Examining self-efficacy and anxiety in data-driven decision-making practices among learning disabilities teachers in Saudi Arabia: a mixed-methods study

Abstract

This mixed-methods sequential explanatory study aimed to evaluate the self-efficacy and anxiety of learning disabilities teachers (LD teachers) regarding data-driven decision-making (DDDM). The initial phase involved surveying 309 LD teachers, followed by in-depth interviews with five participants to gain further insights. The study’s second phase aimed to identify the factors influencing and resources that can enhance teacher self-efficacy in DDDM. Quantitative findings revealed a high level of teacher self-efficacy and minimal anxiety associated with the use of DDDM. The interview analysis uncovered three significant themes affecting teacher self-efficacy: perceptions of DDDM, challenges encountered (such as an inadequate data technology system, lack of necessary skills and knowledge, and resistance to change), and the impact of accountability. The study emphasizes the importance of targeted professional development opportunities and improved access to data analysis tools.

Keywords

• Data science applications in education
• special needs education
• teacher professional development
• evaluation methodologies
• improving classroom assessment
• Assessment & Testing