• Therapeutic Science for Rehabilitation
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• v.13 no.1
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• pp.49-63
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• 2024

Objective : The purpose of this study was to investigate parental perspectives on cognitive rehabilitation using a combination of phenomenological research methodology and word cloud analysis. Methods : Interviews were conducted with five parents of children with developmental disabilities. Word cloud analysis was conducted using Python, and five researchers analyzed the meaning units and themes using phenomenological methods. Words with high frequency were considered as a heuristic tool. Results : A total of 43 meaning units and nine components related to the phenomenon of cognitive rehabilitation were derived, and three themes were finalized. The main themes encompassed the definition of cognitive rehabilitation, challenges associated with cognitive rehabilitation, and factors influencing the selection of a cognitive rehabilitation institute. Cognitive rehabilitation emerged as a treatment focused on improving learning, daily functioning, and cognitive abilities in children with developmental disabilities. The perceived issues with cognitive rehabilitation pertained to treatment methods, therapist expertise, and associated costs. In addition, parents highlighted the importance of therapist expertise, humane personality, and affordability of cost and schedule when choosing a cognitive rehabilitation institute. Conclusion : Parents expressed expectations for substantial improvements in their children's daily functioning through cognitive rehabilitation. However, challenges were identified in clinical practices. Going forward, we expect that cognitive rehabilitation will evolve into a better therapeutic support service addressing the concerns raised by parents.

• Korean Journal of Construction Engineering and Management
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• v.25 no.1
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• pp.50-61
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• 2024

The market for compact houses is growing due to the demand for floor plans prioritizing user needs. However, clients often have difficulty communicating their spatial requirements to professionals including architects because they lack the means to provide evidence, such as spatial configurations or cost estimates. This research aims to create a framework that can translate sketched data-driven spatial requirements into 3D building components in BIM models to facilitate spatial understanding and provide building performance analysis to aid in budgeting in the early design phase. The research process includes developing a process model, implementing, and validating the framework. The process model describes the data flow within the framework and identifies the required functionality. Implementation involves creating systems and user interfaces to integrate various systems. The validation verifies that the framework can automatically convert sketched space requirements into walls, floors, and roofs in a BIM model. The framework can also automatically calculate material and energy costs based on the BIM model. The developed frame enables clients to efficiently create 3D building components based on the sketched data and facilitates users to understand the space and analyze the building performance through the created BIM models.

• Smart Media Journal
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• v.12 no.11
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• pp.48-56
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• 2023

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

• The Mathematical Education
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• v.63 no.2
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• pp.165-186
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• 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 Korea Society of Computer and Information
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• v.29 no.3
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• pp.67-74
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• 2024

In this paper, we present a study aimed at analyzing how different rainfall measurement methods affect the performance of reservoir water level predictions. This work is particularly timely given the increasing emphasis on climate change and the sustainable management of water resources. To this end, we have employed rainfall data from ASOS, AWS, and Thiessen Network-based measures provided by the KMA Weather Data Service to train our neural network models for reservoir yield predictions. Our analysis, which encompasses 34 reservoirs in Jeollabuk-do Province, examines how each method contributes to enhancing prediction accuracy. The results reveal that models using rainfall data based on the Thiessen Network's area rainfall ratio yield the highest accuracy. This can be attributed to the method's accounting for precise distances between observation stations, offering a more accurate reflection of the actual rainfall across different regions. These findings underscore the importance of precise regional rainfall data in predicting reservoir yields. Additionally, the paper underscores the significance of meticulous rainfall measurement and data analysis, and discusses the prediction model's potential applications in agriculture, urban planning, and flood management.

• Journal of the Korea Society of Computer and Information
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• v.29 no.3
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• pp.217-225
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• 2024

To foster the core talents of the future, the development of diverse and substantial SW·AI education programs is required, and a systematic system that can assist public education in SW and AI must be established. In this study, we develop and combine SW·AI education modules to construct a SW and AI education program applicable to public education. We also establish a systematic education system and provide sustainable SW·AI education to elementary, middle, and high school students through 'Job's Garage Camp' based on various sharing platforms. By creating a sustainable follow-up educational environment, students are encouraged to continue their self-directed learning of SW and AI. As a result of conducting a pre-post survey of students participating in the 'Job's Garage Camp', the post-survey values improved compared to the pre-survey values in all areas of 'interest', 'understanding and confidence', and 'career aspirations'. Based on these results, it can be confirmed that students had a universal positive perception and influence on SW and AI. Therefore, if the operation case of 'Job's Garage Camp' is improved and expanded, it can be presented as a standard model applicable to other SW and AI education programs in the future.

• Science of Emotion and Sensibility
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• v.26 no.4
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• pp.3-14
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• 2023

Recently, the effect of using social media on psychological well-being has been highlighted. However, studies exploring factors that may predict the quality of social media relationships are relatively rare. The present study investigated whether social media activity and psychological states, such as loneliness and depression, can predict the quality of social media relationships during the COVID-19 quarantine period using a machine learning technique. Ninety-five participants completed a self-report survey on loneliness, Instagram activity, quality of social media relationships, and depression at different time points (during the self-isolation and after the release of self-isolation). Similarity analyses, including multidimensional scaling (MDS), representational similarity analysis (RSA), and classification analyses, were conducted separately at each point in time. The results of MDS revealed that time spent on social media and depression were distinguished from others in the first dimension, and loneliness and passive use were distinguished from others in the second dimension. We divided the data into two groups based on the quality of social media relationships (high and low), and we conducted RSA on each group. Findings indicated an interaction between the quality of the social media relationships and the situation. Specifically, the effect of self-isolation on the high-quality social media relationship group is more pronounced than that on the low-quality group. The classification results also revealed that the predictors of social media relationships depend on whether or not they are isolated. Overall, the results of this study imply that social media relationship could be well predicted when people are not in isolated situations.

• Education of Primary School Mathematics
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• v.27 no.1
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• pp.75-90
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• 2024

This study analyzed the vocabularies presented in the 1st to 2nd grade elementary school mathematics field review textbook according to the 2022 revised curriculum using a 9th grade vocabulary system and improved them. The result of the analysis shows that the frequency of vocabulary that was not appropriate for the students' level was found to be 6.67% in the first semester of the first year and 12.17% in the second semester of the first year. For the first semester of the second year, it was 11.73%, and for the second semester of the second year, it was 14.19%. This shows that the frequency of vocabulary that may be difficult for students gradually increases. Based on the analysis results, vocabulary that had a high difficulty level but was not essential in the textbook was deleted, and essential vocabulary or vocabulary that was difficult for students was presented with pictures added or revised in more detail. In addition, words that can be modified with similar words with low lexical difficulty were replaced and presented. In this way, research on vocabulary difficulty can identify aspects of vocabulary used in textbooks and can help develop high-quality textbooks by appropriately modifying vocabulary for effective mathematics learning.

• The Mathematical Education
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• v.63 no.1
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• pp.19-33
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• 2024

This study investigates longitudinal patterns in middle school students' mathematics interest and achievement using panel data from the 4th to 6th year of the Gyeonggi Education Panel Study. Results from the multivariate growth mixture model confirmed the existence of heterogeneous characteristics in the longitudinal trajectory of students' mathematics interest and achievement. Students were classified into four latent classes: a low-level class with weak interest and achievement, a high-level class with strong interest and achievement, a middlelevel-increasing class where interest and achievement rise with grade, and a middle-level-decreasing class where interest and achievement decline with grade. Each class exhibited distinct patterns in the change of interest and achievement. Moreover, an examination of the correlation between intercepts and slopes in the multivariate growth mixture model reveals a positive association between interest and achievement with respect to their initial values and growth rates. We further explore predictive variables influencing latent class assignment. The results indicated that students' educational ambition and time spent on private education positively affect mathematics interest and achievement, and the influence of prior learning varies based on its intensity. The perceived instruction method significantly impacts latent class assignment: teacher-centered instruction increases the likelihood of belonging to higher-level classes, while learner-centered instruction increases the likelihood of belonging to lower-level classes. This study has significant implications as it presents a new method for analyzing the longitudinal patterns of students' characteristics in mathematics education through the application of the multivariate growth mixture model.

• The Mathematical Education
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• v.63 no.1
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• pp.1-18
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• 2024

This study aims to analyze low-performing high school students' difficulties in constructed response (CR) mathematics assessments and explore ways to use writing activities to support student learning. The participants took CR assessments, engaged in guided writing activities across 15 lessons, and provided responses to our interviews. The study identified 20 types of student difficulties, which were sorted into two main categories: "mathematical difficulties" and "CR difficulties." The difficult nature of mathematics as a school subject included a lack of understanding of mathematical concepts, students' difficulty with mathematical symbols and notations, and struggles with word problems. Challenges specific to CR assessments included students' difficulties arising from the testing conditions unlike those of multiple-choice items, and included issues related to constructing appropriate responses and psychological barriers. To address these challenges in CR assessments, the study conducted guided writing activities as an intervention, through which six themes were identified: (1) internalization of mathematical concepts, (2) mathematical thinking through relational understanding, (3) diverse problem-solving methods, (4) use of mathematical symbols, (5) reflective thinking, and (6) strategies to overcome psychological barriers.