Effect of Deep Learning Approach on Career Self-Efficacy: Using Off-Campus Internships of Hospitality College Students as an Example

Abstract

Off-campus internships are among the policies implemented over the years to reduce the academic gap in the hospitality industry. Additionally, career decision making is unavoidable for college students, who can use different learning strategies in internships to gain knowledge beyond textbooks and determine their future direction. Our research aims to explore the mediating role of self-regulated learning in the relationship between deep learning approach (DLA) in hospitality internships and career self-efficacy. Moreover, it uses cognitive engagement as a moderator to explore the relationships between the three constructs. Data are collected through stratified sampling from hospitality college students in Taiwan who completed off-campus internships. Descriptive statistics are employed, and confirmatory factor analysis and structural equation modeling are conducted to analyze the data. Results indicate the following: (1) DLA has a positive influence on self-regulated learning. (2) Self-regulated learning has a positive influence on career self-efficacy. (3) DLA has a positive influence on career self-efficacy. (4) Self-regulated learning mediates the effect of DLA on career self-efficacy. (5) Cognitive engagement moderates the effect of DLA and self-regulated learning on career self-efficacy. Theoretical and practical implications are also discussed.