• Journal of Engineering Education Research
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• v.23 no.4
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• pp.37-51
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• 2020

This study examined the differential effects of High-Impact Practices(HIPs) on the career aspiration of STEM college students by gender. Through the theoretical lens of Social Cognitive Career Theory(SCCT), a two-level model analysis was conducted. A sample of 2,101 third- and fourth-year undergraduate students majoring in STEM at 38 universities, which had been collected from the National Survey on College Student Experiences and Learning Outcomes funded by the Korea Research Foundation, was used. This study found that the three HIP domains(learning with peers, faculty support, content relevancy) had different influences depending on gender. These findings suggest that HIPs can benefit the development of female students' career aspiration and have gender-differential effects on students in STEM majors. Based on those findings, this study also deduced implications about the roles of faculty members and higher-education institutions that might foster the retention of women in STEM.

• Journal of Engineering Education Research
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• v.20 no.4
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• pp.38-50
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• 2017

The purpose of this study is to analyze the current evaluation methods on faculty performance at Korean engineering colleges and develop teaching evaluation indicators for faculty performance. We investigated the faculty performance cases in engineering colleges inside and outside of the Korea, the engineering faculty's awareness of evaluation factors for their educational performance, and the appropriate ratios by indicating factors. Also we developed evaluation indicators for educational achievements to improve the current faculty performance system. 227 engineering faculty members answered our survey questionnaire. The result in the case study on faculty performance evaluation is as follows. First, most items of faculty performance evaluation are about quantitative indicators that can easily conduct objective evaluation. Second, evaluation items of faculty performance are mostly focused on instruction in a classroom. Third, the evaluation by students and administrative managers is more dominant than that by professors or their colleagues, document evaluation than on site evaluation, general evaluation than formative evaluation, and static evaluation than dynamic evaluation. Lastly, Some universities tend to substitute outstanding articles for underperforming instruction. The evaluation indicators that we have developed can be implemented by four types of subjects, such as students, professors, their colleagues, and deans. Also, based on the evaluation indicators, faculties can freely select their evaluation domains depending on the their tracks, such as a teaching track, a research track, or an industry-university cooperation track. The mandatory evaluation fields include teaching, student counselling, teaching portfolio evaluation by mentors or colleagues, class management evaluation by deans, and self-evaluation. The other areas are optional and professors can choose their evaluation factors.

• International conference on construction engineering and project management
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• 2007.03a
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• pp.346-355
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• 2007

Employability is a critical issue in construction education. Employability is more than students obtaining employment upon graduation. The concept is far more ranging, and should encompass enabling students to acquire the knowledge, personal and professional skills and encouraging attitudes that will support their future development and employment. This paper describes two case studies relating to how the true concept of employability can be incorporated into the construction higher education curriculum. Case study 1 was a collaborative venture with contributions from a higher education provider, employers, students and a professional body (Association of Building Engineers). It outlines the whole process from course inception through to graduation and feedback. Thus it presents a valid model for other higher education providers of construction courses to adapt or adopt. Case study 2 outlines how the opportunity of a degree programme revalidation process was utilized to introduce modules which would enhance students' employability on graduation.

• Journal of Power Electronics
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• v.12 no.1
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• pp.201-207
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• 2012

This paper presents the results and gained experiences from the Project Based Learning (PBL) of magnetic component design within a Power Electronics Course. PBL was applied during the laboratory exercises through a design-project task based on a boost converter test board. The students were asked to calculate the main boost converter's circuit parameters' capacitor C and inductor L, and then additionally required to design and build-up the inductor L, in order to meet the project's goals. The whole PBL process relied on ideas from the CDIO (Conceive, Design, Implement, Operate), where the students are encouraged to consider the whole system's process, in order to obtain hands-on experience. PBL is known to be a motivating and problem-centered teaching method that gives students the ability to transfer their acquired scientific knowledge into industrial practice. It has the potential to help students cope with demanding complexities in the field, and those problems they will face in their future careers.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.3
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• pp.285-291
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• 2022

The number of people enrolling in universities is rising due to the simplicity of applying and the benefit of earning a bachelor's degree. However, the on-time graduation rate has declined since plenty of students fail to complete their courses and take longer to get their diplomas. Even though there are various reasons leading to the aforementioned problem, it is crucial to emphasize the cause originating from the management and care of learners. In fact, understanding students' difficult situations and offering timely Number of Test data and advice would help prevent college dropouts or graduate delays. In this study, we present a machine learning-based method for early detection at-risk students, using data obtained from graduates of the Faculty of Information Technology, Dainam University, Vietnam. We experiment with several fundamental machine learning methods before implementing the parameter optimization techniques. In comparison to the other strategies, Random Forest and Grid Search (RF&GS) and Random Forest and Random Search (RF&RS) provided more accurate predictions for identifying at-risk students.

• Journal of Power Electronics
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• v.10 no.6
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• pp.621-627
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• 2010

This paper presents a TSMST (Theory - Simulation - Measurement - Simulation - Theory) method for power electronics laboratory. The method successfully integrates theory, simulation and measurement, thus enabling better integration of student's knowledge and better usage of inadequate number of laboratory hours. Students are attracted with relatively simple tasks to be solved and modern, but economical laboratory equipment. A significant part of the assignments can be made at home, thus lowering the pressure on students to finish the tasks on time. The proposed method is described on three basic examples explaining characteristic phases of the TSMST method.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.9
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• pp.35-43
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• 2021

This study aims to investigate factors affecting eBook acceptance and learning outcomes among students experiencing online distance learning. As conventional textbooks are now switched into eBooks, the effects of contextual factors including lecturer, student computer competency, content and design of the course, access ability, infrastructure, and university support on eBook acceptance and learning outcome needs to be evaluated. The sample of this study is represented by students at the Universiti Teknologi MARA, City Campus Melaka, undertaking 'strategic management course'. Non-probability random sampling was selected as the sampling technique and a purposive sampling method was chosen to select the samples. The samples comprised 171 students randomly selected through Google Form. The questionnaire data was analyzed by using PLS-SEM. The results indicated that these factors contributed 62.3% variations in the eBook acceptance and 67.1% variations in the learning outcomes. The strongest factor affecting both dependent variables was content and design of course. Managerial implication suggested that the content for all courses taught through the eBook platform needs to be revisited and improved in accordance with the mode of online deliverance. Tutorial on how to navigate the eBook platform is important to all users as this would enhance acceptance and produce better learning outcomes among students.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.32-44
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• 2023

Information security breach is a major risk in e-learning. This study presents the potential information security disruptions in Learning Management Systems (LMS) from the perspective of users. We use the Technology Acceptance Model approach as a user perception model of information security, and the results of a questionnaire comprising 44 questions for instructors and students across Indonesia to verify the model. The results of the data analysis and model testing reveals that lecturers and students perceive the level of information security in the LMS differently. In general, the information security aspects of LMSs affect the perceptions of trust of student users, whereas such a correlation is not found among lecturers. In addition, lecturers perceive information security aspect on Moodle is and Google Classroom differently. Based on this finding, we recommend that institutions make more intense efforts to increase awareness of information security and to run different information security programs.

• Journal of Engineering Education Research
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• v.15 no.4
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• pp.3-7
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• 2012

In 1996, the Faculty of Engineering, Niigata University, Japan entered an era of open student-exchange with Otto-von-Guericke-University Magdeburg, Germany. Thus far, more than 50 of our students have devoted anywhere from three months, to an entire year of their courses, to collaborative efforts with fellow students, (-and some cases, the local citizenry) -in their native environment experiencing unfamiliar education systems and cultures.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3836-3854
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• 2022

The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division.