• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.95-112
• /
• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.44 no.2
• /
• pp.25-36
• /
• 2016

This paper discusses the character of communication among participating stakeholders in the Improving Alley Landscapes in Residential Neighborhoods project. The participation of diverse stakeholders in conventional urban redevelopment is considered to delay and complicate the progress of a project. However, in urban regeneration, a field-oriented operating system and collaboration between diverse stakeholders is considered critical to building a sustainable community. A stakeholder is defined as "a person or organization that can influence decision-making or be influenced by it." This paper uses a case study to examine what types of stakeholders participate and what communicative processes and ideas are shared among them. Six neighborhoods were selected out of a total of 26 of Seoul's 2014 Improving Alley Landscapes project. This research was developed through interviews and a review of the literature. The character of communication among stakeholders in the case study is as follows. Firstly, the administration initiated the project but did not show leadership. This was caused by a gap in understanding about the project between city and borough administrations, Further, the city administration lacked experience with projects that placed an emphasis on fieldwork. Tongjand and Banjang, at ancillary institutions, acted as spokespersons and helped people in the community to understand the administrative process. However, because they led communication and used personal relationships to ensure they communicated effectively, the communication process had limits from the perspective of democratic process. Diverse stakeholders expressed their opinions in the public sphere and communicated about them using diverse media. Finally, experts produced the output, facilitated communication, and mediated in conflicts. Because new experts acted as facilitators and mediators, there was a great deal of trial and error. This project has particular significance: Seoul's city government deals with urban space rather than parks and green space, which are limited by boundaries; and whether "green" can be used for urban renovation was tested by several landscape architects, who sought to identify a new role in urban renovation, namely, the role of landscape and landscape architecture. However, the project has some limitations, including an insufficiently detailed project plan, a lack of common understanding among stakeholders, and a short timeframe. A number of stakeholders overcame these limitations to a certain degree. Officials of the Borough and the Dong managed the project and resolved civil complaints. Experts provided special information, and contributed to the design and construction of improvements.

• Journal of Internet Computing and Services
• /
• v.15 no.3
• /
• pp.101-107
• /
• 2014

With the development of online service, recent forms of databases have been changed from static database structures to dynamic stream database structures. Previous data mining techniques have been used as tools of decision making such as establishment of marketing strategies and DNA analyses. However, the capability to analyze real-time data more quickly is necessary in the recent interesting areas such as sensor network, robotics, and artificial intelligence. Landmark window-based frequent pattern mining, one of the stream mining approaches, performs mining operations with respect to parts of databases or each transaction of them, instead of all the data. In this paper, we analyze and evaluate the techniques of the well-known landmark window-based frequent pattern mining algorithms, called Lossy counting and hMiner. When Lossy counting mines frequent patterns from a set of new transactions, it performs union operations between the previous and current mining results. hMiner, which is a state-of-the-art algorithm based on the landmark window model, conducts mining operations whenever a new transaction occurs. Since hMiner extracts frequent patterns as soon as a new transaction is entered, we can obtain the latest mining results reflecting real-time information. For this reason, such algorithms are also called online mining approaches. We evaluate and compare the performance of the primitive algorithm, Lossy counting and the latest one, hMiner. As the criteria of our performance analysis, we first consider algorithms' total runtime and average processing time per transaction. In addition, to compare the efficiency of storage structures between them, their maximum memory usage is also evaluated. Lastly, we show how stably the two algorithms conduct their mining works with respect to the databases that feature gradually increasing items. With respect to the evaluation results of mining time and transaction processing, hMiner has higher speed than that of Lossy counting. Since hMiner stores candidate frequent patterns in a hash method, it can directly access candidate frequent patterns. Meanwhile, Lossy counting stores them in a lattice manner; thus, it has to search for multiple nodes in order to access the candidate frequent patterns. On the other hand, hMiner shows worse performance than that of Lossy counting in terms of maximum memory usage. hMiner should have all of the information for candidate frequent patterns to store them to hash's buckets, while Lossy counting stores them, reducing their information by using the lattice method. Since the storage of Lossy counting can share items concurrently included in multiple patterns, its memory usage is more efficient than that of hMiner. However, hMiner presents better efficiency than that of Lossy counting with respect to scalability evaluation due to the following reasons. If the number of items is increased, shared items are decreased in contrast; thereby, Lossy counting's memory efficiency is weakened. Furthermore, if the number of transactions becomes higher, its pruning effect becomes worse. From the experimental results, we can determine that the landmark window-based frequent pattern mining algorithms are suitable for real-time systems although they require a significant amount of memory. Hence, we need to improve their data structures more efficiently in order to utilize them additionally in resource-constrained environments such as WSN(Wireless sensor network).

• Journal of Technology Innovation
• /
• v.24 no.1
• /
• pp.113-142
• /
• 2016

T-50 exportation to Indonesia embodied an objective of governmental policy and became a catalyst accelerating the exportation of domestic defense industries. Defense industry exportation is recognized as a new growth engine creating economic interests and it became an important policy of the government. This study will suggest an effective direction for the support policy of the defense industry exportation through analysis on factors behind the success of the T-50 exportation to indonesia in the view of policy network. Policy network theory has its efficacy and workability in analyzing what kind of results are yielded from each policy actor's attributes and their interaction during the execution and establishment of the support policy for the defense exportation. The type of policy network of the T-50 exportation to Indonesia was a policy community. Many governmental ministries, defense industry which is the group of interest, and experts from the research institutes have established the Korea Defense Trade Support Center(KODITS) for accomplishing common policy goal with mutually shared sentiment, and sought for a strategy for the success of the defense industry exportation having official and unofficial meeting centering around the KODITS. Although there were oppositions and conflicts among major actors, though forming a cooperative relationship among majority of the actors, policy-wise decision making for the exportation of the T-50 to Indonesia was efficiently carried out. The cooperative relationship was the key in the success of the T-50 exportation. Considering that the policy community from cooperative mutual interaction is efficient in reaching the goal of the defense industry exportation support policy, this study suggests operating government-wise temporary Task Force(TF) to succeed in big exportation projects such as the T-X exportation to the U.S. In addition, institutional and procedural supplementation such as regular meetings among the head of related governmental ministries and etc. are required in order to enhance the mutually cooperative relationship withing the TF.

• Journal of The Korean Association For Science Education
• /
• v.38 no.4
• /
• pp.467-480
• /
• 2018

This study aims to investigate how flipped learning-based socioscientific issue instruction (FL-SSI instruction) affected middle school students' key competencies and character development. Traditional classrooms are constrained in terms of time and resources for exploring the issues and making decision on SSI. To address these concerns, we designed and implemented an SSI instruction adopting flipped learning. Seventy-three 8th graders participated in an SSI program on four topics for over 12 class periods. Two questionnaires were used as a main data source to measure students' key competencies and character development before and after the SSI instruction. In addition, student responses and shared experience from focus group interviews after the instruction were collected and analyzed. The results indicate that the students significantly improved their key competencies and experienced character development after the SSI instruction. The students presented statistically significant improvement in the key competencies (i.e., collaboration, information and technology, critical thinking and problem-solving, and communication skills) and in two out of three factors in character and values as global citizens (social and moral compassion, and socio-scientific accountability). Interview data supports the quantitative results indicating that SSI instruction with a flipped learning strategy provided students in-depth and rich learning opportunities. The students responded that watching web-based videos prior to class enabled them to deeply understand the issue and actively engage in discussion and debate once class began. Furthermore, the resulting gains in available class time deriving from a flipped learning approach allowed the students to examine the issue from diverse perspectives.