Precision in Special Education : Insights from a Systematic Review on Data-Based Decision Making

Abstract

This research aims to identify the types of special needs that benefit from Data-Based Decision Making (DBDM), the stages of its implementation, and the challenges teachers face in executing DBDM effectively. This research used a qualitative approach, with a systematic literature review method using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis), systematically analyzing relevant articles from the Sage Journal and Wiley Online database scopus indexed published between 2013-2023. A total of 54 articles were initially identified, and through a thorough screening process, 5 articles were included for in-depth review. The data analysis technique for this research used a content analysis approach. The implementation of Data-Based Decision Making (DBDM) supports special needs students facing academic (e.g., reading and writing difficulties) as well as emotional or behavioral challenges. While Curriculum-Based Measurement (CBM) and Mastery Measures with clear decision rules are often used for academic difficulties, DBDM for behavioral issues is more complex due to the diversity of behaviors and required tools. Teachers are encouraged to apply DBDM, provided they develop skills in assessment selection, data processing, and analysis to adjust interventions effectively. Successful DBDM requires strong support from various school stakeholders. The review highlights the need for specialized training for teachers to enhance their competence in applying DBDM for diverse special needs students.

Keywords

• data-based decision making
• special needs student
• teacher skills
• assessment tools
• decision rules.