Using data for…

...informed teaching.

Hello all. Welcome to the 157th edition of TEPS Weekly!

When teachers hear the word data, they often think first of test scores, grades and report cards. That is understandable. These are the most visible forms of evidence in schools. They are concrete, easy to record, and often the first thing that school systems ask for. Then there are some who think of the role of data in their teaching, and these teachers/educators also think of data from formative assessments to inform their teaching. Today we introduce you to these and also go beyond these.

A student’s assessment score may tell a teacher that there is a problem. It does not always tell the teacher why that problem exists or what to do next. For that, teachers need a wider view of data – not only marks and attendance, but also participation, learner profile, home context (if possible), and evidence of whether their own teaching methods are working.

A practical framework for teacher-useful data

Category of data	Source	Frequency	Instructional use
Learning data	Tests, quizzes, notebooks, oral responses, exit tickets	Weekly / End of unit	To understand what students know, where they are struggling, and what misconceptions they have
Engagement and behaviour data	Observation, participation records, homework logs, group work notes	Daily / Weekly	To understand attention, participation, work completion, collaboration, and classroom behaviour
Learner profile data	Student conversations, writing samples, reading records, surveys, observation	Monthly / Term-wise	To understand language comfort, interests, confidence, goals, and social style
Context data	Attendance records, parent conversations, school records, pastoral notes	Weekly / Term-wise	To understand barriers to learning such as absences, home support, access to resources, and well-being
Teaching effectiveness data	Teacher reflection, student response to lesson types, classroom climate feedback	After lessons / weekly	To understand which teaching methods are helping students learn and where teaching needs adjustment

This framework is useful because it helps teachers avoid jumping to conclusions. Instead of reacting quickly to a visible problem, the teacher can ask: Which category of data should I check before deciding what to do?

Let’s explore this with an example:

Situation 1: Students scoring low in assessments

A Grade 7 Science teacher finds that many students have scored poorly on a quiz on heat and temperature.

Data the teacher checks

The teacher can do these different checks at different points of time and also at different frequencies. And as you will see, the teachers may informally be clocking these data, anyway.

Category	What the teacher checks	What the teacher finds
Learning data	Written quiz responses, written exit tickets	~60% students are confusing heat and temperature
Engagement and behaviour data	Participation during previous lessons	~90% low-scoring students were passive during explanation-heavy lessons
Learner profile data	Language proficiency	~40% students seem to understand orally but struggle to write accurate explanations
Context data	Attendance	~10% students missed one lesson in the sequence
Teaching effectiveness data	Teacher reflection	The lesson relied heavily on verbal explanation and not enough demonstration

What the teacher should do

The teacher now has a much clearer response plan:

• explicitly teach the difference between the two terms using simple language, as the students are specifically struggling with the difference and probably understanding in English

• reteach the concept using a practical demonstration and visual comparison, to overcome the passiveness of the lecture mode

• give students sentence starters or oral rehearsal before written work, as the students need help with articulation in English language 

• build in pair discussion so passive students process the idea before answering AND students who were absent are also helped by their partners

Why this matters

If the teacher had only looked at marks, the conclusion might have been: “Students have a confusion between heat and temperature.” But connecting this information with other data, the conclusion becomes more useful: “Students have a shared misconception, some need language support, some missed instruction, and my teaching approach needs adjustment.” This conclusion leads to better and more informed teaching.

Each of the categories mentioned for data collection gives us a unique perspective, and an integration of these points of view is definitely better than the sum of its parts – the data that a majority of the students are confusing heat and temperature tells the teacher one thing; additionally, the data says that 40% of the students have a language issue. Both these together, give us a reason why students may be confusing heat and temperature.   

It’s too complicated!

Some of us may feel that having five different categories for data collection would be too much for a teacher. They are already over-burdened with so much compliance stuff. We think teachers are already subconsciously collecting or at least clocking the type of data that we have mentioned. Teachers ‘know’ who the more introverted students are, they have a good idea of their students’ context and sometimes also the dynamics at home. So, teachers do not need complicated systems to begin using data well – the solution needs to be easy to use and give them quick insights as shown in the earlier example.  

The reflective layer is essential

One of the most important parts of the framework is the final category – teaching effectiveness data. Teachers are often asked to collect data about students, but not always encouraged to collect data about their own practice. Yet this is what makes the process truly professional.

A reflective teacher can ask:

• Which lesson structure worked better?

• When were students most engaged?

• Which explanation created confusion?

• Which students responded well to visuals, discussion, guided practice, or independent work?

• Did my assessment format show what students knew, or hide it?

This kind of reflection should not feel like self-blame. It is simply part of improving practice. In fact, it is the step that connects data to action.

Before we conclude, here is another example of how all-round data is useful for teaching:

Situation 2: Homework and classwork are often incomplete

A Grade 8 Mathematics teacher finds that several students regularly submit incomplete work.

Data the teacher checks

Category	What the teacher checks	What the teacher finds
Learning data	Error patterns	Many get stuck in the first step of the problem
Engagement and behaviour data	Homework tracker, notebook checks	Some students begin tasks but do not finish them
Learner profile data	Confidence and self-belief	A few students say they are “bad at maths”
Context data	Parent communication and home support	Some students do not get help or time at home
Teaching effectiveness data	Teacher reflection	Homework may be too long and not matched to readiness level

What the teacher should do

The solution is not simply to demand better compliance. A more effective plan would be:

• break the task into steps and model the first one clearly, as student engagement is lowering with increasing number of steps AND students are getting stuck on the first step of the problem

• provide one scaffolded example before independent practice, because students are getting stuck on the first step of the problem

• provide some practice time in class AND check understanding before assigning homework as some students may not get support at home

• give feedback that builds competence and confidence, not shame, as students think that they are ‘bad at maths’

• shorten the homework and focus it on fewer, better-chosen questions

Why this matters

Incomplete work is often treated as a discipline issue. Sometimes it is. But often it is also a learning issue, a confidence issue, or a context issue. Teachers need data from more than one category before deciding.

Conclusion

In the end, data-informed teaching is not about collecting more and more information. It is about collecting the right information, interpreting it carefully, and using it to make better decisions. Marks matter. Behaviour matters. Learner profile matters. Parent involvement and context matter. Teacher reflection matters too. The strongest classrooms are not those where the teacher has the most data. They are the ones where the teacher uses different kinds of data to understand students more fully and teach more effectively.

This was just the first instalment of how data can inform teaching. We will dive deeply into each of the categories of data that we have written about, and show the nuances that we can find even within each category.

---

If you found this newsletter useful, please share it.

If you received this newsletter from someone and you would like to subscribe to us, please click here.

Edition: 5.12