Authors:
June Rey L. Dullete
Addresses:
Department of Drafting Technology, University of Antique, Sibalom, Antique, Philippines.
Abstract:
A descriptive-comparative quantitative study investigated adaptability, collaboration, problem-solving, growth, and professionalism among 297 College of Industrial Technology graduates. The academic major selected participants through stratified randomisation. A systematic online survey was distributed through class group conversations, with informed consent obtained to maintain anonymity. Excel replies were examined by SPSS v26. ANOVA and Tukey's HSD were used to examine differences in organisational involvement and academic success at an alpha level of 0.05. Descriptive statistics summarized the results, and ethical standards were upheld throughout the study. The survey found that College of Industrial Technology graduates demonstrated flexibility in teamwork, problem-solving, professionalism, and personal growth. Flexibility was often ranked lower, indicating a key area for development. Women reported more personal growth, but differences were modest. Independent students scored higher on several adaptation characteristics, suggesting it may increase soft skills. Student leaders enhanced workplace adaptation, cooperation, and adaptability. Thus, leisure social media users were more flexible than productive users in growth and professionalism. Upper-class students rated themselves higher in key categories of adaptability. Organisational involvement, housing, social media use, and socioeconomic status also affected collaboration and problem-solving. These data suggest that internal and contextual factors affect students' preparation for a dynamic professional context. Interactive and adaptive learning may enhance the careers of technical education graduates.
Keywords: Quantitative Study; Industrial Technology; Soft Skills; Adaptability Categories; Organisational Involvement Problem-Solving; Interactive and Adaptive; Technical Education.
Received: 10/05/2024, Revised: 02/08/2024, Accepted: 13/09/2024, Published: 03/06/2025
DOI: 10.64091/ATITL.2025.000128
AVE Trends in Intelligent Techno Learning, 2025 Vol. 2 No. 1 , Pages: 19-34