Four ways to keep kindergarten ESL students focused all day

Heath Pulliam
A teacher sat in a classroom with a child, sharing crayons with eachother and smiling
Reading time: 5 minutes

Heath Pulliam is an independent education writer with a focus on the language learning space. He’s taught English in South Korea and various subjects in the United States to a variety of ages. He’s also a language learning enthusiast and studies Spanish in his free time.

Those who have taught children anywhere between the ages of 4 and 8 know that one of the biggest challenges of getting through to them is keeping your presentation style interesting. As someone who taught ESL in South Korea to kindergarteners, there are a few factors that make keeping students engaged a challenge. In countries where students learn English, students often have a heavy courseload and high expectations. As a first-year teacher, I learned a lot about what worked and what didn’t through trial and error. These are four methods that I consistently used to keep my students interested and engaged all day.

Students are quick to lose focus at such a young age. You’re not speaking their mother tongue and some parts of an ESL curriculum are less than exciting. With young students, you can’t lecture your way through the material all day. Kindergarteners have a small window of focus and it must be capitalized on. The following methods are ones that worked for me and can be modified to cover any topic you’ll run into in an ESL curriculum.

Activities to engage pre-primary ESL students all day
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1.The wheel of names

Don't let the simplicity of this tool fool you. The Wheel of Names, which is exactly what it sounds like, is a very simple tool that you can put the names of your students on to randomly (or not) select a student. The Wheel of Names crushes hand-raising and it’s a constant struggle to get everyone raising their hands. (We can’t let the quiet ones just slip through the cracks!)

The beauty of the Wheel of Names is the guaranteed excitement on every spin. They’re kindergarteners. The nature of randomizing who’s going to answer the question, come up to the board, or go first in a game is so exciting to them. Use it however you want. The suspense has kids excited to see who will be chosen and has them cheering on whichever classmate gets chosen.

I used this tool mostly during review sessions to choose students to answer questions. It can also be used for creating small groups, making sure everyone gets a chance to speak during activities and seeing who’s going to get to do the next fun thing in the classroom. You’re teaching kindy, have fun with it.

2.Team-based review game

We never came up with a good name for this game, but we used it a lot. Every Friday, during our chapter review, this was undoubtedly the best compromise between fun and effectiveness for reviewing material. It also fosters speaking, one of the most important parts of learning a language.

Here’s how it works. The class is separated into two teams. (For a little extra fun, use the wheel of names or a cup of popsicle sticks with students’ names on them to select the teams.) The teacher has a set of review questions or vocabulary words. On each turn, one person from each team has a chance to answer the question. You can do hand-raising or a randomization method to pick who answers.

If the student representative from the team gets it right, they earn a point. If they get it wrong, the opposing team gets a chance to answer for a point. Alternating between each team, the first to a certain number of points wins. A simple game, but at this age many students are just getting used to team-based activities.

This game is so effective because it makes use of children’s natural competitive spirit. When one student is up, the rest of their team cheers them on. Learning material is important, but a big part of teaching young students is teaching them social skills, too. This game gets everyone excited and gets the material to stick. It also teaches them the importance of teamwork and how to win and lose gracefully. (Not everyone can win every time).

3.Creating super sentences

At the end of the year, the students in my class ended up being the best writers in their respective age groups. I think this activity, making ‘Super Sentences” a few times a week was a big contributor to that. Inspired by a textbook we had, here’s how this activity works.

We’d create sentences with this formula:

Who or what – The owl

Did what – hunted for mice

Where – in the forest

When – at night

The owl hunted for mice in the forest at night.

Before students got to work on their own, We’d fill out a little table on the board as a class that looked something like this – but changed every time.

Who or what?

(noun)

Did what? (verb)

Where?

(prepositional phrase)

When?

(prepositional phrase)

The dog

The raccoon

Joey

Mr. Heath

Chloe

The thief

Ran

Slept

Attacked _______

Found _________

Learned to ________

sneaked

On top of __________

Under __________

Through the ________

Next to the ________

Inside of the _______

Behind the ________

At sunset

At 4:00 PM

In the middle of the day

In March

In the evening

After this, students can get to work making their own sentences and drawing what is happening below. After a few times, many students began to make up their own nouns, verbs and prepositions that were not listed on the board. And when everyone gets comfortable writing sentences, you can add a ‘Why?’ section at the end.

This activity is one of my favorites because it helps bring out children’s natural creativity. In an ESL curriculum, there’s a lot of material that’s less than exciting, so any way to give the kids a little freedom can be a game-changer. It’s also great for vocabulary, grammar and writing practice.

To finish off the activity, have each student read their sentence and have the class vote on their favorite for a prize!

4. Vocabulary bingo

Something to know about teaching ESL in non-English-speaking countries is that often, no time is allowed to be spent not learning. This is definitely the case in South Korea. Even after lunch and towards the end of the day, students are rarely allowed to do anything if it isn’t enriching. Because of this, playing games that are fun and learning-effective is a must.

Now, I’m certainly not the first one to play Bingo. This version however, is slightly modified to be as enriching as possible. We played this version often at the end of the day, because focus runs low around that time.

Essentially, this is just vocabulary bingo with a little bit of charades mixed in. For this activity, make a basic bingo board with some of your current vocabulary words or target language. There are plenty of sites that make this easy.

What makes this version different is that on each turn, rather than telling everyone the word, describe it to the class and try to get them to guess what the word is. So, if the word was ‘teamwork’, you might say something like “This is what it’s called when everyone works together to help their team win.” You can even have a student helper that helps facilitate the game by giving the descriptions.

After the word is guessed, reiterate it to the class and proceed with Bingo as usual.

Conclusion

Part of being an effective teacher, especially with the younger ages, is harnessing kids’ natural excitement, energy and innate curiosity to their own benefit. Instead of suppressing it, it’s essential to adapt your teaching style to work for your students.

Teaching ESL to young children has its own set of challenges. They can hardly sit still for an hour of lecture, let alone a whole day. Make use of, or draw inspiration from these methods and see how they can work for you.

Teaching young learners? Read our posts '8 first lesson problems for young learners' and 'Effective classroom management routines for very young learners'.

Explore our pre-primary and primary courseware

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    Can computers really mark exams? Benefits of ELT automated assessments

    By app Languages

    Automated assessment, including the use of Artificial Intelligence (AI), is one of the latest education tech solutions. It speeds up exam marking times, removes human biases, and is as accurate and at least as reliable as human examiners. As innovations go, this one is a real game-changer for teachers and students. 

    However, it has understandably been met with many questions and sometimes skepticism in the ELT community – can computers really mark speaking and writing exams accurately? 

    The answer is a resounding yes. Students from all parts of the world already take AI-graded tests.  aԻ Versanttests – for example – provide unbiased, fair and fast automated scoring for speaking and writing exams – irrespective of where the test takers live, or what their accent or gender is. 

    This article will explain the main processes involved in AI automated scoring and make the point that AI technologies are built on the foundations of consistent expert human judgments. So, let’s clear up the confusion around automated scoring and AI and look into how it can help teachers and students alike. 

    AI versus traditional automated scoring

    First of all, let’s distinguish between traditional automated scoring and AI. When we talk about automated scoring, generally, we mean scoring items that are either multiple-choice or cloze items. You may have to reorder sentences, choose from a drop-down list, insert a missing word- that sort of thing. These question types are designed to test particular skills and automated scoring ensures that they can be marked quickly and accurately every time.

    While automatically scored items like these can be used to assess receptive skills such as listening and reading comprehension, they cannot mark the productive skills of writing and speaking. Every student's response in writing and speaking items will be different, so how can computers mark them?

    This is where AI comes in. 

    We hear a lot about how AI is increasingly being used in areas where there is a need to deal with large amounts of unstructured data, effectively and 100% accurately – like in medical diagnostics, for example. In language testing, AI uses specialized computer software to grade written and oral tests. 

    How AI is used to score speaking exams

    The first step is to build an acoustic model for each language that can recognize speech and convert it into waveforms and text. While this technology used to be very unusual, most of our smartphones can do this now. 

    These acoustic models are then trained to score every single prompt or item on a test. We do this by using human expert raters to score the items first, using double marking. They score hundreds of oral responses for each item, and these ‘Standards’ are then used to train the engine. 

    Next, we validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. If this doesn’t happen for any item, we remove it, as it must match the standard set by human markers. We expect a correlation of between .95-.99. That means that tests will be marked between 95-99% exactly the same as human-marked samples. 

    This is incredibly high compared to the reliability of human-marked speaking tests. In essence, we use a group of highly expert human raters to train the AI engine, and then their standard is replicated time after time.  

    How AI is used to score writing exams

    Our AI writing scoring uses a technology called . LSA is a natural language processing technique that can analyze and score writing, based on the meaning behind words – and not just their superficial characteristics. 

    Similarly to our speech recognition acoustic models, we first establish a language-specific text recognition model. We feed a large amount of text into the system, and LSA uses artificial intelligence to learn the patterns of how words relate to each other and are used in, for example, the English language. 

    Once the language model has been established, we train the engine to score every written item on a test. As in speaking items, we do this by using human expert raters to score the items first, using double marking. They score many hundreds of written responses for each item, and these ‘Standards’ are then used to train the engine. We then validate the trained engine by feeding in many more human-marked items, and check that the machine scores are very highly correlated to the human scores. 

    The benchmark is always the expert human scores. If our AI system doesn’t closely match the scores given by human markers, we remove the item, as it is essential to match the standard set by human markers.

    AI’s ability to mark multiple traits 

    One of the challenges human markers face in scoring speaking and written items is assessing many traits on a single item. For example, when assessing and scoring speaking, they may need to give separate scores for content, fluency and pronunciation. 

    In written responses, markers may need to score a piece of writing for vocabulary, style and grammar. Effectively, they may need to mark every single item at least three times, maybe more. However, once we have trained the AI systems on every trait score in speaking and writing, they can then mark items on any number of traits instantaneously – and without error. 

    AI’s lack of bias

    A fundamental premise for any test is that no advantage or disadvantage should be given to any candidate. In other words, there should be no positive or negative bias. This can be very difficult to achieve in human-marked speaking and written assessments. In fact, candidates often feel they may have received a different score if someone else had heard them or read their work.

    Our AI systems eradicate the issue of bias. This is done by ensuring our speaking and writing AI systems are trained on an extensive range of human accents and writing types. 

    We don’t want perfect native-speaking accents or writing styles to train our engines. We use representative non-native samples from across the world. When we initially set up our AI systems for speaking and writing scoring, we trialed our items and trained our engines using millions of student responses. We continue to do this now as new items are developed.

    The benefits of AI automated assessment

    There is nothing wrong with hand-marking homework tests and exams. In fact, it is essential for teachers to get to know their students and provide personal feedback and advice. However, manually correcting hundreds of tests, daily or weekly, can be repetitive, time-consuming, not always reliable and takes time away from working alongside students in the classroom. The use of AI in formative and summative assessments can increase assessed practice time for students and reduce the marking load for teachers.

    Language learning takes time, lots of time to progress to high levels of proficiency. The blended use of AI can:

    • address the increasing importance of formative assessmentto drive personalized learning and diagnostic assessment feedback 

    • allow students to practice and get instant feedback inside and outside of allocated teaching time

    • address the issue of teacher workload

    • create a virtuous combination between humans and machines, taking advantage of what humans do best and what machines do best. 

    • provide fair, fast and unbiased summative assessment scores in high-stakes testing.

    We hope this article has answered a few burning questions about how AI is used to assess speaking and writing in our language tests. An interesting quote from Fei-Fei Li, Chief scientist at Google and Stanford Professor describes AI like this:

    “I often tell my students not to be misled by the name ‘artificial intelligence’ — there is nothing artificial about it; A.I. is made by humans, intended to behave [like] humans and, ultimately, to impact human lives and human society.”

    AI in formative and summative assessments will never replace the role of teachers. AI will support teachers, provide endless opportunities for students to improve, and provide a solution to slow, unreliable and often unfair high-stakes assessments.

    Examples of AI assessments in ELT

    At app, we have developed a range of assessments using AI technology.

    Versant

    The Versant tests are a great tool to help establish language proficiency benchmarks in any school, organization or business. They are specifically designed for placement tests to determine the appropriate level for the learner.

    PTE Academic

    The  is aimed at those who need to prove their level of English for a university place, a job or a visa. It uses AI to score tests and results are available within five days.