Implementation of Data-Based Decision-Making: Linking Research From the Special Series to Practice

Area of Science:

• Education
• Psychology
• Special Education

Background:

• Data-based decision-making (DBDM) is crucial for improving educational outcomes.
• Students with learning disabilities require tailored interventions for academic success.
• Existing research highlights the importance of teacher factors and system-level support in educational interventions.

Purpose of the Study:

• To synthesize findings on data-based decision-making (DBDM) for students with learning disabilities.
• To evaluate the role of teacher knowledge, skills, beliefs, and professional learning in DBDM.
• To examine the impact of systems-level factors on the effectiveness of DBDM.

Main Methods:

• Analysis of research papers from Germany, the Netherlands, and the United States.
• Evaluation of a model of DBDM, including teacher and system-level components.
• Focus on data-based individualization (DBI) as a key DBDM process.

Main Results:

• DBDM, particularly data-based individualization (DBI), can improve achievement for students with learning disabilities, especially with innovative supports like new technologies.
• Teacher professional development (PD) enhances DBDM knowledge and implementation, but practical experience is also critical.
• Classroom-level DBDM does not always guarantee success for students with learning disabilities, and system-level factors are vital.

Conclusions:

• DBDM holds significant potential for improving outcomes for students with learning disabilities.
• Effective implementation of DBDM requires a multifaceted approach, integrating teacher support, practical experience, and robust system-level factors.
• Future research should prioritize understanding and optimizing system-level influences on DBDM, particularly in real-world settings.