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.
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.
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.
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.
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It's incredible to know that there are thousands of languages spoken across the world, each with its unique set of challenges for learners. A question that often pops up is: "Which language is the hardest to learn?". Today we take a closer look at this question and consider different factors that make learning a language challenging.
Preparing students for the modern world of work can be challenging; it's constantly adapting and changing which can be hard to keep track of. Today's post is a Q&A on the teaching of employability skills and the essential skills and qualities students need to thrive in the 21st century. offers insights to answer common questions and help you improve your language teaching skills.
My advice is to push the issue back to the students by putting them in groups and asking each group to identify a different job/career that involves people working with others. Then ask "What would happen if this job was competitiveinstead of collaborative?" You may have to re-explain the concepts, but let students write a little story about a situation in which the workers suddenly all turn competitive.
For example, It was chaos in the women's soccer/football game. All the members of the blue team suddenly became competitive. Each one still wanted to win, but each decided that she would shoot a ball into the other team's net. This included the goalie, who ran up to the front of the field and purposely shoved and tripped members of her team ….
Or for more traditional jobs, In the middle of the operation the nurse pushed the doctor out of the way and picked up the instrument. The patient also wasn’t completely asleep, and he tried to do the operation himself, then …
It's all absurd, of course, but it can lead into other tasks asking students why collaboration is so important in each job. Then, turning it back to language, what kinds of language does each profession require to collaborate? For soccer/football players, this includes shouted requests and commands:Pass the ball to me! Shoot!as well as hand and body gestures. Similarly, doctors require professional jargon:Pass me the scalpel, please.Rather thanGive me the pointy knife thing!
One hundred years ago, in 1923, Thomas Edison predicted that motion pictures would replace teachers and books. Since then, similar predictions have been made for radio, TV and computers. It hasn't happened, and one of the reasons is that we crave the human touch in our teaching and learning. I recently read. "When it comes to getting knowledge to stick, there may be no substitute for human relationships. … I've been to former students' weddings and baby showers and funerals of their parents," says Millard, the high school English teacher in Michigan. "I've hugged my students. I've high-fived my students. I've cried with my students. A computer will never do that. Ever, ever.”(Waxman, 2023, para. 21-22)
But, that doesn’t mean teachers should stop learning about new technologies. We need to keep finding ways for them to help us and our language learners in the classroom. It can seem overwhelming, though, which is why I recommend shifting responsibility to students: “Do any of you know about ChatGPT? Yes? How do you think you could use it to help you learn?”
Although the world is in many ways competitive, there are countless examples of how students will do better by collaborating. Most of our students today won't be working in environments where they are competing against their co-workers. Instead, they'll be in teams and need critical thinking and negotiation skills to help them do so.
One way forward is to ensure that your classroom features more collaborative activities. Get students working in pairs and groups on all their assignments, but also create a buddy system so students always have someone else to ask for help. For example, if they're having to read a text and come across difficulties, it's often easier for them to text or call a friend than to wait until the next class. After a few collaborative activities, discuss collaboration versus competition with students and ask them which they prefer. Also, ask them for examples of what their friends and family members do regarding collaborating and competing.
As always, it's better to lead students to understand a new idea than to tell them.
If you want to learn more, make sure to check out Ken's webinar here. If you'd like to learn more about teaching future skills to students check out21st-century skills and the English language classroom.
Ehsan Gorji is an Iranian teacher, teacher trainer and teacher educator. He also designs strategic plans, devises study syllabuses, runs quality-check observations, and develops materials and tests for different language institutes and schools in the country. Ehsan has been a GSE Thought Leader and Expert Rater since 2016.
Reflective teaching, despite it sounding modern and sophisticated, has not yet become a common practice among English language teachers. However, the experientialproposed byoffers a practical approach for teachers. The cycle involves teaching a lesson, reflecting on "what we did" and "how we did them," and then using that reflection to improve future English classes. By using this approach, teachers can prepare for better teaching in the long term.
