How using jargon, idioms and colloquialism confuses English learners

A man sat a laptop, with his hands to his face looking comtemplative

“How do I learn thee? Let me count the ways”

Did you get it?

To ‘get’ the title of this post, you must first recognize that it is based on the famous opening line from Elizabeth Barrett Browning’s Sonnet 43, published in 1850. Then you need to understand that “thee” is an old form of the word “you”. Next, you need to appreciate the pun on the word “love”, which has been changed to “learn”. Lastly, you need to figure out the full meaning of the phrase, which likens the idea of learning English to the idea of love, or a labor of love (also an idiom), and the many different ways you can do it.

That’s a lot of steps, but a fluent English speaker would likely pick it up. That’s because they've learned the language from childhood in an English-speaking country, probably studied some poetry at school and have absorbed this quote through news media, popular culture or at a wedding.

Understanding jargon, idioms and colloquialism is one of the hardest parts of learning any new language. It’s only achieved by repeated exposure to – and immersion in – speech. In the Global Scale of English Learning Objectives for Adult Learners, listening to, and recognizing a wide range of idioms and colloquialisms doesn’t appear until 83, at the very upper edge of C1. For speaking, joining a conversation in progress with fluent speakers on complex topics comes in at 81. Reading idiomatic or non-standard language appears at 76, again within C1. It all adds up to a very sophisticated level of understanding.

Yet jargon and idioms are huge parts of English. They are also constantly changing, and jargon morphs with new innovations, professional disciplines, and generations.

When an idiom is over-used, it becomes a cliché. Sometimes idioms stick out like a sore thumb because they’re unrelated to context – but not always.

Even fluent English speakers don’t necessarily realize an idiom is an idiom. Take the phrase “I’ll call you tomorrow”.Most fluent English speakers would see that as a simple declarative sentence. The expression comes from the idea of “calling on” someone in person, or calling their name to get their attention, but asecond-language English speaker may not immediately grasp the fact that it now involves a phone, and can be achieved over long distances.

English is both complex and rich in figurative language; we know this. That’s one of its beauties and also a challenge of learning it. But at what point do these kinds of figurative language become incorrect?

As ,second-language English speakers now outnumber fluent English speakers globally, which means the balance is tipping. Fluent English speakers are doing business with, learning from and interacting with second-language English speakers more than ever. Billions of pounds in trade and countries’ fates can hinge on those written and spoken conversations; the stakes are high.

Second-language English speakers find idioms and jargon difficult and therefore see far less need for them. Although sayings can be lovely, charming and fun, these linguistic devices mask meaning by their very nature. This makes language less efficient when not every participant in a conversation can decode them. The proportion of people who can’t is growing, which might affect what is considered to be “correct” in the coming decades and have implications for what is taught.

Want to learn some idioms? Check out - Eerie English idioms and phrases

More blogs from app

  • Hands typing at a laptop with symbols

    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.