WHAT IS DATA?

Data are individual facts, statistics, or items of information, often numeric. In a more technical sense, data are a set of values of qualitative or quantitative variables about one or more persons or objects, while a datum is a single value of a single variable.

Now, if we talk about data mainly in the field of science, then the answer to “what is data” will be that data is different types of information that usually is formatted in a particular manner. All the software is divided into two major categories, and those are programs and data. Programs are the collection made of instructions that are used to manipulate data. So, now after thoroughly understanding what is data and data science, let us learn some fantastic facts.

Types and Uses of Data

Growth in the field of technology, specifically in smartphones has led to text, video, and audio is included under data plus the web and log activity records as well. Most of this data is unstructured.

The term big data is used in the data definition to describe the data that is in the petabyte range or higher. Big Data is also described as 5Vs: variety, volume, value, veracity, and velocity. Nowadays, web-based eCommerce has spread vastly, business models based on Big Data have evolved, and they treat data as an asset itself. And there are many benefits of Big Data as well, such as reduced costs, enhanced efficiency, enhanced sales, etc.

The meaning of data expands beyond the processing of data in computing applications. When it comes to what data science is, a body made of facts is called data science. Accordingly, finance, demographics, health, and marketing also have different meanings of data, which ultimately make up different answers for what is data.

Qualitative Data Type

Qualitative or Categorical Data describes the object under consideration using a finite set of discrete classes. It means that this type of data can’t be counted or measured easily using numbers and therefore divided into categories. The gender of a person (male, female, or others) is a good example of this data type.

These are usually extracted from audio, images, or text medium. Another example can be of a smartphone brand that provides information about the current rating, the color of the phone, category of the phone, and so on. All this information can be categorized as Qualitative data. There are two subcategories under this:

Nominal

These are the set of values that don’t possess a natural ordering. Let’s understand this with some examples. The color of a smartphone can be considered as a nominal data type as we can’t compare one color with others.

It is not possible to state that ‘Red’ is greater than ‘Blue’. The gender of a person is another one where we can’t differentiate between male, female, or others. Mobile phone categories whether it is midrange, budget segment, or premium smartphone is also nominal data type.

Ordinal

These types of values have a natural ordering while maintaining their class of values. If we consider the size of a clothing brand then we can easily sort them according to their name tag in the order of small < medium < large. The grading system while marking candidates in a test can also be considered as an ordinal data type where A+ is definitely better than B grade. 

These categories help us deciding which encoding strategy can be applied to which type of data. Data encoding for Qualitative data is important because machine learning models can’t handle these values directly and needed to be converted to numerical types as the models are mathematical in nature.

For nominal data type where there is no comparison among the categories, one-hot encoding can be applied which is similar to binary coding considering there are in less number and for the ordinal data type, label encoding can be applied which is a form of integer encoding.

Quantitative Data Type

This data type tries to quantify things and it does by considering numerical values that make it countable in nature. The price of a smartphone, discount offered, number of ratings on a product, the frequency of processor of a smartphone, or ram of that particular phone, all these things fall under the category of Quantitative data types.

The key thing is that there can be an infinite number of values a feature can take. For instance, the price of a smartphone can vary from x amount to any value and it can be further broken down based on fractional values. The two subcategories which describe them clearly are:

Discrete

The numerical values which fall under are integers or whole numbers are placed under this category. The number of speakers in the phone, cameras, cores in the processor, the number of sims supported all these are some of the examples of the discrete data type.

Continuous

 The fractional numbers are considered as continuous values. These can take the form of the operating frequency of the processors, the android version of the phone, wifi frequency, temperature of the cores, and so on.

DATA DRIVEN INSTRUCTION

What is data driven instruction?

In short, data driven instruction involves gathering together a database of information about the students in each classroom, and using that information to improve the quality of teaching in the classroom.

While much of this work is done by the teachers themselves, it’s up to school leadership to build a culture of data driven instruction.

Every classroom is full of students with their own needs, abilities, and levels of understanding. Data driven instruction aims to take all of this information into account when building curriculums, or even directly when teaching in the classroom.

There are three main steps involved in data driven instruction:

• Data collection: Gather information from class assessments and standardized test results, as well as observations from the teacher, and create a database on information.
• Data analysis: Separate essential information from non-essential information. Watch for patterns and dive into the reasons behind these results. Draw conclusions and formulate teaching plans.
• Action: Congratulate your class and move to the next topic, or prepare time to re-teach certain ideas to the class.

Going deeper than the “what”

Until now, many schools have taken data driven instruction to mean building up a database of what the students know and what they don’t know.

However, to truly benefit the students in your school, you need to understand more than just the “whats”.

Exam scores and standardized test results only tell you the knowledge level of the students. You’ll need to dig deeper to understand the “why” and “how” of the situation.

For example, imagine that the majority of one science class doesn’t have the required knowledge to pass the standardized test. The ‘what’ is clear: they lack equivalent understanding of the subject.

Now, it’s time to figure out the “why” and “how”.

So, why did these students miss key information taught in the class? Is there some sort of distraction that can be minimized? Did the way the information was presented have an effect on their understanding?

Then, how can these students be re-taught in such a way that the information sticks? If they clearly understood other topics that were taught during the same semester, how did they learn those topics? How can you apply the same principles to re-teach the information they didn’t learn?

Gathering the necessary data to answer the “what”, “why”, and “how” is the basis for data driven instruction.

So, what strategies will help you develop your school’s data culture?

11 strategies to build a culture of data driven instruction in your school

1. Involve teachers in the process

While you as the school leader are setting the groundwork for data driven instruction, it’s the teachers that will have to do most of the heavy lifting.

That’s why it’s important for teachers to be involved in the process of creating and building your data driven culture.

There’s a lot of work involved in creating actionable plans that get to the heart of student assessment and data analysis. So, involve your teachers in making these plans. Get together and set up the routines and standards that will be the basis for data driven instruction in your school.

Teacher training is also extremely important. Once you set the standards for data collection, analysis, and application, teachers need to understand exactly what that means for them in their day-to-day activities in and out of the classroom.

So, train teachers to quickly analyze data and draw conclusions that spark action, thus helping them implement a data driven instruction policy.

2. Slowly scale your efforts

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Diving into data collection and analysis for every class in your entire school may seem daunting.

That’s because it’s too much work.

Instead of the ‘all-or-nothing’ approach, try to start with just one class.

Gather data on student knowledge levels, and how they learn. With the goal of starting small, build a process that makes data easy to collect and translate into action.

For example, have teachers track the number of times students ask for clarification on a topic in the classroom, as well as what teaching strategies they were using at the time. This data can be easily translated into an action: remove teaching methods that don’t present the information clearly.

Once you start to see results in one or two classrooms, you can start to expand the same methods of data collection and application to the rest of the school.

3. Set the right standards for assessments

Don’t spread yourself too thin. There are plenty of things that students ‘should’ be learning. And teachers may have their own ideas of what is important information in a lesson plan.

However, to have definite, stable results in data driven instruction, you need to define the standards of assessment.

That means taking each unit and answering the following questions:

• What information is vital for students to learn?
• What will they need to know and understand in order to pass SATs or other important exams?
• What information would absolutely need to be re-taught if students hadn’t mastered it by a certain time?

Once these standards are in place, it’s easier for teachers and students to work towards solid learning goals, and to collect the necessary data for better teaching.

4. Build routines for interim assessments

While summative are important, never forget that learning is an ongoing process. If a number of students never mastered a topic that was covered at the beginning of the school year, it would be easier to address the problem earlier than in the weeks before the summer break.

So, work with the teachers at your school to build a system for interim or summative assessment

First, take the standards that you set above. Then, divide the main topics throughout the school year.

For example, let’s say an English class must master the topic of sentence structure by the end of the semester. So, take that topic and divide it into its main parts. What is the goal date for students to master an understanding of adjectives, verbs, subjects, and objects? When do they need to understand the placement of all these parts in the sentence?

Then, based on these main parts of the essential topic, set dates for interim assessments. Whether it’s through tests, team projects, essays, etc., students must be able to demonstrate mastery of the topic in question.

These interim assessments will help teachers plan for the needs of each student, giving them time to re-teach essential ideas while the topic is still in their mind, rather than weeks or months later.

5. Collect only the data you need

Part of the reason that data driven instruction is so daunting is that there is just so. Much. Data.

Implementing a successful strategy involves thinning down the data only to what is necessary.

Obviously, the teachers in your school have plenty on their plate already: don’t add wading through excessive data to their workload.

Instead, make sure that your data collection processes center on information that is essential. While making sure that your assessments are standardized is important, as discussed above, another way to do this would be giving teachers specific guidelines on how to collect and analyze student data.

That way, all the teachers involved are collecting only necessary data, and the time they spend on analyzing that data is more focused on what’s really important.

6. Set goals that are visible for students

The teachers aren’t the only ones involved in collecting and analyzing student data: the students can also get involved!

After all, the endgame of data driven instruction is to help students reach educational goals. So, show them how they’re doing with those goals!

This can be done by creating visual goals and allowing students to measure their own progress. Help teachers plan time for student self-analysis. Give kids the opportunity to look back on their work, see what they’ve accomplished and develop a growth mindset

To make this strategy really stand out, make the progress visual.

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For example, when coming to the end of a unit, teachers can use classroom response systems (or ‘clickers’) to get a fast overview of how much the class understands of the topic. These fun, interactive tests allow all students (even the really shy ones in the back) to participate.

At the end of the test, most clicker systems produce a bar chart that displays how many students chose each answer choice. This gives students and teachers an easy view of the progress they’ve made, or where they’re still lacking.

7. Use EdTech that displays learning progress for students

It’s no secret that ed-tech is making strides in the classroom.

But did you know it can actually help you (and your teachers) to implement data driven instruction?

In fact, 75% of teachers identify data driven instruction as a top trend for EdTech.

That’s up from just 28% in 2017.

Why the increase? Because most ed-tech programs take advantage of the answers provided by students to give teachers clear data about what’s going on in their classroom.

8. Build a schedule for data analysis

It’s time to give your teachers guidelines for analyzing data.

For example, instead of asking teachers to write out their conclusions after reading through the data, create a uniform process for data analyzation. You could do this by creating reports with short, specific questions that teachers must answer on a scale of 1-10 based on the data they’ve collected.

• What kind of mastery does the class (or student) have in this topic?
• How prepared is the class to answer questions about this topic on a standardized test?
• Can the class explain with ease their understanding of this topic?

Then, teachers can list specific knowledge gaps or weaknesses that they’ve seen in either individual students or the class as a whole.

Next, they’ll need to analyze the data collected about teaching methods. Using the same 1-10 scale, they can rate the different teaching methods they used by how well the class responded to them.

Lastly, teachers should list at least 3 actions they plan to take in order to improve their teaching for the next unit. This could include re-teaching certain topics, or changing up their in-classroom methods to make the information stick better.

Now, you need to build a schedule for your teachers to analyze that data.

Some schools set specific time in the schedule of the teachers to analyze the data they’ve collected. Ideally, this would be soon after assessments are made.

Having a clear process and schedule helps teachers and school leaders to keep a handle on student progress.

9. Encourage teachers to collaborate with each other

When it comes to data analyzation, teachers should know that they don’t have to go it alone. When scheduling time for data analyzation, encourage teachers to work together to get through the data they’ve collected.

This method helps both teachers and students to reap the benefits of data driven instruction. They say two work better than one, and this is a prime example: two or more teachers working together to understand the data they’ve collected and brainstorm ideas of how to proceed.

Teacher collaboration is also a form of professional development. Teachers will learn valuable skills from each other, and build off of each other’s ideas to create an even better learning experience in the classroom.

10. Review the effects of re-teaching

So, after assessment, a class needed re-teaching of an essential topic. Instead of just going through the information again and moving on, it’s important that teachers revisit their assessments to ensure that students are really getting the sense of the information.

Using clicker tests as mentioned above is a great way to get a fast overview of the class’s overall understanding of a topic. This method won’t take a lot of time, and allows teachers to see immediate feedback on whether the re-teaching had the desired effect.

11. Chart the progress of your school as a whole

Once your data driven instruction plan is in place, it will be interesting to watch what a difference it makes in your classrooms.

So, make sure to record a baseline from the starting point. Store those first classroom assessments and the analysis that teachers took away from the data they collected.

This will allow you to see the incredible results that data driven instruction brings.

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