What describes data-driven decision making in teaching and its steps?

• A. Rely on intuition and classroom anecdotes.
• B. Collect and analyze data from assessments, identify trends, set goals, adjust instruction, and monitor progress.
• C. Use data only from standardized tests.
• D. Analyze data but skip progress monitoring.

Answer: (B)

Data-driven decision making in teaching means basing instructional choices on evidence gathered from students’ learning, not on gut feelings or anecdotes. The process involves collecting and analyzing data from assessments and ongoing checks, identifying trends across students and time, setting specific, measurable goals, adjusting instruction to address identified needs, and then monitoring progress to see if those changes are making an impact. This creates a continuous feedback loop that keeps teaching responsive to what students actually understand and can do.

Why this approach fits best: collecting data from multiple sources and continually analyzing it ensures decisions are based on real evidence rather than memory or intuition. Setting concrete goals gives you targets to aim for and criteria to measure success. Adjusting instruction based on what the data show helps close gaps and push learning forward. Ongoing progress monitoring confirms whether the changes worked or if further adjustments are needed.

Why the other ideas don’t fit as well: relying on intuition and anecdotes doesn’t provide consistent evidence to support decisions. Using data only from standardized tests misses daily learning and the full range of student skills, and may not capture growth or struggles in real classroom moments. Analyzing data without monitoring progress stops the cycle—there’s no way to confirm that the changes actually improved learning.