When we talk about teachers and the use of classroom data, we’re often talking about data produced via assessment – “the systematic collection and analysis of information to improve student learning
How to Navigate a Wealth of Student Data
This word can conjure a lot of things for a lot of people, ranging from the high-stakes world of accountability tests to a student’s own reflections on the quality of work they’re producing. When teachers, students, and families use data to make learning more effective, they navigate a whole constellation of assessment types to understand progress against goals and make choices on a day-to-day basis.
This constellation is even more complex in today’s blended-learning classrooms, where online learning programs can capture and record more data on students’ performance. While item banks make creating assessments quicker and easier for teachers, and online tools allow more to be done with data than ever before, there can be a downside: sometimes these data can be hard to access, difficult to understand, or conflicting.
In our research at The Learning Accelerator (TLA), we’ve observed how blended-learning teachers navigate the wealth of student data produced in assessment cycles across multiple formats, ranging from computer-produced activities or brief written tasks to multiple-choice or authentic demonstration tasks. Every piece of data answers distinct questions for practitioners and learners.
In this post, we’ll share concrete examples from practice and advice for effective data use.
When and why different types of assessment are used to inform instruction
- Diagnostic assessments: typically used at the beginning of the year or a unit to determine what skills or content a student has already mastered and where there might be gaps to fill. Diagnostic assessments can also be used to determine differences in baseline learning given cognitive skill sets.
- Monitoring assessments: used continually (either by the teacher or student) to understand how students are progressing against goals or activities.
- Formative assessments: used frequently to assess mastery of specific skills or content (regardless of activity).
- Summative assessments: used at the end of units or courses and often include assessments of multiple cumulatively gained skills or content knowledge.
- Interim assessments: administered to assess the pace of learning.
- End-of-year assessments: particular types of summative assessments administered after instruction to assess how well students mastered skills or content.
How these assessments come together in a classroom
TLA has found that great teachers embed various assessments throughout the learning cycle, iteratively and transparently, to triangulate progress from emerging data and connect learning to instructional actions. The chart below shows how assessments can work together to help educators make good decisions to support student learning.
Making the data work to support classroom instruction
Data-driven educators work in a cyclical manner. Based on what they know about students and the instructional objectives, they develop a plan, implement it, and then analyze data to understand whether or not objectives were achieved and to identify a series of actions to take next. However, hidden within this seemingly straightforward planning and analysis process is a pretty complex set of data actions:
- Creation: educators design and implement tools and assessments, creating data around student behaviors, progress, and mastery.
- Collection: once data are generated, educators grade assignments and extract, validate, and pool all data into accessible formats.
- Organization: with numerous data sources, educators consolidate and format data into accessible and organized dashboards, allowing for easy manipulation and analysis. They also seek to validate through this process, looking across data from multiple sources to identify patterns of alignment and consistency.
- Analysis: using a variety of data, educators identify trends and triangulate data to gain holistic insights into students’ strengths and growth areas.
- Action: based on group trends and individual student needs, educators plan and adjust instruction for whole groups, small groups, and individual students, generating data to then restart the data cycle.
Each of these steps has different levels of value and difficulty. As educators move through the process and get closer to being able to look at the data (ideally in partnership with students), the potential value of the effort they put in increases. Efforts to support data-driven instruction should, therefore, focus on maximizing the time teachers have in the analysis and action phases to understand progress and plan activities. Unfortunately, though not surprisingly, teachers are usually asked to spend most of their time on lower level tasks, and important data gets “lost” along the way.
Minimizing fatigue around data
Given the many data sets that teachers have access to, especially when leveraging edtech tools, it is important to have a clear sense of what the data provide insight into. Teachers are often given support to analyze and assess different data reports but aren’t typically given the tools they need to become data literate. If we want to reduce fatigue and want teachers to make the best instructional decisions for their students, we must intentionally build their capacity.
Questions for teachers to think about when looking at any student data to drive instruction may include:
- What do the data show mastery of? Standards and substandards? Skills? Units?
- Are there other data sets that either confirm, validate, or provide a different view of mastery?
- Are there multiple students in the classroom who have similar gaps?
- What are the misconceptions students have that prevent them from achieving mastery?
Data can be a powerful tool in the hands of teachers. By engaging with data in an intentional way, educators can focus on providing personalized supports which will help solve challenges in their classrooms and enhance learning for their students.