• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.15 no.6
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• pp.189-207
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• 2020

The government has achieved quantitative growth in university start-up education while supporting start-up education. However, it failed to systematize start-up education from an academic, policy, and practical perspective and to reveal the relationship between education and achievements in supporting start-ups. Therefore, there is a lack of interest and effort to promote effective education. In Europe, in-depth research has already been done over many years to establish an EntreComp system. Competences create values for others and attempt to apply them to education, viewing them as the people's lifelong competitiveness. On the other hand, it is urgent to improve the education system as domestic university start-up education is mainly focused on cultural level start-up skills and easy-to-access education from a business administration perspective. Based on this, the entrepreneurship competence-based start-up education system was designed. Next, eight EntreComp frames were drawn for university students through the Focus Group Interview (FGI) and Delphi survey methods, as well as domestic and international prior studies on EntreComp. In 2018, 919 start-up education programs of 42 start-up leading universities were conducted to derive the status of education by EntreComp. Prior studies of 25 entrepreneurship competences, including data from Bacigalupo et al.(2016), which studied EntreComp in the EU, were investigated and reflected the frequency of research and the importance of education and start-up perspectives. Based on the purpose of the university start-up education presented in this study, the entrepreneurship competence frame consisting of a total of eight, including spotting opportunities, value creation, self improvement, mobilising resources, technology application, strategic management, relationship, and learning through experience, was derived through expert verification. It also investigated the current status of education by competence, the degree of reflection of competence education, and the relationship with the results of support for start-ups that reflect the number of students enrolled in each university. Through this, it was suggested that future start-up education at universities could be improved from the EntreComp perspective. It has a differentiation in research in that it conducted a thorough survey using the data on start-up courses operated by leading startup universities for a certain period. However, it is difficult to generalize because the number of samples of leading startup universities is limited. Nevertheless, this study proposes the educational goal of advancing university start-up education from the perspective of entrepreneurial competence, cultivating future required competences, and fostering entrepreneurial talents that create value for others. In addition, it is meaningful in that it presents a clear direction for subsequent research by preparing a framework for research from a more essential perspective on the entrepreneurship competence frame.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.197-214
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• 2024

This study aimed to investigate students' perceptions at science and technology specialized universities towards entrepreneurship support policies and to derive policy improvement measures by applying a bottom-up approach to reflect the requirements of the policy beneficiaries, i.e., the students. Specifically, the research explored effective execution strategies for student entrepreneurship support policies through a survey and analysis of KAIST students. The findings revealed that KAIST students recognize the urgent need for improvement in sharing policy objectives with the student entrepreneurship field, reflecting the opinions of the campus entrepreneurship scene in policy formulation, and constructing an entrepreneurship-friendly academic system for nurturing student entrepreneurs. Additionally, there was a highlighted need for enhancement in the capacity of implementing agencies, as well as in marketing and market development capabilities, and organizational management and practical skills as entrepreneurs within the educational curriculum. Consequently, this study proposes the following improvement measures: First, it calls for enhanced transparency and accessibility of entrepreneurship support policies, ensuring students clearly understand policy objectives and can easily access information. Second, it advocates for student-centered policy development, where students' opinions are actively incorporated to devise customized policies that consider their needs and the actual entrepreneurship environment. Third, there is a demand for improving entrepreneurship-friendly academic systems, encouraging more active participation in entrepreneurship activities by adopting or refining academic policies that recognize entrepreneurship activities as credits or expand entrepreneurship-related courses. Based on these results, it is expected that this research will provide valuable foundational data to actively support student entrepreneurship in science and technology specialized universities, foster an entrepreneurial spirit, and contribute to the creation of an innovation-driven entrepreneurship ecosystem that contributes to technological innovation and social value creation.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.293-300
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• 2024

On October 31, 2023, the revision of the Medical Technologist Act made it mandatory to complete field training courses in order to obtain a license as a radiologic technologist. Therefore, we would like to survey the actual situation of field training in medical institutions to inform the revised Medical Technologist Act and propose improvement measures to increase the effectiveness of field training. A survey was conducted from March to April, 2023, among radiologic technologists working in medical institutions. The questionnaire was sent through a form on a domestic portal site, Company N, and 120 respondents completed it. Eighty-two respondents, or 68.3 percent, had experience in educating on-the-job training students. 58% of the respondents were aware of the fact that the amendment to the Act on Medical Technologist etc. made field training mandatory to obtain a radiologic technologist license. In accordance with Article 9 of the Medical Technologist Act, which prohibits unlicensed persons from practicing, 50% of the respondents were aware that those who are in training to complete an education course equivalent to the license they are seeking to obtain at a university or other institution are allowed to practice as medical Technologists. When asked what is currently taught during fieldwork, 6% of respondents said that they are required to perform radiation-generating activities in addition to observing, guiding patients, and positioning and moving patients. When asked about the future direction of education as fieldwork becomes mandatory for licensure, 77% of respondents said that they will teach more than they currently do. When asked about the appropriate total length of fieldwork, 35% said 12 weeks and 480 hours, 33% said 8 weeks and 320 hours, and 27% said 16 weeks and 640 hours. It can be seen that the current on-the-job training is inadequate according to various regulations, and students' satisfaction is low. However, with the revision of the Act on Medical Technologists, field training has become mandatory to obtain a license as a radiologist, and it is necessary to improve the educational conditions of field training. Therefore, it is necessary to comply with the Nuclear Safety Act and the Rules on the Safety Management of Diagnostic Radiation Generating Devices, introduce standardized training objectives and evaluation systems, designate training hospitals and radiologists in charge of training, and introduce extended training periods and simulation exercises to internalize field training.

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