• Education of Primary School Mathematics
• /
• v.27 no.3
• /
• pp.227-246
• /
• 2024

The purposes of this study were to analyze the levels of cognitive demand and questioning types in tasks of 'Data and Chance' presented in elementary mathematics textbooks in Korea, the United States, and Australia. The levels of cognitive demand of textbook tasks were analyzed according to the knowledge and process and thinking types required in the tasks. The tasks were also analyzed for questioning types, answer types, and response types. As a result, in terms of knowledge and process and thinking types in tasks, all three countries had something in common: the percentage of tasks requiring 'representation' and process was the highest, and the percentage of tasks requiring 'basic application of skill/concept' was also the highest. From a thinking types perspective, differences were found between textbook tasks in the three countries in graph and chance learning. The results of analyzing questioning types showed that in all three textbooks, the percentage of observational reasoning questions was highest, followed by the percentage of factual questions. The proportions and characteristics of the constructing questions included in the U.S. and Australian textbooks differed from those in the Korean textbooks. Based on these results, this study presents implications for constructing elementary mathematics textbook tasks in 'Data and Chance.'

• Korean Association of Arts Management
• /
• no.58
• /
• pp.205-239
• /
• 2021

This study explores the importance of semantic web-based network construction in art data archiving, as well as its meaning and value in the context of arts management along with its potential for future application. The study focuses on oral history obtained from the Arko Arts Archives that contained records of the lives and artistic views of early artists. In this study, the possibility of applying semantic web-based technology to materials concerning culture and the arts was discussed in five aspects based on the results of the case analysis. First, checking the relationship and discovering hidden artists are possible by revealing relationships between characters. Second, understanding and studying society and culture at a given time is possible by interpreting the contextual meaning of information. Third, art exploration can be done broadly and deeply, encompassing various genres from the perspective of the consumer. Fourth, through art construction, history can be reconstructed using a new and rich method. Fifth, expanding the scope beyond the boundaries of art is possible through convergence and collaboration of programs that handle big data. The network data can be used in various methods, such as art history research, art planning, and creation, throughout the art ecosystem. The results of the study suggest that digitizing a large quantity of data concerning culture and the arts is meaningful in arts management as well as identifying and analyzing the relationship network among data clusters using semantic web-based technology.

• Journal of Intelligence and Information Systems
• /
• v.18 no.3
• /
• pp.97-117
• /
• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.8 no.1
• /
• pp.1088-1096
• /
• 1966

The following is a method of synthetic unitgraph derivation based on the routing of a time area diagram through channel storage, studied by Clark-Jonstone and Laurenson. Unithy drograph (or unitgraph) is the hydrograph that would result from unit rainfall\ulcorner excess occuring uniformly with respect to both time and area over a catchment in unit time. By thus standarzing rainfall characteristics and ignoring loss, the unitgraph represents only the effects of catchment characteristics on the time distribution of runoff from a catchment The situation abten arises where it is desirable to derive a unitgraph for the design of dams, large bridge, and flood mitigation works such as levees, floodways and other flood control structures, and are also used in flood forecasting, and the necessary hydrologie records are not available. In such cases, if time and funds permit, it may be desirable to install the necessary raingauges, pruviometers, and stream gaging stations, and collect the necessary data over a period of years. On the otherhand, this procedure may be found either uneconomic or impossible on the grounds of time required, and it then becomes necessary to synthesise a unitgraph from a knowledge of the physical charcteristics of the catchment. In the preparing the approach to the solution of the problem we must select a number of catchment characteristic(shape, stream pattern, surface slope, and stream slope, etc.), a number of parameters that will define the magnitude and shape of the unit graph (e.g. peak discharge, time to peak, and base length, etc.), evaluate the catch-ment characteristics and unitgraph parameters selected, for a number of catchments having adequate rainfall and stream data and obtain Correlations between the two classes of data, and assume the relationships derived in just above question apply to other, ungaged, Catchments in the same region and, knowing the physical characteritics of these catchments, substitute for them in the relation\ulcorner ships to determine the corresponding unitgraph parameters. This method described in this note, based on the routing of a time area diagram through channel storage, appears to provide a logical line of research and they allow a readier correlation of unitgraph parameters with catchment characteristics. The main disadvantage of this method appears to be the error in routing all elements of rainfall excess through the same amount of storage. evertheless, it should be noted that the synthetic unitgraph method is more accurate than the rational method since it takes account of the shape and tophography of the catchment, channel storage, and temporal variation of rainfall excess, all of which are neglected in rational method.

• Journal of KIISE:Databases
• /
• v.37 no.3
• /
• pp.127-136
• /
• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• Journal of the Korean Society of Earth Science Education
• /
• v.10 no.1
• /
• pp.50-61
• /
• 2017

The purpose of this study is to analysis eye tracking for high school students' learning styles in the process of solving in the behavioral domains of the College Scholastic Ability Test on Earth Science I. The subjects of this study were 50 students from two classes out of 4 classes in E high school in Chungcheong province. Among them, we conducted experiments by randomly sampling 2 students of each type of learning based on the criteria that they had not encountered the problem of Earth Science I from the past two years. The findings indicate that the item correctness rate of divergers, assimilators, convergers, and accommodators were higher in the knowledge domain, application domain, knowledge-understanding domain, and understanding domain. This confirms that there is a difference among the four learning styles in the level of achievement according to the behavioral areas of the assessment questions. The latter finding was that the high eye-share of AOI 2 appeared higher than AOI 1, 3, 4 in the course of solving the problems. This is because the four types of learners pay more careful attention to the AOI 2 area, which is the cue-or-information area of problem solving, that is, the Table, Figure, and Graph area. Therefore, in order to secure the fairness and objectivity of the selection, it is necessary that an equal number of questions of each behavioral domain be selected on the Earth Science I Test of the College Scholastic Ability Test in general. Besides, it seems to be necessary that the knowledge, understanding, application, and the behavior area of the inquiry be highly correlated with the AOI 2 area in development of test questions.

• The Journal of Engineering Research
• /
• v.6 no.2
• /
• pp.11-22
• /
• 2004

A distributed constraint satisfaction problem (distributed CSP) is a constraint satisfaction problem(CSP) in which variables and constraints are distributed among multiple automated agents. ACSP is a problem to find a consistent assignment of values to variables. Even though the definition of a CSP is very simple, a surprisingly wide variety of AI problems can be formalized as CSPs. Similarly, various application problems in DAI (Distributed AI) that are concerned with finding a consistent combination of agent actions can be formalized as distributed CAPs. In recent years, many new backtracking algorithms for solving distributed CSPs have been proposed. But most of all, they have common drawbacks that the algorithm assumes the priority of agents is static. In this thesis, we establish a basic algorithm for solving distributed CSPs called dynamic priority search algorithm that is more efficient than common backtracking algorithms in which the priority order is static. In this algorithm, agents act asynchronously and concurrently based on their local knowledge without any global control, and have a flexible organization, in which the hierarchical order is changed dynamically, while the completeness of the algorithm is guaranteed. And we showed that the dynamic priority search algorithm can solve various problems, such as the distributed 200-queens problem, the distributed graph-coloring problem that common backtracking algorithm fails to solve within a reasonable amount of time. The experimental results on example problems show that this algorithm is by far more efficient than the backtracking algorithm, in which the priority order is static. The priority order represents a hierarchy of agent authority, i.e., the priority of decision-making. Therefore, these results imply that a flexible agent organization, in which the hierarchical order is changed dynamically, actually performs better than an organization in which the hierarchical order is static and rigid. Furthermore, we describe that the agent can be available to hold multiple variables in the searching scheme.