for the usability evaluation of consumer electronic products.

• Journal of the Korean Society of Food Culture
• /
• v.31 no.5
• /
• pp.506-514
• /
• 2016

The objective of this study was to investigate the effect of sample presenting types on the sensory characteristics of chilled buckwheat noodle soup (Naeng-myeon). Generic descriptive analysis was performed for evaluating only stock (system 1), only noodle (system 2) and stock with noodle (system 3). Eight kinds of commercially available Naeng-myeon were samples of interest. Ten female descriptive panelists participated. ANOVA and regression analysis were used for data analysis. In the training sessions, ten sensory properties were developed for system 1, four were additionally established for noodle. In each system, the 8 products showed significantly different intensities in almost all sensory attributes like darkness of stock, overall flavor, sweetness, saltiness, sourness. When integrating the two systems, sample presenting types showed significant difference for the seven sensory attributes, especially saltiness, sourness, beef flavor tended to be rated more strongly in system 1 than system 3.

• Korean Journal of Plant Resources
• /
• v.28 no.6
• /
• pp.734-742
• /
• 2015

The purpose of this research was to find the most appropriate process for making yacon leaf tea. We applied both green tea and black tea brewing and fermenting methods for producing yacon leaf tea. This research included consumer test and descriptive analysis of professional trained panel on yacon tea tastes and flavors. Both consumer test and descriptive evaluation preferred yacon tea produced by black tea brewing and fermenting methods because it tasted less bitter and had a sweet, delicate taste. According to orthogonal transformation resulting from varimax rotation, first principal component was positively affected by black tea brewing and fermenting methods, while it was negatively affected by green tea brewing and fermenting methods. As a result, the study concluded black tea brewing and fermenting methods were appropriate for producing yacon leaf tea.