Analytics training for career development and self-development
DOI:
https://doi.org/10.47751/Keywords:
KEY WORDS Machine learning (ML), Learning analytics (LA), Skill development, Professional education and development, Feedback system in training, Self-efficacy in career (SEC), Applications of artificial intelligence (AI)Abstract
Among the many problems that educational institutions face in the era of the labor market is the development of career self-efficacy among students. It was traditionally believed that self-efficacy develops through direct experience of competence, indirect experience of competence, social beliefs and physiological signals. These four factors, especially the first two, are difficult to include in educational and training programs, where the change in skills makes the exact value of the competence of graduates largely unknown and, despite other works presented in this collection, largely unknown. In this article, we discuss a working metacognitive model of career self-efficacy that equips students with the skills needed to assess their skills, attitudes, and values, and then adapts and develops them as they develop in a career context. Our proposed model is one of the complex subsystems in a changing environment, revealing various influencing factors


