Session Information
11 ONLINE 52 A, Quality of higher education: Students' skills development
Paper Session
MeetingID: 941 8647 0225 Code: S0YB3u
Contribution
Due to the advancement of digitalization and robotics, enterprises will employ only competent and highly skilled fresh engineering graduates who are capable of controlling these technologies. As a result, fresh engineering graduates face stiff competition for jobs, and a high unemployment rate for low-skilled graduates appears to be a global issue (NESDC, 2020). This could be due to a mismatch between workforce supply and demand in various production sectors, such as individuals with non-essential qualifications or specialized work-related techniques (Chonsalasin & Khampirat, 2022). It is critical to emphasize the necessity of utilizing the benefits of information and communication technology (ICT) in order to keep modern organizations' operations running smoothly. As a result, students must be prepared to develop and enhance their professional skills in order to graduate with the ability to work effectively. Digital literacy is a crucial talent in the digital era, in addition to engineering proficiency (Khampirat et al., 2019). Previous research has emphasized the importance of digital skills as a development tool for students (Simo et al., 2020) in order to acquire strong work chances and career advancement. Researchers have looked at the factors that influence students' digital literacy (Kim et al., 2018), and they've discovered that digital abilities are linked to creative self-efficacy (Yang & Cheng, 2009). Self-efficacy was identified by Bandura (Bandura, 1997) as a key to support that could be used to understand students' confidence and beliefs about their capacity to do specific tasks or activities. Having a high level of self-efficacy will greatly improve your chances of success (Bandura, 1997). Few researches have been conducted on the relationship between digital skills and self-efficacy in engineering skills (ENSE). ENSE plays an important role in working efficiently and is wanted in many production sectors (Audibert et al., 2020).
As a result, an empirical study is required to better our understanding of the association between digital abilities and ENSE. Digital skill is a term that encompasses both skill and specialized approaches required for effective usage of digital technology, technology skills, digital literacy, digital competence, and ICT skills (Chonsalasin &. Khampirat, 2022). UNESCO (2018) proposed a concept frame of operation in seven primary aspects for the newest competency areas essential to digital literacy skills: devices and software operations, information and data literacy, communication and collaboration, digital content creation, safety, problem-solving, and career-related competencies.
The purpose of this study was to investigate the link between digital skills and engineering students' ENSE at higher education levels. The information gathered can help educational institutions, students, policymakers, and employers plan to learn activities that will improve student ENSE quality, boost employment possibilities, and encourage career prosperity (Khampirat, 2021).
Method
Participants: The participants in this study were 1,316 engineering students in Thailand. The majority of them (61.17%, N = 805) were male, while 38.60% (N = 508) were female, and 0.23% (N = 3) did not specify their gender. 73.71% (N = 970) were between the ages of 18 and 22, while 25.46% (N = 335) were between the ages of 23 and 27. Senior students made up more than half of the participants (50.45%, N = 664), while junior students made up 37.31% (N = 491). Data Collection Procedure: The survey was conducted in classrooms. Before distributing the questionnaire, authorization to collect data was obtained. All students were given a memorandum of understanding in which they agreed to participate in the research. They were also told that their involvement was entirely optional and that they might leave at any time. Their responses to the questionnaire were also kept private and anonymous. Instruments: (1) Digital skills: The researcher created this scale based on past research, the ABET framework, and learning outcomes measurements for engineering students. It included 8 self-reported items scored on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).c (2) Engineering skill self-efficacy (ENSE): Mamaril's scale (Mamaril, 2014) was adapted to investigate three subscales: engineering design self-efficacy (4 items), experimental skills self-efficacy (5 items), and tinkering skills self-efficacy (5 items). It consisted of 14 items, each of which was rated on a 5-point scale (1 = strongly disagree, 5 = strongly agree). Data Analysis: The internal consistency or reliability between several items was evaluated by Cronbach's alpha reliability coefficient. Descriptive statistics and Pearson correlation were analyzed to explore characteristics of variables, check the assumptions of multivariate statistical analysis, and explain relations between indicators in the model. Structural equation modeling (SEM) was performed to test the effect of digital skills on ENSE.
Expected Outcomes
Pearson correlation analysis was investigated the issue of multicollinearity occurs. All intercorrelations among indicators had correlation coefficients less than 0.80, which meant that their relation was not high enough to have multicollinearity (Hair et al., 2014). Cronbach's alpha (α) values of each subscale and construct were between 0.813 – 0.923, which exceeded 0.50 (Streiner & Norman, 2003), indicating that there was internal consistency of a tool to measure the convergent validity. The values of construct reliability (CR) and average variance extracted (AVE) confirmed the reliability of the tools in this study and showed that the used tools have adequate construct validity. The results of CFA showed that the two tested constructs had a good fit to empirical data and the observed variables were reliable in their latent constructs (digital skills and ENSE). The values of standardized factor loading had statistical significance (p < 0.001). The SEM results found that digital skill is important to the building of ENSE for engineering students. This often occurs when students have the knowledge and abilities to use advanced computers, ICTs, and professional tools in engineering practice to a variety of job circumstances. This result is in line with Yang and Cheng (2009). The results of a more detailed study will be presented at the conference. The findings of the study could help educators to promote students' ENSE, such as Upskilling/reskilling students to be a part of the digital future, having a strong ICT and digital skills for today's workplace, and understanding how to integrate digital technology knowledge as lifelong education. Although this research work was conducted in the context of Thailand, the theoretical models and statistical analyses presented in this work can apply in other contexts and are of interest for audiences in other countries as well.
References
Audibert, A., Vieira, D. A., De Andrade, A. L., & de Oliveira, M. Z. (2020). Transversal and professional skills self-efficacy scale: Cultural adaptation and evidence of validity. Trends in Psychology, 28(3), 368-380. https://doi.org/10.1007/s43076-020-00030-6 Bandura, A. (1997). Self-efficacy: The exercise of control (Vol. 50). W.H. Freeman. Brown, T. A. (2006). Confirmatory Factor Analysis for Applied Research. The Guilford Press. Chonsalasin, D., &. Khampirat, B. (2022). The Impact of Achievement Goal Orientation, Learning Strategies, and Digital Skill on Engineering Skill Self-Efficacy in Thailand. IEEE Access.https://doi.org/10.1109/ACCESS.2022.3146128. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data analysis: A global perspective (7th ed.). Pearson education. Khampirat, B. (2021). The impact of work-integrated learning and learning strategies on engineering students’ learning outcomes in thailand: A multiple mediation model of learning experiences and psychological factors. IEEE Access, 9, 111390-111406. https://doi.org/10.1109/ACCESS.2021.3055620 Khampirat, B., Pop, C., & Bandaranaike, S. (2019). The effectiveness of work-integrated learning in developing student work skills: A case study of Thailand. International Journal of Work-Integrated Learning, 20(2), 127-146. Kim, H. J., Hong, A. J., & Song, H.-D. (2018). The Relationships of Family, Perceived Digital Competence and Attitude, and Learning Agility in Sustainable Student Engagement in Higher Education. Sustainability, 10(12), 4635. https://doi.org/10.3390/su10124635 Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3rd ed.). The Guilford Press. Li, X., & Hu, R. (2020). Developing and validating the digital skills scale for school children (DSS-SC). Inf. Commun. Soc., 1-18. https://doi.org/10.1080/1369118X.2020.1864002 Mamaril, N. J. A. (2014). Measuring Undergraduate Students’ Engineering Self-Efficacy: A Scale Validation Study Univ. Ky.]. Lexington, KY, USA. NESDC. (2020). Social situation and outlook 2020. https://www.nesdc.go.th/ewt_news.php?nid=10291 Simo, V. L., Lagaron, D. C., & Rodriguez, C. S. (2020). STEM education for and with a digital era: the role of digital tools for the performance of scientific, engineering and mathematic practices. Red-Revista De Educacion a Distancia, 20(62), 29. Streiner, D. L., & Norman, G. R. (2003). Health Measurement Scales: A practical guide to their development and use (3rd ed.). Oxford University Press. Tuamsuk, K., & Subramaniam, M. (2017). The current state and influential factors in the development of digital literacy in Thailand’s higher education. Inf. Learn. Sci., 118(5/6), 235-251. https://doi.org/10.1108/ILS-11-2016-0076 UNESCO. (2018). A global framework of reference on digital literacy skills for indicator 4.4.2. http://uis.unesco.org/sites/default/files/documents/ip51-global-framework-reference-digital-literacy-skills-2018-en.pdf Yang, H.-L., & Cheng, H.-H. (2009). Creative self-efficacy and its factors: An empirical study of information system analysts and programmers. Comput. Hum. Behav., 25(2), 429-438.
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