What does 'data-driven instruction' primarily involve?

Data-driven instruction primarily involves making instructional decisions based on student performance data. This approach focuses on collecting and analyzing information about students' learning processes and outcomes to tailor teaching strategies effectively. By using performance data, educators can identify strengths and weaknesses within their students, allowing for targeted interventions and adjustments to improve learning experiences.

This method emphasizes the importance of ongoing assessment and feedback in the learning environment, which contributes to a more personalized and effective teaching approach. The goal is to enhance student learning by ensuring that instructional strategies are aligned with the actual needs and abilities of the students, resulting in better educational outcomes.

In contrast, the other options do not accurately represent the concept of data-driven instruction. For instance, teaching without considering student data negates the fundamental premise of this approach, as it relies heavily on data analysis to inform teaching practices. Similarly, encouraging students to guess on their assessments lacks the structured, evidence-based methodology that defines data-driven instruction. Lastly, while standardized tests can provide valuable data, relying solely on them to guide all teaching methodologies is overly restrictive and does not encompass the broad range of data sources that can inform instructional decisions.