Student Satisfaction, Perceived Employability Skills, and Deep Approaches to Learning: A Structural Equation Modeling Analyses

This study explored the relationship of Deep Approaches to Learning (DAL) with overall students' satisfaction and perceived employability skills in the field of Science, Technology, Engineering and Mathematics (STEM) for the undergraduate seniors in the U.S. The study also aimed to investigate whether there is a difference between students in STEM and non-STEM fields on the relationship of DAL to overall student satisfaction and students' perceived employability skills. The data for the analysis was taken from the National Study of Student Engagement (NSSE) data. The Structural Equation Modeling (SEM) analysis was applied to explore the relationship between students' Deep Approaches to Learning (DAL), overall students' satisfaction and their perceived employability skills. The measurement invariance testing explored whether estimated factors are measuring the same constructs for STEM and non-STEM groups. The findings of the study show that HO and RI construct was found to have statistically significant positive total (direct and indirect) effect on overall student satisfaction. Further, the results show that HO and RI learning activities were identified as the statistically significant factors in predicting students' perceived employability skills for STEM students. The HO and RI have a statistically significant positive effect on perceived employability skills for STEM and the non-STEM students. The STEM students have a higher effect of HO learning activities on perceived employability skills than the non-STEM students. Further, the direct effect of perceived employability skill on overall student satisfaction is also positive for both the groups. The findings of the study confirmed the indirect effect of employability on overall students' satisfaction for both STEM and non-STEM students. This study has created strong groundwork for future researchers to use the measurement models and the hypothesized full structure model for invariance testing among the groups of STEM and non-STEM in higher education in the U.S. Thus, this measurement model has a strong generalizability to both STEM and non-STEM groups. The implications and limitations of study are further discussed.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Educational Research and Evaluation

Department dc:contributor.department

Educational Research and Evaluation

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Kapania, Madhu Bala

Chairs dc:contributor.committeechair

• Skaggs, Gary E.
• Savla, Jyoti S.

Committee members dc:contributor.committeemember

• Kniola, David John
• Miyazaki, Yasuo

• Deep Approaches to Learning
• STEM
• Non-STEM
• Employability Skills
• Satisfaction.

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:37329

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/115324