Graduates’ career success predicted by mathematical and affective abilities, effective higher-education learning and economic contexts: a bioecological positivity to success model

ABSTRACT

This study posits a bioecological positivity to success (BEPS) model and examines how diverse bioecological factors predict graduates’ career success. The BEPS model with an emphasis on hard (e.g. science, technology, engineering and mathematics [STEM]) and soft (e.g. interpersonal and critical thinking) skills generate a hypothetical model: positive aspects of person (mathematical/hard and affective/soft abilities), process (effective hard and soft competencies learning in higher education) and proximal contexts (original family income and present employment status) predict graduates’ career success (job income and perceived extrinsic, intrinsic and autonomy satisfaction) in early adulthood. Gender, studying STEM, and study years are also included as predictors in the path analysis as control. Path analyses examine the model with cohort data from the Taiwan Education Panel Survey (TEPS) and its follow-up (TEPS-B), which are longitudinal studies of a group of young people (n = 2,700) since grade 7 till age 24–25 years old. Results reveal that soft skills and employment play the most significant roles in graduates’ career success. Hard skills play a minor role. Findings support the BEPS model and provide implications for educational practices and policymaking to emphasise on soft skills learning, employability and entrepreneurship education.

KEYWORDS:

• Bioecological theories
• career success
• higher education
• hard and soft skills

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mei-Shiu Chiu

Dr. Mei-Shiu Chiu is currently a professor of education at National Chengchi University, Taiwan. She completed her doctoral study at the Faculty of Education, Cambridge University, U.K. In 2020, she served as a Fulbright scholar at University of Pennsylvania Graduate School of Education, U.S., working for facilitating dialogues between educational and data sciences for STEM education. Her research interests focus on the design, implementation, and effectiveness evaluation of learning, teaching, and assessment in a variety of areas of knowledge (e.g. mathematics, science, and energy); interactions between emotions, cognition, and culture; and multiple research methods and data analysis methods (including educational and data science methods). She has developed several research-based educational theories, relevant assessment tools, as well as school and teacher development courses for educational and research practices.

Weiyan Xiong

Dr. Weiyan Xiong is a Research Assistant Professor at Lingnan University, Hong Kong. He received his PhD in Higher Education Management from the University of Pittsburgh in 2018. His research interests include comparative, international, and development education, liberal arts education, indigenous education, and higher education management. From 2013 to 2017, Xiong served as a Programme Coordinator at the University of Pittsburgh IISE. He also used to work as a Visiting Student Researcher at the UC Berkeley Myers Center for Research on Native American Issues, a Research Assistant at the Center for International Higher Education of Peking University. Dr. Xiong received his master’s degree in Higher Education and bachelor’s degree in International Political Economy from Peking University, China.

Ping-Yin Kuan

Dr. Ping-Yin Kuan ([email protected]) is a professor in the department of sociology and the International Doctoral Programme in Asia-Pacific Studies, National Chengchi University, Taiwan. His research focuses on using panel data to investigate the impact of family and school environments on adolescents’ educational outcomes and mental health. He has been the principal investigator of the Taiwan Education Panel Survey and Beyond since 2009.