How can we motivate adult language learners?

Ken Beatty
Two Adult students studying in a library, one with a laptop, the other writing

The problem of motivation

Have you ever had a problem like Jaime?

One of my TESOL graduate students in Colombia does. She has a dream job, teaching English at a country club near Bogota. The club attracts international visitors who come to stay, swim, play tennis and golf. Her job is to improve the English skills of everyone from the club’s hotel staff to the tennis coaches, lifeguards, and golf caddies. Most are highly motivated.

Except for Jaime.

Jaime, a golf caddy, would slump into class each day, throw down his bag, sit at the back, and automatically take out his phone.

“Jaime, could you take out your book, please?”

“Forgot it, teacher.”

“Jaime, could you answer the question?”

“No idea, teacher.”

She tried everything to motivate him, but nothing seemed to work. So, imagine her surprise one day when she walked into class to find him sitting at the front, book open, sharpened pencils ready, and no phone in sight. Although she found it surprising, she didn’t want to embarrass him and instead taught the lesson as normal. Jaime’s hand shot up constantly, either asking her to repeat a point while he took careful notes, or attempting to answer each of her questions.

What changed? By the end of the week, she had to find out and asked him to stay behind.

“Because I only speak Spanish,” he sighed, “I only work for local golfers. But I just found out that the caddies who speak English and carry clubs for the international golfers get tipped ten times as much.”

Jaime had discovered one of two major reasons for adults to learn a second language: career progression. The other, if you can’t guess, is love...removing language as a barrier to intimacy.

Transferring motivation

Everyone is motivated about something and that motivation can be translated to the classroom. For example, without mentioning language learning, ask your students to list a few things they have been motivated about in the past, and identify the principles involved.

Let’s say a student is motivated to improve her soccer skills. Beyond “It’s fun!”, dig deeper and you’ll uncover things such as understanding personal potential, doing something social, and not letting down teammates. If you substitute classmates for teammates, you can see that these are all motivations for learning a language. There are countless more, but focus on what is personal for each student.

Enemies of motivation

Beyond a lack of awareness about the advantages of learning English, here are three enemies of motivation.

Enemy 1: I’m shy.

It’s a simple truth that more outgoing people have an easier time learning a language; they’re more willing to make an effort and continue trying until they succeed.

Naturally shy people will still learn – sometimes focusing more on reading and writing – but there are ways to help them be more outgoing. For example, language games can help shy students by increasing a sense of competitiveness and lowering their affective filters, the emotions that interfere with language acquisition such as anxiety and a lack of self-confidence (Krashen, 1992). The connection is confidence; build up students’ confidence and they will be more motivated.

Enemy 2: If I don’t speak, I won’t make mistakes.

This issue is often a byproduct of constant teacher interruptions and comments on students’ language use. Be patient, give students time to think before they answer, and don’t always take the answer from the first person to raise a hand. Consider asking everyone to raise their hands before asking one student.

When you give feedback, focus on errors and ignore mistakes. We all make mistakes in our first and second languages when we speak and generally know we are making them. It’s just that we may be temporarily distracted. Errors, on the other hand, are mistakes that are repeated and the speaker is unaware of them. This leads to fossilization and challenges remediating them. Focus on systematic errors instead of mistakes.

Also, as a general rule, when students are speaking, observe the same politeness you would with anyone else. Avoid interrupting, and offer corrections later, perhaps privately or in a note.

Enemy 3: I don’t have time.

Many adult language learners are heavily committed to other activities; families and work obligations leave them little time to study. However, others may assume they are busy. As a language task, ask students to write a schedule of their typical day and explain what they do hour by hour.

While studying cello in my 30s, I was fortunate to come across a book by educator John Holt (1991) who also learned cello as an adult. In the book, he said that if you do anything new in a major way, make room in your life by giving up something else. For students, that something else may be part of their social life.

But even the busiest students have a few minutes between tasks… time waiting in lines, or on a bus. Using a phone app can provide practice.

Motivation over time

Students tend to be motivated to different degrees as they progress through a language. At first, students will make rapid progress and have high expectations about learning English. However, as they advance, their progress becomes less noticeable. This can be a difficult time for students.

Try using 'imagining sessions' with students, where they roleplay themselves 20 years from now as fluent English speakers. What will they be doing? Maybe they’ll be English teachers themselves! Share your journey to becoming someone who now teaches English. Perhaps you were once a golf caddy named Jaime.

Explore our educator section to see how we can help improve your teaching

References

  • Holt, J. (1991). Never too late. Perseus: Cambridge, MA.
  • Krashen, S. (1992). .

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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.