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1 – 2 of 2Yuting Cui, Fanghui Huang, Zhiqun Zhao and Fan Gao
Firstly, this study diagnosed professional competence amongst Chinese vocational students within a broad range of the manufacturing sectors; then, the authors examined how…
Abstract
Purpose
Firstly, this study diagnosed professional competence amongst Chinese vocational students within a broad range of the manufacturing sectors; then, the authors examined how different types of P-E fit (job, organisation and vocation) and internship quality jointly shape the newly acquired professional competences of interns.
Design/methodology/approach
This study utilised the COMET methodology to conduct a large-scale assessment of professional competence amongst 961 graduates from vocational colleges who had successfully completed internships. Participants actively engaged in the data collection process by responding to questionnaires that sought contextual information concurrently.
Findings
The majority of students have attained fundamental functional competencies, indicating their fulfillment of basic requirements. However, there is a tendency to overlook the cultivation of shaping competence. Three types of P-E fit and task characteristics are positively correlated with professional competence. The indirect relationship between P-E fit and professional competence mediated by task characteristics was verified through P-V fit and P-J fit except for P-O fit. Overall, the model explains 39.2% of the variance in professional competence.
Originality/value
“How to promote professional competence” has been highlighted as an important topic in vocational education. This paper contributes to identify the characteristics of a quality internship program for vocational colleges and firms. These insights are important in considering a student-centred approach, design internships programmes that better fit their own abilities, needs and vocations, avoiding a one-size-fits-all approach to implement internships and thus, enhance students' professional development.
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Keywords
Krishna Mohan A, Reddy PVN and Satya Prasad K
In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best…
Abstract
Purpose
In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG & Harris are used for the process of feature extraction. The proposed method will give the best results when compared to other existing methods.
Design/methodology/approach
This paper introduces the concept and research status of tracks; later the authors focus on the representative applications of deep learning in visual tracking.
Findings
Better tracking algorithms are not mentioned in the existing method.
Research limitations/implications
Visual tracking is the ability to control eye movements using the oculomotor system (vision and eye muscles working together). Visual tracking plays an important role when it comes to identifying an object and matching it with the database images. In visual tracking, deep learning has achieved great success.
Practical implications
The authors implement the multiple tracking methods, for better tracking purpose.
Originality/value
The main theme of this paper is to review the state-of-the-art tracking methods depending on deep learning. First, we introduce the visual tracking that is carried out manually, and secondly, we studied different existing methods of visual tracking based on deep learning. For every paper, we explained the analysis and drawbacks of that tracking method. This paper introduces the concept and research status of tracks, later we focus on the representative applications of deep learning in visual tracking.
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