Industry 4.0 and the digital age have dramatically influenced both information technology (IT) job characteristics and IT labor demand. Leaders in higher education must keep up with the situation and accelerate plans to produce graduates with the quality and preparation required to meet industry needs. But based on the existing demand gap, universities are eager to first know which skills the IT-related industries expect from new digital workers. This study, conducted in Thailand, explores the competency of the digital workforce, an issue that was identified as vital to the 2017–2021 national agenda. The research project was divided into two steps. Phase one was to study and identify essential competencies for the digital workforce by first reviewing the literature, then verifying these results through qualitative methodology. Thirty IT experts in IT and related industries were invited to interview sessions. Eventually, after content analysis, 24 competencies were presented. Phase two was to survey the competency expectations of IT experts by using the initial questions generated by Phase One's outcome. 260 questionnaires were analyzed. Exploratory factor analysis (EFA) was selected to cluster the digital workforce competencies that were found. Three significant categories were selected based on Eigenvalue, and the average results of demand were explained. Industries had most expected competencies in the Professional skills and IT knowledge category, followed by the IT technical category and IT management and support category. The top five competencies desired were lifelong learning, personal attitude, teamwork, dependability, and IT foundations. However, there were some slightly different requirements between the IT industry and IT in non-IT industries. The results presented a new perspective that is very useful to Thailand. The academic sector can use these results to shape IT curriculum in order to effectively respond to real demand. In addition, recent graduates or graduating students can study these conclusions and better prepare themselves for future jobs.
This study developed an application ontology related to the medical tourism supply chain domain (MTSC). The motivation for developing an ontology is that current MTSC studies use a descriptive approach to provide knowledge, which is difficult to comprehend and apply. The formalization of MTSC knowledge can provide medical tourism stakeholders with a shared understanding of the medical tourism business. As a result, the MTSC domain requires efficient semantic knowledge representation. Ontology is a popular approach for integrating knowledge and comprehension because it presents schema and knowledge base in an accurate and relevant feature. This paper employed the ontology engineering methodology, which included specification, conceptualization, and implementation steps. The conceptual model and facets of the MTSC are proposed. The MTSC objective and scope are tested with semantic competency questions against SPARQL Query formulations. The ontology metrics evaluation was used to verify the ontology quality including the external validation done by the domain experts. The results showed that the MTSC ontology has an appropriate schema design, terminologies, and query results.
Background: The constructive and specific feedback in guiding long jump athletes to improve their performance in each phase is part of the critical process for achieving desired long jump distance. However, to date, the potential approach for assisting a coach in capturing long jump movement and transferring their knowledge to long jump students is not well-established.Objectives: To investigate the performance of long jump students and evaluate transferring knowledge from coaches to long jump students using a Knowledge-Based Smart Trainer (KBST) System.Methods: Twenty-two participants (fifteen males, mean age = 15.33 ± 1.95 years; seven females, mean age = 14.57 ± 2.07 years) participated in the study. All participants were recruited from eleven sports schools in Thailand. Each participant was instructed to perform the long jump movement, including running, take-off, and landing, for three attempts (Test 1, Test 2, and Test 3). Test 1 was the conventional approach (coaches provided the feedback based on their experience). Test 2 and Test 3 were the KBST system approach (coaches provided the feedback based on the results from KBST system). Two cameras were used to record the participant movement from the starting position to the landing position. The capture data were analyzed by KBST system program. The outcome measures were starting position, maximum velocity, maximum velocity position, and take-off angle. Repeated-Measures ANOVA was conducted to compare the long jump performance across the three trials. The statistical significance was set at p-value < 0.05.Results: There was a statistically significant difference between Test 1 and Test 3 for long jump distance (mean difference = 0.292; Std. Error = 0.129; Sig. = 0.34). However, the mean of take-off angle was similar across the three trials (Test 1 = 12.16°, Test 2 = 12.71°, and Test 3 = 12.95°, respectively).Conclusion: The KBST system was effective in improving long jump students’ performance and also transferring knowledge from the coach to long jump students.
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