Date
12-4-2025
Department
School of Education
Degree
Doctor of Philosophy in Education (PhD)
Chair
Treg Hopkins
Keywords
self-efficacy, digital learning, professional development, socioeconomic status, digital divide, ISTE standards
Disciplines
Educational Leadership
Recommended Citation
Steigelman, Sabrina C., "A Causal-Comparative Study on the Impact of Digital Learning Competency Professional Development and Socioeconomic Status on Teacher Digital Self-Efficacy" (2025). Doctoral Dissertations and Projects. 7687.
https://digitalcommons.liberty.edu/doctoral/7687
Abstract
The purpose of this quantitative study was to examine the relationship between teacher self-efficacy, participation in digital learning professional development, and the socioeconomic status of schools. A causal-comparative design was used to investigate how these variables influenced teacher self-efficacy in implementing digital learning strategies aligned with the International Society for Technology in Education (ISTE) standards. The sample included K-12 educators from public schools across North Carolina, representing both low and mid- socioeconomic school settings. Data were collected through a Qualtrics survey, which included the Technology Integration Confidence Scale Version 3 (TICS V.3) and a demographic questionnaire. A two-way ANOVA was conducted using IBM SPSS software to analyze the effects of socioeconomic status and professional development participation on teacher self-efficacy. Results revealed a statistically significant main effect for professional development participation, with participants reporting higher self-efficacy. No significant main effect was found for socioeconomic status, nor was there a significant interaction between socioeconomic status and professional development. These findings suggest that professional development aligned to digital learning competencies can effectively strengthen teacher self-efficacy, regardless of socioeconomic context. Future research should use experimental or mixed-methods design to explore causal relationships, triangulate self-report data through artifacts or learning management analytics. It should also expand research into additional regions and utilize instruments that reflect evolving technologies such as artificial intelligence.
