6 tips for teaching business English to low level learners

Margaret O'Keeffe
A business woman in a suit sat at a laptop

The CEFR describes A1 and A2 learners as ‘basic users’ of a language. So how can we help these students to develop their English for the workplace?

Here are our six top tips:

1. Focus on high-frequency vocabulary for work

Learning English vocabulary for work context is the top priority for many low-level learners in business English classes. It helps them to communicate their message in a simple, effective way. This makes it important to teach common words and set expressions for everyday work situations.

These include:

  • lexical sets (words related to the same topic or situation) – for example, days, months, numbers, verbs to describe work routines, verbs in the past.
  • common collocations with verbs and nouns (for example, manage a team, have meetings, place an order, solve a problem).
  • functional language and fixed phrases – greetings (How are you? Nice to meet you.) and offers (How can I help you? Would you like…?).

2. Help students with vocabulary learning

Teach vocabulary items in realistic contexts. For example, phone calls, to-do lists, short emails, text messages etc.

While it might be tempting to give students lots of vocabulary to memorize, this can cause overload, be frustrating and ultimately demotivating for learners. Instead, you should aim to present eight to ten new words in a lesson as a general rule. This is an achievable number for working memory and helps to build learners’ confidence. The number of words can be a little higher if items are easy to show in images or there is repetition; for instance, the numbers 20 to 100.

Have students make simple decisions about new words, as this helps with recall later. Start with simple tasks, such as matching words and pictures or verb and noun collocations they’ve seen in a short text (for example, managing a team, call customers, writing emails, etc.). Next, ask students to complete sentences using the target words and write their own sentences using these words.

Getting students to personalize new vocabulary makes it more memorable, for instance writing sentences describing their work routines. Repetition also aids long-term memory, so make sure vocabulary is recycled in the materials in later lessons.

Finally, make a list of vocabulary games to use for revision exercises, warmers and to finish classes.

3. Maximize student speaking time

Learners need to develop their English-speaking skills for work. The classroom is a safe, low-stakes environment for them to gain fluency and confidence.

Use the audio and video scripts of short dialogues or an extract from a longer script. Students read the dialogue aloud in pairs or groups. Give feedback by drilling the stress and rhythm of any words or phrases which were difficult with the whole class. Back-chaining phrases – starting with the last sound and building up going backwards – is an excellent way to drill. Get students to swap roles and repeat the task.

You can also use another technique called disappearing dialogue. Put a short dialogue on the board for students to practice in pairs. Then delete parts of the dialogue and ask them to repeat the task, swapping roles each time. Gradually delete more parts to increase the challenge. Students can reconstruct the dialogue as a final task.

Moreover, surveys, questionnaires, true/false games, and information-gap exercises are ways to practice speaking in English, target structures, and vocabulary.

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4. Provide support for speaking tasks

Use a model dialogue from the coursebook or one you wrote yourself. Ask students to build their own short dialogues by changing some details (such as names, dates, prices, and quantities). Or use one half of the dialogue and ask students to write the other part.

Then, have them perform their dialogues together with their script. Then, ask them to try to memorize it without the script. Finally, they should perform the dialogue for another pair or even for the whole class.

Give students a reason to listen to their partners when they are speaking. For example, a speaking task like placing an order on the phone, gives them a reason. The listening student can note the essential information and check their answers afterwards.

Repeating tasks with slight variations increases the challenge, improves fluency, helps students remember useful phrases, and builds self-confidence.

5. Practice work skills your students need

Students are much more engaged and motivated when the class content is relevant to their everyday situations. They will want to learn English for work and skills they need to practice include telephoning, socializing and giving presentations.

Writing skills are also important. This includes formal and informal text messages, simple forms, less formal emails to colleagues (e.g. to update on work) and more formal emails to customers (e.g. replying to a simple inquiry).

At the start of the class, make it clear what students will be doing in the lesson. You can refer to the lesson outcome on the coursebook page or write the lesson outcome in your own words on the whiteboard. For instance, “Today you will learn to place a simple order on the phone”.

At the end of the class, ask students to respond to the self-assessment statement: “I can place a simple order on the phone.”

This is a reminder of the purpose of the lesson. It also helps the students and teachers to reflect on the progress they are making.

The grammar syllabus should also relate to English learners' communicative needs (for example, describing your company, instructions, and talking about arrangements).

6. Teach functional language phrases

Draw students’ attention to useful phrases and functional language in speaking and writing. For instance, when greeting visitors (“Nice to meet you.” “See you later.”). They can memorize these utterances and put them to immediate use outside the classroom.

Use role plays to practice work skills and functional language skills. Give learners ample time to prepare and write down what they want to say. In a phone call role play, put students back to back to increase the challenge and add an element of authenticity; even better if they can call each other on their mobile phones from separate rooms.

Similarly, with presentations (for example, introducing yourself and your company), give students time to prepare and rehearse. They can ask colleagues to video them on their mobile phones for later correction work and feedback. Or they could rehearse and film themselves at home and show the final video in the next class.

These are just a few tips and techniques for teaching corporate English to low-level learners. It’s especially important for these students to start simple, recycle language often and build their confidence in their workplace English.

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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. 

    app English International Certificate (PEIC)

    app English International Certificate (PEIC) also uses automated assessment technology. With a two-hour test available on-demand to take at home or at school (or at a secure test center). Using a combination of advanced speech recognition and exam grading technology and the expertise of professional ELT exam markers worldwide, our patented software can measure English language ability.