Educators, students, and families weathered historic interruptions in learning over the past few years due to the COVID-19 pandemic. Student achievement, predicted to slide in the ongoing chaos, revealed its fragile nature in the NAEP’s recently released Long-Term Trend results from 2020–2022. The report showed the first-ever decline in mathematics and the most significant drop in reading achievement since the 1980s.
Effective student data analysis
As educators address this new landscape, accurate interventions to accelerate student learning are in demand. In order to know what type of intervention a student needs, high-quality data remain an essential element for educators. Teacher review and understanding of students’ data will guide instructional decisions and create positive outcomes for all students.
Proper analysis of student data is a skill worth developing. Educators need to:
- Review prior intervention outcomes
- Identify each student’s current learning needs
- Select the best intervention to match these needs
- Measure the effectiveness of interventions
In order to make sense of prior efforts to address student learning needs, teachers can review historical data trends to view the complete picture of a student’s learning progress.
Gathering and analyzing multiple data points over weeks and months is the most accurate method to inform instruction. Pulling a single data point for use can be tempting, especially when the pressure to implement interventions is intense, but caution is recommended here. A single data point may show an aberration. If educators act upon a single data point, they may implement unnecessary, time-wasting interventions. Taking the long view by analyzing long-term data and identifying historical trends and patterns provides a solid base to draw from to guide instructional decisions.
Historical trends: An effective gauge for student mastery
Viewing historical trends in student data involves reviewing the lows and the highs of a student’s performance over time and helps educators identify where a student is succeeding and where deficits remain. Only multiple data points over months and sometimes years can accurately depict a student’s mastery level. Reviewing student data starting in November each year provides educators with qualitative and quantitative student performance patterns that can inform future instruction.
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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.
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