Trending December 2023 # How To Categorize A Year As A Leap Or Non # Suggested January 2024 # Top 21 Popular

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Vue can be defined as a progressive framework for building the user interfaces. It has multiple directives that can be used as per the user needs. The basic core library is mainly focused on building the view layer only and is also easy to pick up other libraries or integrate with them.

In this article, we will use the Vue filters to check whether a string is a Leap year or not. A leap year has 366 days, whereas a non-Leap year has only 365 days. We can use logic to check whether a year is a Leap or not. If a year is divisble by 400 or 4 it is a leap year, else it is not a leap year.

if (year % 100 === 0) { if (year % 400 === 0) { return "Leapyear"; } else { return "Non-Leapyear"; } } else { if (year % 4 === 0) { return "Leapyear"; } else { return "Non-Leapyear"; } } Steps

We can follow these steps to categorize a year as a leap or non-leap year −

Define a function called leapyear that takes a year parameter.

Check if the year is divisible by 100 using the modulus operator (%) to get the remainder when the year is divided by 100. If the remainder is 0, it means the year is evenly divisible by 100.

If the year is divisible by 100, check if it is also divisible by 400. If the remainder when the year is divided by 400 is 0, it means the year is a leap year. Return the string “Leapyear” in this case.

If the year is divisible by 100 but not by 400, it is not a leap year. Return the string “Non-Leapyear” in this case.

If the year is not divisible by 100, it may still be a leap year if it is divisible by 4. Check if the remainder when the year is divided by 4 is 0. If it is, the year is a leap year. Return the string “Leapyear” in this case.

If the year is not divisible by 100 and not divisible by 4, it is not a leap year. Return the string “Non-Leapyear” in this case.

Example: Checking if a year is a Leap year or not

First, we need to create a Vue project. To do this you can refer to this page. Create two files chúng tôi and chúng tôi in your Vue project. The file and directory with code snippets are given below for both files. Copy and paste the below code snipped in your Vue project and run the Vue project. You shall see the below output on the browser window.

FileName – app.js

Directory Structure — $project/public — app.js

const parent = new Vue({ el: "#parent", data: { year1: 2023, year2: 1996, year3: 1900, year4: 2000, year5: 1997, }, filters: { leapyear: function (year) { if (year % 100 === 0) { if (year % 400 === 0) { return "Leapyear"; } else { return "Non-Leapyear"; } } else { if (year % 4 === 0) { return "Leapyear"; } else { return "Non-Leapyear"; } } }, }, });

FileName – index.html

Directory Structure — $project/public — index.html

Run the following command to get the below output −

Complete Code

const parent = new Vue({ el: “#parent”, data: {year1: 2023, year2: 1996, year3: 1900, year4: 2000, year5: 1997,}, filters: { leapyear: function(year) { if (year % 100 === 0) { if (year % 400 === 0) { return “Leapyear”; } else { return “Non-Leapyear”; } } else { if (year % 4 === 0) { return “Leapyear”; } else { return “Non-Leapyear”; } } }, }, });

In the above example, we check for five years and display if the particular year is a leap year or non-leap year.

You're reading How To Categorize A Year As A Leap Or Non

A Gentle Introduction To Handling A Non


They all fall under the concept of time series data! You cannot accurately predict any of these results without the ‘time’ component. And as more and more data is generated in the world around us, time series forecasting keeps becoming an ever more critical technique for a data scientist to master.

But time series is a complex topic with multiple facets at play simultaneously.

For starters, making the time series stationary is critical if we want the forecasting model to work well. Why? Because most of the data you collect will have non-stationary trends. And if the spikes are erratic how can you be sure the model will work properly?

The focus of this article is on the methods for checking stationarity in time series data. This article assumes that the reader is familiar with time series, ARIMA, and the concept of stationarity. Below are some references to brush up on the basics:

Table of contents

A Short Introduction to Stationarity

Loading the Data

Methods to Check Stationarity

ADF Test


Types of Stationarity

Strict Stationary

Trend Stationary

Difference Stationary

Making a Time Series Stationary


Seasonal Differencing

Log transform

1. Introduction to Stationarity

‘Stationarity’ is one of the most important concepts you will come across when working with time series data. A stationary series is one in which the properties – mean, variance and covariance, do not vary with time.

Let us understand this using an intuitive example. Consider the three plots shown below:

In the first plot, we can clearly see that the mean varies (increases) with time which results in an upward trend. Thus, this is a non-stationary series. For a series to be classified as stationary, it should not exhibit a trend.

Moving on to the second plot, we certainly do not see a trend in the series, but the variance of the series is a function of time. As mentioned previously, a stationary series must have a constant variance.

If you look at the third plot, the spread becomes closer as the time increases, which implies that the covariance is a function of time.

The three examples shown above represent non-stationary time series. Now look at a fourth plot:

In this case, the mean, variance and covariance are constant with time. This is what a stationary time series looks like.

Think about this for a second – predicting future values using which of the above plots would be easier? The fourth plot, right? Most statistical models require the series to be stationary to make effective and precise predictions.

So to summarize, a stationary time series is the one for which the properties (namely mean, variance and covariance) do not depend on time. In the next section we will cover various methods to check if the given series is stationary or not.

2. Loading the Data

In this and the next few sections, I will be introducing methods to check the stationarity of time series data and the techniques required to deal with any non-stationary series. I have also provided the python code for applying each technique. You can download the dataset we’ll be using from this link: AirPassengers.

Before we go ahead and analyze our dataset, let’s load and preprocess the data first.

#loading important libraries import pandas as pd import matplotlib.pyplot as plt %matplotlib inline #reading the dataset train = pd.read_csv('AirPassengers.csv') #preprocessing train.timestamp = pd.to_datetime(train.Month , format = '%Y-%m') train.index = train.timestamp train.drop('Month',axis = 1, inplace = True) #looking at the first few rows #train.head()



1949-01-01 112

1949-02-01 118

1949-03-01 132

1949-04-01 129

1949-05-01 121

Looks like we are good to go!

3. Methods to Check Stationarity

The next step is to determine whether a given series is stationary or not and deal with it accordingly. This section looks at some common methods which we can use to perform this check.

Visual test

Consider the plots we used in the previous section. We were able to identify the series in which mean and variance were changing with time, simply by looking at each plot. Similarly, we can plot the data and determine if the properties of the series are changing with time or not.


Although its very clear that we have a trend (varying mean) in the above series, this visual approach might not always give accurate results. It is better to confirm the observations using some statistical tests.

Statistical test

Instead of going for the visual test, we can use statistical tests like the unit root stationary tests. Unit root indicates that the statistical properties of a given series are not constant with time, which is the condition for stationary time series. Here is the mathematics explanation of the same :

Suppose we have a time series :

yt = a*yt-1 + ε t

where yt is the value at the time instant t and ε t is the error term. In order to calculate yt we need the value of yt-1, which is :

yt-1 = a*yt-2 + ε t-1

If we do that for all observations, the value of yt will come out to be:

yt = an*yt-n + Σεt-i*ai

If the value of a is 1 (unit) in the above equation, then the predictions will be equal to the yt-n and sum of all errors from t-n to t, which means that the variance will increase with time. This is knows as unit root in a time series. We know that for a stationary time series, the variance must not be a function of time. The unit root tests check the presence of unit root in the series by checking if value of a=1. Below are the two of the most commonly used unit root stationary tests:

ADF (Augmented Dickey Fuller) Test

The Dickey Fuller test is one of the most popular statistical tests. It can be used to determine the presence of unit root in the series, and hence help us understand if the series is stationary or not. The null and alternate hypothesis of this test are:

Null Hypothesis: The series has a unit root (value of a =1)

Alternate Hypothesis: The series has no unit root.

If we fail to reject the null hypothesis, we can say that the series is non-stationary. This means that the series can be linear or difference stationary (we will understand more about difference stationary in the next section).

Python code:

#define function for ADF test from statsmodels.tsa.stattools import adfuller def adf_test(timeseries):     #Perform Dickey-Fuller test:     print ('Results of Dickey-Fuller Test:')     dftest = adfuller(timeseries, autolag='AIC')     dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])     for key,value in dftest[4].items():        dfoutput['Critical Value (%s)'%key] = value     print (dfoutput) #apply adf test on the series adf_test(train['#Passengers']) Results of Dickey-Fuller Test: Test Statistic 0.815369 p-value 0.991880 #Lags Used 13.000000 Number of Observations Used 130.000000 Critical Value (1%) -3.481682 Critical Value (5%) -2.884042 Critical Value (10%) -2.578770 dtype: float64

Test for stationarity: If the test statistic is less than the critical value, we can reject the null hypothesis (aka the series is stationary). When the test statistic is greater than the critical value, we fail to reject the null hypothesis (which means the series is not stationary).

2 . KPSS (Kwiatkowski-Phillips-Schmidt-Shin) Test

KPSS is another test for checking the stationarity of a time series (slightly less popular than the Dickey Fuller test). The null and alternate hypothesis for the KPSS test are opposite that of the ADF test, which often creates confusion.

The authors of the KPSS test have defined the null hypothesis as the process is trend stationary, to an alternate hypothesis of a unit root series. We will understand the trend stationarity in detail in the next section. For now, let’s focus on the implementation and see the results of the KPSS test.

Null Hypothesis: The process is trend stationary.

Alternate Hypothesis: The series has a unit root (series is not stationary).

Python code:

#define function for kpss test from statsmodels.tsa.stattools import kpss #define KPSS def kpss_test(timeseries): print ('Results of KPSS Test:') kpsstest = kpss(timeseries, regression='c') kpss_output = pd.Series(kpsstest[0:3], index=['Test Statistic','p-value','Lags Used']) for key,value in kpsstest[3].items(): kpss_output['Critical Value (%s)'%key] = value print (kpss_output)

Results of KPSS test: Following are the results of the KPSS test – Test statistic, p-value, and the critical value at 1%, 2.5%,  5%, and 10% confidence intervals. For the air passengers dataset, here are the results:

Results of KPSS Test: Test Statistic 1.052175 p-value 0.010000 Lags Used 14.000000 Critical Value (10%) 0.347000 Critical Value (5%) 0.463000 Critical Value (2.5%) 0.574000 Critical Value (1%) 0.739000 dtype: float64

Test for stationarity: If the test statistic is greater than the critical value, we reject the null hypothesis (series is not stationary). If the test statistic is less than the critical value, if fail to reject the null hypothesis (series is stationary). For the air passenger data, the value of the test statistic is greater than the critical value at all confidence intervals, and hence we can say that the series is not stationary.

I usually perform both the statistical tests before I prepare a model for my time series data. It once happened that both the tests showed contradictory results. One of the tests showed that the series is stationary while the other showed that the series is not! I got stuck at this part for hours, trying to figure out how is this possible. As it turns out, there are more than one type of stationarity.

So in summary, the ADF test has an alternate hypothesis of linear or difference stationary, while the KPSS test identifies trend-stationarity in a series.

3. Types of Stationarity

Let us understand the different types of stationarities and how to interpret the results of the above tests.

Strict Stationary: A strict stationary series satisfies the mathematical definition of a stationary process. For a strict stationary series, the mean, variance and covariance are not the function of time. The aim is to convert a non-stationary series into a strict stationary series for making predictions.

Trend Stationary: A series that has no unit root but exhibits a trend is referred to as a trend stationary series. Once the trend is removed, the resulting series will be strict stationary. The KPSS test classifies a series as stationary on the absence of unit root. This means that the series can be strict stationary or trend stationary.

Difference Stationary: A time series that can be made strict stationary by differencing falls under difference stationary. ADF test is also known as a difference stationarity test.

It’s always better to apply both the tests, so that we are sure that the series is truly stationary. Let us look at the possible outcomes of applying these stationary tests.

4. Making a Time Series Stationary

Now that we are familiar with the concept of stationarity and its different types, we can finally move on to actually making our series stationary. Always keep in mind that in order to use time series forecasting models, it is necessary to convert any non-stationary series to a stationary series first.


In this method, we compute the difference of consecutive terms in the series. Differencing is typically performed to get rid of the varying mean. Mathematically, differencing can be written as:

yt‘ = yt – y(t-1)

where yt is the value at a time t

Applying differencing on our series and plotting the results:

train['#Passengers_diff'] = train['#Passengers'] - train['#Passengers'].shift(1) train['#Passengers_diff'].dropna().plot()

Seasonal Differencing

In seasonal differencing, instead of calculating the difference between consecutive values, we calculate the difference between an observation and a previous observation from the same season. For example, an observation taken on a Monday will be subtracted from an observation taken on the previous Monday. Mathematically it can be written as:

yt‘ = yt – y(t-n)

n=7 train['#Passengers_diff'] = train['#Passengers'] - train['#Passengers'].shift(n) Transformation

Transformations are used to stabilize the non-constant variance of a series. Common transformation methods include power transform, square root, and log transform. Let’s do a quick log transform and differencing on our air passenger dataset:

train['#Passengers_log'] = np.log(train['#Passengers']) train['#Passengers_log_diff'] = train['#Passengers_log'] - train['#Passengers_log'].shift(1) train['#Passengers_log_diff'].dropna().plot()

End Notes

In this article we covered different methods that can be used to check the stationarity of a time series. But the buck doesn’t stop here. The next step is to apply a forecasting model on the series we obtained. You can refer to the following article to build such a model: Beginner’s Guide to Time Series Forecast.


How To Use Chromebook As A Second Monitor

One display screen is sufficient for most users. But a second monitor drastically improves productivity for gaming, simulations, video editing, or similar purposes. It also becomes much more convenient if you want to use multiple apps simultaneously.

Normally you need to invest in a monitor to extend your screen, but what if you have a spare laptop? If you have a Chromebook that you can transform into a secondary screen, using it is the most economical method.

Using a Chromebook as a second monitor is not as easy as using Windows or Mac laptops. So, we have created his article to help you out in this process.

A Chromebook does not have video input. So, you can only use some server clients that use remote desktop access to emulate Chromebook as a mirroring or extended screen.

The apps work through wired or wireless connections. Wireless interference can cause lags and delays for such connections. You can even experience delays in wired mode as the cables you use are not optimized for video transfer.

So, if you need a second monitor for tasks where delays are undesirable, such as gaming, it’s better to invest in a separate monitor. But for casual uses, if you already have a Chromebook, you can use it as a second monitor.

As mentioned earlier, since Chromebook lacks video input, you need to use an application to emulate the effect of having a second monitor. There are no such apps on your default Chromebook or other Operating Systems.

So you can only use third-party apps. Users consider Duet Display, a paid app, to have the best performance for this purpose. But many free programs are also available, such as Spacedesk and Deskreen.

Duet Display is the most popular software users use to set up Chromebook as a second monitor. They claim that it provides the least lag and many options for user convenience. So this application has many users despite it being a paid app.

Google used to provide a free Chromebook perk for Duet Display. While it has already stopped doing so, you can still check the Chromebook Perks website in case Google starts offering the perk again.

While users consider Duet Display as the best option to use Chromebook as a second monitor, it’s a paid app. And its free perk for Chromebook is also no longer available (as of this article’s published date). 

It does not mean that Google won’t provide such perks in the future. But you might want to use free alternatives. So, here are some open-source apps you can use for this purpose.


Spacedesk is a free multi-monitor application that supports both wired and wireless modes. While it is somewhat buggy and lags a bit compared to Duet Display, you can use it well on high-spec systems or for less resource-intensive tasks.

Here’s how you can use it to set up Chromebook as a second monitor:

Splashtop Wired XDisplay

Splashtop Wired XDisplay is another free alternative software. It only supports a wired connection, but you won’t supper form lags or other issues. Follow the steps below to use Chromebook as a second monitor using this application:


Duet Display, Spacedesk, and Wired XDisplay all only allow using Chromebook as a second monitor for Windows or Mac computers. So, if you want to extend your display for a Linux PC, a good option is the Deskreen. It also works with Windows and Mac but is somewhat laggy.

To use this program to set up Chromebook as a second monitor,

You can adjust the Chromebook’s display from your Multiple Monitor display settings. 

First, it’s better to arrange your logical screen positions similar to the physical setup for more convenience. Here’s how you can do so:

Then, change the settings for the Chromebook monitor from your display settings. The options you can modify are self-explanatory, such as resolution, orientation, and so on.

If you choose to extend your display or use Chromebook as a separate display, you need to drag a window to the edge of your primary display to transfer it to Chromebook.

If you encounter any issues while using Chromebook as a second monitor, you can apply the following troubleshooting methods:

Update or Reinstall Display drivers.

Update or Reinstall USB and Network drivers depending on the mode you use.

Update Operating System.

Uninstall the Multi-monitor server client and reinstall it.

Reset Chromebook OS.

How To Switch To Digital Marketing As A Career?

The trick to getting the best salary and staying relevant and valuable in the job market is to be on top of your career graph. If you are a fresher, you should be doing something that the industry demands. Currently, the world is moving toward digital infrastructure. where all four Ps of the marketing mix are being digitized. The market is now open globally, and the competitor for every business is not their neighborhood brand but all the prominent brands that have the courage to stand out on the internet. It is very critical for businesses now to do their marketing online, and hence there is a growing need for digital marketers.

In this article, we will be understanding how one can switch to a career in digital marketing, how one can start working in the field of digital marketing, and what will be the benefits of working in this particular domain.

Digital Marketing and Digital Marketers

Simply put, “digital marketing” refers to marketing a product or service via the internet, and digital marketers assist businesses in doing so. A good digital marketer will conduct activities that will increase the revenue of the company, increase brand recognition, and create a loyal customer base. In digital marketing, because the company will be hustling with billions of other brands offering similar or identical products, it is very crucial for them to stay at the top of their game throughout. One slip, and you can lose customers because today’s customers are spoiled with choices and overloaded with information.

Points to Consider While Choosing Digital Marketing as Your Career

We will be discussing certain key aspects of what is required when you are working as a digital marketer. Be it a career switch or a career start, a digital marketer is supposed to be passionate about these parameters.

Written communication skills − it is very crucial for digital marketers to have good communication skills. Written communication skills are a must because content that is rich in quality is only considered by Google for ranking purposes in Search Engine Results Pages (SERP), Gen Z users of social media platforms would never take someone seriously if they see grammatical and spelling errors in a post, and it is also essential for content creation for websites and articles. If you are unsure or under confident about this skill of yours, you can take courses in SEO writing and basic communication skills through applications like


Great Learning


Google and others

We are not seeking excellent communication skills, surrealistic vocabulary, or difficult sentences; simple, short, and crisp content is more appreciated by the users.

Business Acumen − A person should have an understanding of the marketer and its consumers. If you are only going to focus on your product, marketing techniques, or price, you will not be able to succeed. A digital marketer must be able to align its 4Ps of marketing with the needs of the consumer. It is learned through experience, but it is also innate. You can also take some courses available on the internet to gain a better understanding of the applications mentioned above.

Sub-careers in Digital Marketing

A digital marketer is supposed to be involved in all the marketing activities of the firm; however, one can also choose an area of expertise. Depending on the size of the organization and your capabilities, you can be any or all of the following points.

Content writer

Graphic designer

SEO Specialist

Instructional designer

social media manager

Affiliate marketers

Video editor and creator

Research interns and others

Switching a career in digital marketing or starting a new career in digital marketing is not difficult. All that it requires is the will to do the same. Digital marketing is a trend, seeing how the world is operating post-pandemic. E-commerce has boomed, as have other industries. Every small business today is trying to enter the online retail space to increase its sales at a nominal rate, and your knowledge of digital marketing can earn you bucks. You begin earning money by using your phone and social media handles.

How To Run A Linux Command As Another User

If you are an experienced Linux user, you know that sometimes you need to run a command as another user. This can be necessary for various reasons, such as running a command that requires root privileges or running a command that belongs to another user. In this article, we will explain how to run a Linux command as another user.

Understanding User Accounts in Linux

Before we dive into the specifics of running a command as another user, it is important to understand the concept of user accounts in Linux. Every user in Linux has a unique user ID (UID) and a corresponding username. Additionally, every user belongs to at least one group, which is identified by a group ID (GID) and a group name.

When you log in to a Linux system, you are assigned a user account that determines your permissions and access to system resources. By default, you have limited privileges and cannot perform certain actions that require elevated permissions. However, you can use the sudo command to temporarily elevate your privileges and perform administrative tasks.

Using the su Command to Run a Command as Another User

The su command in Linux allows you to switch to another user account and run commands as that user. To use the su command, you must have the password of the user account you want to switch to. Here is the syntax of the su command:

su [options] [username]

[options]: This parameter specifies any optional arguments that modify the behavior of the su command. For example, you can use the -c option to specify the command to run as the target user.

[username]: This parameter specifies the username of the target user account.

When you run the su command without any options, you are prompted to enter the password of the target user account. Once you enter the correct password, you are switched to that user’s account and can run commands as that user.

Here is an example of how to use the su command to run a command as another user:

su john -c "ls /home/john"

In this example, we are running the ls command as the user john. The -c option specifies the command to run, and the argument "ls /home/john" specifies the directory to list.

Using the sudo Command to Run a Command as Another User

Another way to run a command as another user is to use the sudo command. Unlike the su command, the sudo command does not require you to enter the password of the target user account. Instead, you must have the permission to run commands as that user.

To run a command as another user using the sudo command, you must specify the -u option followed by the username of the target user. Here is the syntax of the sudo command:

sudo -u [username] [command]

-u [username]: This option specifies the username of the target user account.

[command]: This parameter specifies the command to run as the target user.

Here is an example of how to use the sudo command to run a command as another user:

sudo -u john ls /home/john

In this example, we are running the ls command as the user john. The -u john option specifies the target user, and the argument /home/john specifies the directory to list.


Running a Linux command as another user can be useful in many situations, such as when you need to run a command that requires elevated privileges or when you need to access files or directories that belong to another user. In this article, we explained how to use the su and sudo commands to run a command as another user. By following these instructions, you can perform administrative tasks and access system resources with ease.

A Powerful Way To End The School Year

Year mapping allows students to see what they’ve learned in your class, and it’s a great resource for your incoming class.

One of our strategies that teachers enjoy using at the end of the school year is a practical, easy-to-use tool we call Celebrating Learning With Year Mapping. This activity gives your current students a chance to feel good about what they’ve learned and provides incoming students an opportunity to see real evidence that they can be successful learners in the coming school year. And it gives teachers a chance to enjoy seeing students share what they’ve learned and to internalize their successful teaching.

Several elements of this strategy make it a powerful way to end the school year with a positive experience, often much needed after testing is over and as a busy year comes to an end. With prompted recall, each student can remember learning events that mean the most to them. Year-end mapping utilizes the power of positive teacher-student relationships as well as personalized learning, summarizing, group learning, and organizing information graphically.

Creating the Presentations

The following steps can be used to create a visual representation of key content studied over the year, which can become a catalyst for celebrating learning successes:

As the end of the year approaches, tell your students they’ll be using graphic organizers to create a large map of what they’ve learned about the content you’ve taught. (You can find templates for graphic organizers by doing a Google image search for “end of school year graphic organizer.”)

Guide students in groups of four to work on specific parts of the curriculum, using prompted recall to help them remember important learning events, knowledge, and skills learned during the year.

Assemble a giant map—a collection of the group maps—ideally on a large wall space of the classroom.

Ask each student in turn to present one part of the map. These short presentations could include materials that helped them learn, such as books, drawings, pictures, notes, articles, or other meaningful artifacts.

Rehearse the presentations.

Invite groups of students who may be in your class next year to come to the presentations and be taught by your current class.

What Students—and Teachers—Get From Year Maps

The year mapping strategy gives teachers a way to relate to individual students around their successful learning—and to revisit some of their own favorite lessons and interactions with students over the past year. It gives students a chance to create fascinating graphic organizers that help them arrange evidence of their year of learning with visual appeal. 

The strategy also provides motivation for students to summarize key experiences and knowledge learned throughout the year. Summarizing is a useful learning strategy, as it requires more than retelling. When students summarize, they analyze knowledge, determine key elements, and translate a lot of information into a brief and coherent presentation.

Working in groups often provides an emotional hook that makes the learning experience more meaningful and memorable for students. Group work makes it possible for all students to play a part in making the year-end maps, as individual memories may vary dramatically from student to student. Working together, students can rely on others to remember aspects of the year that may not have been so memorable for them. They in turn remember elements of the curriculum that others may have forgotten.

Questions you can ask your students who are moving up to the next grade include: What were some key things you learned this year? How does this activity make you feel as we pull our giant map together and reflect on what we’ve learned?

At the end of the presentation to students who will be moving up to the grade level you teach, a good question might be: What are you most excited about learning next year?

This strategy can be adapted in a variety of ways. For example, it can be extended to more classrooms across participating grade levels. Some schools have used this strategy to celebrate learning school-wide. Year-end mapping can be successfully paired with other tools we have developed, such as success files—repeatedly updated collections that provide evidence to help students internalize and remember their learning successes.

The power of year mapping is in how it helps both students and teachers internalize learning and celebrate it. Feel free to change the way you use our approach so that it works best for you and your students at this special time of the year.

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