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All languages change over time, and there can be many different reasons for this. The English language is no different – but why has it changed over the decades?

Some of the main influences on the evolution of languages include:

• The movement of people across countries and continents, for example, migration and, in previous centuries, colonization. For example, English speakers today would probably be comfortable using the Spanish word 'loco' to describe someone who is 'crazy'.
• Speakers of one language coming into contact with those who speak a different one. No two individuals speak identically: people from different geographical places clearly speak differently and even within the same community there are variations according to a speaker’s age, gender, ethnicity and social and educational background. For example, the word 'courting' has become 'dating'.
• New vocabulary required for inventions such as transport, domestic appliances and industrial equipment, or for sporting, entertainment, cultural and leisure reasons. For example, the original late 19th century term 'wireless' has become today’s 'radio'.

Due to these influences, a language always embraces new words, expressions and pronunciations as people come across new words and phrases in their day-to-day lives and integrate them into their own speech.

What changes has the English language seen?

As the English language has changed, it’s been easy to pick out words that pass into common usage. Here at ����app English, we have explored some of these recent changes to the English language. The rise in popularity of internet slang has seen phrases such as 'LOL' (Laugh Out Loud), 'FOMO' (Fear Of Missing Out) and 'fam' (an abbreviated form of family) become firmly embedded in the English language over the past ten years.

Every decade sees new slang terms like these appearing in the English language. And while some words or abbreviations do come from internet or text conversations, others may appear as entirely new words, a new meaning for an existing word, or a word that becomes more generalized than its former meaning, brought about by any one of the reasons above. Decades ago, 'blimey' was a new expression of surprise, but more recently 'woah' is the word in everyday usage.

Sentence structure is of course, another change to the English language. Decades ago, it would have been normal to ask 'Have you a moment?' Now, you might say 'D’you have a sec?' Similarly, 'How do you do?' has become 'How’s it going?' Not only have the sentences been abbreviated, but new words have been introduced to everyday questions.

Connected to this is the replacement of certain words with other, more modern versions. It’s pretty noticeable that words like 'shall' and 'ought' are on the way out, but 'will', 'should' and 'can' are doing just fine.

Other changes can be more subtle. Many verbs can take a compliment with another verb in either the '-ing' form or the 'to' form, for example, 'they liked painting/to paint', 'we tried leaving/to leave', or 'he didn’t bother calling/to call'. Both of these constructions are still used and have been for a long time, but there has been a steady shift over time from the 'to' to the '-ing' compliment.

What do the changes mean?

There are many other changes to the English language – what have you noticed? Have these changes affected your teaching or learning methods?��

Most contemporary linguistic commentators accept that change in language, like change in society, is inevitable. Some think that is regrettable, but others recognize it as a reinvigoration of a language, bringing alternatives that allow subtle differences of expression.

Linguist, writer and lecturer David Crystal considers whether 'text speak' is undermining the English language. His response to the naysayers who claim it is damaging the English language is to point out that abbreviations have been around for a long time. While some, such as the ones we discussed above, are new, others, such as the use of 'u' for 'you' and the number 8 as a syllable in 'later', have been around for a century or more. Further to this, research shows that there is a correlation between the ability to use abbreviations and the ability to spell. After all, in order to abbreviate, you have to know which letters to abbreviate.

As with everything, change isn’t necessarily a bad thing and, as the needs of English language users continue to change, so will the language.

Fancy learning more about English? Check out our post 'How do English phrases travel across countries?'.

Research has shown that automated scoring can give more reliable and objective results than human examiners when evaluating a person’s mastery of English. This is because an automated scoring system is impartial, unlike humans, who can be influenced by irrelevant factors such as a test taker’s appearance or body language. Additionally, automated scoring treats regional accents equally, unlike human examiners who may favor accents they are more familiar with. Automated scoring also allows individual features of a spoken or written test question response to be analyzed independent of one another, so that a weakness in one area of language does not affect the scoring of other areas.

was created in response to the demand for a more accurate, objective, secure and relevant test of English. Our automated scoring system is a central feature of the test, and vital to ensuring the delivery of accurate, objective and relevant results – no matter who the test-taker is or where the test is taken.

Development and validation of the scoring system to ensure accuracy

PTE Academic’s automated scoring system was developed after extensive research and field testing. A prototype test was developed and administered to a sample of more than 10,000 test takers from 158 different countries, speaking 126 different native languages. This data was collected and used to train the automated scoring engines for both the written and spoken PTE Academic items.

To do this, multiple trained human markers assess each answer. Those results are used as the training material for machine learning algorithms, similar to those used by systems like Google Search or Apple’s Siri. The model makes initial guesses as to the scores each response should get, then consults the actual scores to see well how it did, adjusts itself in a few directions, then goes through the training set over and over again, adjusting and improving until it arrives at a maximally correct solution – a solution that ideally gets very close to predicting the set of human ratings.

Once trained up and performing at a high level, this model is used as a marking algorithm, able to score new responses just like human markers would. Correlations between scores given by this system and trained human markers are quite high. The standard error of measurement between ����app’s system and a human rater is less than that between one human rater and another – in other words, the machine scores are more accurate than those given by a pair of human raters, because much of the bias and unreliability has been squeezed out of them. In general, you can think of a machine scoring system as one that takes the best stuff out of human ratings, then acts like an idealized human marker.

����app conducts scoring validation studies to ensure that the machine scores are consistently comparable to ratings given by skilled human raters. Here, a new set of test-taker responses (never seen by the machine) are scored by both human raters and by the automated scoring system. Research has demonstrated that the automated scoring technology underlying PTE Academic produces scores comparable to those obtained from careful human experts. This means that the automated system “acts” like a human rater when assessing test takers’ language skills, but does so with a machine's precision, consistency and objectivity.

Scoring speaking responses with ����app’s Ordinate technology

The spoken portion of PTE Academic is automatically scored using ����app’s Ordinate technology. Ordinate technology results from years of research in speech recognition, statistical modeling, linguistics and testing theory. The technology uses a proprietary speech processing system that is specifically designed to analyze and automatically score speech from fluent and second-language English speakers. The Ordinate scoring system collects hundreds of pieces of information from the test takers’ spoken responses in addition to just the words, such as pace, timing and rhythm, as well as the power of their voice, emphasis, intonation and accuracy of pronunciation. It is trained to recognize even somewhat mispronounced words, and quickly evaluates the content, relevance and coherence of the response. In particular, the meaning of the spoken response is evaluated, making it possible for these models to assess whether or not what was said deserves a high score.

Scoring writing responses with Intelligent Essay Assessor™ (IEA)

The written portion of PTE Academic is scored using the Intelligent Essay Assessor™ (IEA), an automated scoring tool powered by ����app’s state-of-the-art Knowledge Analysis Technologies™ (KAT) engine. Based on more than 20 years of research and development, the KAT engine automatically evaluates the meaning of text, such as an essay written by a student in response to a particular prompt. The KAT engine evaluates writing as accurately as skilled human raters using a proprietary application of the mathematical approach known as Latent Semantic Analysis (LSA). LSA evaluates the meaning of language by analyzing large bodies of relevant text and their meanings. Therefore, using LSA, the KAT engine can understand the meaning of text much like a human.

What aspects of English does PTE Academic assess?

What do we mean by English fluency, and how can understanding competencies across the four skills provide a more realistic picture of communicative English ability?

What is fluency?

As someone who worked in dictionaries, the meaning of words has always interested me – and fluency is a particular case in point. Language learners often set themselves the goal of becoming fluent in a language. Job adverts often specify “fluent in English or Spanish” as a requirement. But what does being 'fluent' in a language actually mean? If we look in the Longman Dictionary of Contemporary English, we see that fluent means “able to speak a language very well”. Fluent speech or writing is described as “smooth and confident, with no mistakes”. In general, fluency is most often associated with spoken language – but is that the goal of all language learners? And what does being able to speak fluently show about the other language skills?

Describing English proficiency

Before entering the world of dictionaries, I taught English as a foreign language in France. At that time, the Common European Framework of Reference for Languages (CEFR) had not yet been published and learners were described in very general terms – beginner, intermediate, advanced – with no agreed standards on what learners at each level were expected to know. As well as establishing standards, the CEFR also shifted the focus of language assessment from knowledge of grammar and vocabulary to functional competence, i.e. what can a student actually do with the language they’re learning across the four skills:

• listening
• reading��
• speaking
• writing

Interestingly, while calling out specific objectives for each skill, almost two-thirds of the information in the CEFR describes spoken language. This seems to imply that spoken fluency is indeed the most important goal for all language learners.

Mapping out a personalized path to proficiency

As a global publisher, ����app English recognizes that all learners are different – in their backgrounds, learning environments and learning goals. This is why we have undertaken new research to extend the set of learning objectives contained in the CEFR to account for learners who need detailed information about their level in all four skills, not just in one (typically, that of speaking).

No learner will be equally proficient in all four language skills – in the same way that no native speaker is equally proficient in all skills in their first language. Some of us are better at writing than speaking, and many are illiterate in their first language. A true measure of language proficiency needs to take into account all of the skills. Equally, not every learner of English will need to be 'fluent' in spoken communication.

Many researchers need to read papers in English and attend conferences in English – but will only ever present and write in their first language. Is 'fluency' a good way to describe their goal? And if it isn’t, does that somehow diminish their language achievements? By acknowledging proficiency in individual skills – rather than catch-all terms such as 'fluent' – we gain a clearer understanding of goals and outcomes, and with this knowledge, we are in a better position to tailor learning to the individual.

Interested in learning more about the English language? Check out our post��How using jargon, idioms and colloquialism confuses English learners and our post on strange English phrases.

If you're looking to improve your own fluency (in any language) make sure to check out our language learning app Mondy.��