• Journal of Engineering Education Research
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• v.24 no.2
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• pp.29-40
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• 2021

This research aims to examine the relationship between major satisfaction and psychological well-being of female engineering students. To achieve this goal, first, differences in major satisfaction and psychological well-being among engineering students were examined. Second, differences in major satisfaction and psychological well-being among female undergraduate students were investigated. In addition, the effects of psychological well-being, as perceived by undergraduate students by gender and major, on major satisfaction were explored. A total of three hundred and forty-nine (349) undergraduate students from two universities in Korea responded to survey based on a two-variables scale. The findings were that, firstly, male engineering students scored higher in two variables including their most sub-factors, and that gender differences in relation satisfaction as well as positive relationship with others, autonomy, and environmental mastery were statistically significant. Secondly, female engineering students scored higher in major satisfaction and several sub-factors of two variables, and that major differences in major satisfaction (total), social perception satisfaction, and autonomy - a sub-factor of psychological well-being - were statistically significant. Thirdly, a positive correlation among major satisfaction and psychological well-being was identified. Finally, psychological well-being could explain about 19% of major satisfaction. The practical implications of these findings are discussed herein, with attention on education for promotion of major satisfaction.

• Journal of Engineering Education Research
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• v.26 no.3
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• pp.20-34
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• 2023

This research aims to explore the factors affecting engineering students' major persistence and career decision. To achieve this goal, the experiences and perceptions of engineering students and other stakeholders regarding the factors influencing engineering students' major persistence and career decision were analyzed through online focus group interviews (FGI). The analysis of the interviews resulted in the identification of seven categories, which include meeting with older alumni, team project experiences, and internship experiences as factors influencing major persistence. Additionally, engineering interest, engineering self-efficacy, employment rates, economic factors, and limitations in women engineers' career development were identified as factors affecting career decision. Based on these findings, it is expected that engineering education will develop plans that focus on supporting the major persistence and career development of engineering students, thereby contributing to the future direction of engineering education.

• Journal of Engineering Education Research
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• v.19 no.6
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• pp.10-19
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• 2016

This study was conducted to explore the major adjustment factors in undergraduates by investigating the characteristics of group which doesn't correspond to Holland's congruence assumption. For this study, 313 sophomore year undergraduates in engineering were surveyed. The results of this study showed as follows. First, the group which doesn't correspond to Holland's congruence assumption represented 16.6% of all. Second, there were significant differences of basic psychological needs among four groups by interest-major congruence and major adjustment. These findings imply that major selection in congruence with interest is not critical and we should also consider basic psychological needs(autonomy, competence, relatedness) for engineering major adjustment.

• Journal of Engineering Education Research
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• v.26 no.4
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• pp.3-10
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• 2023

The underrepresentation of women in engineering has received considerable attention. A series of policies and practices have been developed to promote gender-balanced participation in engineering. This study focuses on gender disparities in reasons for the choice of engineering majors and regret of that choice. The regression results show that compared to their male counterparts, women are found to select an engineering major primarily based on their high school GPA or CSAT scores rather than their aptitudes and interests. Accordingly, women regret their major choice more than men due to mismatched expectations and abilities or unsatisfactory postgraduate labor market outcomes. The findings provide policy insights to improve gender equity in engineering by further enhancing career education in secondary schools.

• Journal of Engineering Education Research
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• v.19 no.2
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• pp.14-25
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• 2016

This study deals with how liberal education in engineering could be positioned properly in an era of convergence. Disciplines of Engineering has long been focusing on engineer as a 'technical profession' and the liberal education as an accessory to major study has been left behind. Under this understanding, the topic of 'convergence' in the 21th century engineering is hardly to find its way to the establishment into engineering education. This study suggests a direction for the reform of liberal education in engineering, which is expected to overcome the distinction between professional and non-professional aligned with major education and liberal one. For the reform, introducing and developing contents and methodologies considering convergence would open the door of engineering education to interdisciplinary study and to 'open-profession'. Next, the problem solving model in education will be very effective for learners to define the problem, to utilize various knowledges and to lead to meaningful solution. Lastly, we emphasize the changing context of liberal education and it is necessary to integrate 'technology literacy' into liberal education in relation to major study.

• Journal of Engineering Education Research
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• v.25 no.5
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• pp.44-54
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• 2022

The purpose of this study is to effectively implement the artificial intelligence education required in the digital transformation era. As we enter the era of the 4th industrial revolution, the demand for a great digital transformation in industry is essential, and the nurturing of manpower is presented as an indispensable relationship in the industrial field based on it. The integration of various new technologies that have emerged from the era of the 4th industrial revolution has the greatest purpose in realizing artificial intelligence technology. As the importance of digital competency in the top curriculum reorganization has been highlighted, artificial intelligence education is necessary even in the curriculum reorganization in 2022, and there is a demand in the educational field that it should be converted into a mandatory education in middle and high schools. Artificial intelligence education according to the demands of the times is to develop an artificial intelligence curriculum in universities by reestablishing systematic artificial intelligence education in universities, setting educational goals, and presenting the goals of artificial intelligence education by major. The main direction of this study is to present the relationship between artificial intelligence and each major in university education, develop a curriculum based on artificial intelligence for each major, and link artificial intelligence software for AI education customized for each major. We would like to present a process that can measure the learning outcomes of AI education.

• Journal of Engineering Education Research
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• v.24 no.3
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• pp.32-41
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• 2021

The purpose of this study is to develop indicators to establishing the education system, focusing the curriculum in each department of H University for quality management of the curriculum. The indicator was developed based on previous studies and cases studies of other universities, and the validation of them was confirmed by the Delphi survey method. The Delphi surveys were conducted by two times, and 16 and 15 people participated for the first and second survey, respectively. We derived the following major results through developing the indicator. First, in order to conduct an education inspection of major courses, , , , , and are required. Second, the checking factors should be evaluated based on the contents in each stage, and can be regarded as a core activity in the curriculum. Accordingly, the checking factors becomes the basis for the establishment of the indicator to establishing the education system, and the indicator can be an expression method that represents the basis data for determining the factors to be evaluated.

• Journal of Engineering Education Research
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• v.15 no.6
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• pp.80-85
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• 2012

This study investigates significant factors regarding college freshman engineering students and analyses each factors influence on student satisfaction in the College of Engineering core curriculum. We carried out a survey targeting 505 students who completed their fundamental education for major course learning in the 2011 academic year while attending one college in Gwangju and ruled out inadequate respondents. A total sample size 437 students were analyzed. The seven independent variables are academic fees, academic term period, academic environment of the classroom, learning material content, time of lecture, student sincerity and student need for the program. The dependent variable is fundamental education satisfaction level. As a result of multiple regression analysis, the following factors were found to be significant in the following order: learning material content, time of lecture, student sincerity, student need for the program, academic fees and academic environment of the classroom. On the other side academic term period was not significant. For improving fundamental education satisfaction, there is a need for prudent consideration regarding learning material development and lecture times. Also further investigation should take place for policies necessary for increasing learner motivation and sincerity, and expand appropriate conditions for learners to become self-aware of the education they need within their major.

• Journal of Engineering Education Research
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• v.17 no.4
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• pp.95-101
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• 2014

As other major, students have difficulties to be successfully adapted to computer science major without some interest and skill to computer programming. In this paper, we try to find successful factors for computer science major freshmen students to computer programming. The factors we focused are programming experience before college entrance, taking liberal arts/natural science courses at high school, application motivations to computer major, existence of mentors, satisfaction to his/her computer department, student's holland job aptitude code. After analysis, taking liberal arts/natural science courses at high school, satisfaction to computer department, some holland job aptitude code are significant to their successful adaptation to computer major. Also, we found the holland job apptitude code is closely related to student's satisfaction to their major for engineering students including computer science students. Our analysis results will be a suggestion for designing computer science education program with students who enters college without some aptitude or preparation to his major.

• Educational Technology International
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• v.14 no.2
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• pp.137-166
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• 2013

Problem-based learning (PBL) in engineering education has been implemented in various ways. The wide range of PBL methods sometimes creates difficulties in implementing PBL. The purpose of this study was to identify the major variables that a teacher considers in PBL designs for an engineering course and suggest specific PBL methods according to the PBL design variables. This study was conducted using a review research method involving 21 studies from a range of engineering education fields. The results showed that the major variables that engineering professors need to consider when applying PBL are the authenticity of the PBL problem and the method of providing knowledge or information that the learners must know to solve the given problem. Based on the two variables identified, the following four types of PBL methods for engineering education are suggested: 1) lecture-based problem, 2) guided problem-based learning, 3) problem-based learning and 4) co-op problem-based learning.