Understanding historical trends and patterns in student data

Patterns of student performance for a holistic view

Steps for a productive analysis start with organizing and laying out the data to look for patterns in the data common for all students. Patterns should be specific, so instruction can be applied where it’s most needed. For instance, a pattern may appear where students average 85 percent on an assessment, but more than 70 percent miss the same three questions related to a specific standard. Research into this phenomenon can determine if this is an isolated event or something that needs to be addressed.

Determine patterns of need as a first step

In the above example, the data suggest there is a pattern of need (a particular factor affecting student performance). Needs fall into two categories: skill-based and content-based. In the example, if the three misunderstood questions center around one content standard, a pattern is confirmed. The teacher can adjust instruction to address the problem and eliminate any confusion. Identifying patterns of need should be the first step in understanding your student’s strengths and challenges.

It’s important to note that needs are not necessarily a weakness. A need for enrichment may be appropriate in certain circumstances. A student may already have a strong skill level but would benefit from acceleration to advance the learning.

Root cause analysis: A proactive measure

Looking at long-term data for patterns and trends is the first step to improving the reliability of interventions. Root cause analysis gets to the what and the why of the problem. Root cause analysis includes examining data across a group of students to identify what errors were made. Such error analysis will show whether the students made similar or different types of errors.  If the students exhibit similar types of errors, it suggests that the original teaching was not effective, and they all need re-teaching in order to learn the skill. If the error analysis shows that the students made different types of errors, then individual interventions are needed.

Fidelity of interventions

Examining historical trends in student data also sheds light on the fidelity of interventions. Fidelity in intervention is accomplished when:

• The intervention steps were implemented according to the developer guidelines
• The intervention used the recommended frequency
• Intervention sessions used the recommended duration
• Progress monitoring was conducted at regular intervals
• Student progress data were compared with student goals and grade-level benchmarks

Student success depends on effective interventions

In order to help students accelerate learning and offset the learning loss from COVID-related school disruptions, schools need to use effective interventions that include evidence-based instruction.  An important step in providing such interventions is to identify the specific knowledge and skills that students are missing.  Analysis of historical patterns in student data provides insights into the best interventions that can address the learning gaps resulting from COVID. In addition, knowing where to spend time and energy on instruction helps teachers prioritize their efforts within the school day. Historical data review helps teachers identify and address the most urgent learning needs.