Explaining computerized English testing in plain English

ɫèAV Languages
a pair of hands typing at a laptop

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 ɫèAV’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.

ɫèAV 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 ɫèAV’s Ordinate technology

The spoken portion of PTE Academic is automatically scored using ɫèAV’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 ɫèAV’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?

Written scoring

Spoken scoring

  • Word choice
  • Grammar and mechanics
  • Progression of ideas
  • Organization
  • Style, tone
  • Paragraph structure
  • Development, coherence
  • Point of view
  • Task completion
  • Sentence mastery
  • Content
  • Vocabulary
  • Accuracy
  • Pronunciation
  • Intonation
  • Fluency
  • Expressiveness
  • Pragmatics

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  • A group of students stood around a teacher on a laptop

    The ethical challenges of AI in education

    By Billie Jago
    Reading time: 5 minutes

    AI is revolutionising every industry, and language learning is no exception. AI tools can provide students with unprecedented access to things like real-time feedback, instant translation and AI-generated texts, to name but a few.

    AI can be highly beneficial to language education by enhancing our students’ process of learning, rather than simply being used by students to ‘demonstrate’ a product of learning. However, this is easier said than done, and given that AI is an innovative tool in the classroom, it is crucial that educators help students to maintain authenticity in their work and prevent AI-assisted ‘cheating’. With this in mind, striking a balance between AI integration and academic integrity is critical.

    How AI impacts language learning

    Generative AI tools such as ChatGPT and Gemini have made it easier than ever for students to refine and develop their writing. However, these tools also raise concerns about whether submitted texts are student-produced, and if so, to what extent. If students rely on text generation tools instead of their own skills, our understanding of our students’ abilities may not reflect their true proficiency.

    Another issue is that if students continue to use AI for a skill they are capable of doing on their own, they’re likely to eventually lose that skill or become significantly worse at it.

    These points create a significant ethical dilemma:

    • How does AI support learning, or does it (have the potential to) replace the learning process?
    • How can educators differentiate between genuine student ability and AI-assisted responses?

    AI-integration strategies

    There are many ways in which educators can integrate AI responsibly, while encouraging our learners to do so too.

    1.Redesign tasks to make them more ‘AI-resistant’

    No task can be completely ‘AI-resistant’, but there are ways in which teachers can adapt coursebook tasks or take inspiration from activities in order to make them less susceptible to being completed using AI.

    For example:

    • Adapt writing tasks to be hyperlocal or context-specific. Generative AI is less likely to be able to generate texts that are context-bound. Focus on local issues and developments, as well as school or classroom-related topics. A great example is having students write a report on current facilities in their classroom and suggestions for improving the learning environment.
    • Focus on the process of writing rather than the final product. Have students use mind maps to make plans for their writing, have them highlight notes from this that they use in their text and then reflect on the steps they took once they’ve written their piece.
    • Use multimodal learning. Begin a writing task with a class survey, debate or discussion, then have students write up their findings into a report, essay, article or other task type.
    • Design tasks with skill-building at the core. Have students use their critical thinking skills to analyse what AI produces, creatively adapt its output and problem solve by fact-checking AI-generated text.

    2.Use AI so that students understand you know how to use it

    Depending on the policies in your institution, if you can use AI in the classroom with your students, they will see that you know about different AI tools and their output. A useful idea is to generate a text as a class, and have students critically analyse the AI-generated text. What do they think was done well? What could be improved? What would they have done differently?

    You can also discuss the ethical implications of AI in education (and other industries) with your students, to understand their view on it and better see in what situations they might see AI as a help or a hindrance.

    3.Use the GSE Learning Objectives to build confidence in language abilities

    Sometimes, students might turn to AI if they don’t know where to start with a task or lack confidence in their language abilities. With this in mind, it’s important to help your students understand where their language abilities are and what they’re working towards, with tangible evidence of learning. This is where the GSE Learning Objectives can help.

    The Global Scale of English (GSE) provides detailed, skill-specific objectives at every proficiency level, from 10 to 90. These can be used to break down complex skills into achievable steps, allowing students to see exactly what they need to do to improve their language abilities at a granular level.

    • Start by sharing the GSE Learning Objectives with students at the start of class to ensure they know what the expectations and language goals are for the lesson. At the end of the lesson, you can then have students reflect on their learning and find evidence of their achievement through their in-class work and what they’ve produced or demonstrated.
    • Set short-term GSE Learning Objectives for the four key skills – speaking, listening, reading and writing. That way, students will know what they’re working towards and have a clear idea of their language progression.
  • Students sat ina library studying with laptops in front of them chatting to eachother

    Teaching engaging exam classes for teenagers

    By Billie Jago
    Reading time: 4 minutes

    Teachers all over the world know just how challenging it can be to catch their students’ interest and keep them engaged - and it’s true whether you’re teaching online or in a real-world classroom.

    Students have different learning motivations; some may be working towards their exam because they want to, and some because they have to, and the repetitiveness of going over exam tasks can often lead to boredom and a lack of interest in the lesson.

    So, what can we do to increase students’ motivation and add variation to our classes to maintain interest?

    Engage students by adding differentiation to task types

    We first need to consider the four main skills and consider how to differentiate how we deliver exam tasks and how we have students complete them.

    Speaking - A communicative, freer practice activity to encourage peer feedback.

    Put students into pairs and assign them as A and B. Set up the classroom so pairs of chairs are facing each other - if you’re teaching online, put students in individual breakaway rooms.

    Hand out (or digitally distribute) the first part of a speaking exam, which is often about ‘getting to know you’. Have student A’s act as the examiner and B’s as the candidate.

    Set a visible timer according to the exam timings and have students work their way through the questions, simulating a real-life exam. Have ‘the examiners’ think of something their partner does well and something they think they could improve. You can even distribute the marking scheme and allow them to use this as a basis for their peer feedback. Once time is up, ask student B’s to move to the next ‘examiner’ for the next part of the speaking test. Continue this way, then ask students to switch roles.

    Note: If you teach online and your teaching platforms allow it, you can record the conversations and have students review their own performances. However, for privacy reasons, do not save these videos.

    Listening – A student-centered, online activity to practice listening for detail or summarising.

    Ask pairs of students to set up individual online conference call accounts on a platform like Teams or Zoom.

    Have pairs call each other without the video on and tell each other a story or a description of something that has happened for their partner to listen to. This could be a show they’ve watched, an album they’ve listened to, or a holiday they’ve been on, for example. Ask students to write a summary of what their partner has said, or get them to write specific information (numbers, or correctly spelt words) such as character or song names or stats, for example. Begin the next class by sharing what students heard. Students can also record the conversations without video for further review and reflection afterwards.

    Writing –A story-writing group activity to encourage peer learning.

    Give each student a piece of paper and have them draw a face at the top of the page. Ask them to give a name to the face, then write five adjectives about their appearance and five about their personality. You could also have them write five adjectives to describe where the story is set (place).

    Give the story’s opening sentence to the class, e.g. It was a cold, dark night and… then ask students to write their character’s name + was, and then have them finish the sentence. Pass the stories around the class so that each student can add a sentence each time, using the vocabulary at the top of the page to help them.

    Reading –A timed, keyword-based activity to help students with gist.

    Distribute a copy of a text to students. Ask them to scan the text to find specific words that you give them, related to the topic. For example, if the text is about the world of work, ask students to find as many jobs or workplace words as they can in the set amount of time. Have students raise their hands or stand up when they have their answers, award points, and have a whole class discussion on where the words are and how they relate to the comprehension questions or the understanding of the text as a whole.

    All 4 skills –A dynamic activity to get students moving.

    Set up a circuit-style activity with different ‘stations’ around the classroom, for example:

    • Listening
    • Reading
    • Writing (1 paragraph)
    • Use of English (or grammar/vocabulary).

    Set a timer for students to attempt one part from this exam paper, then have them move round to the next station. This activity can be used to introduce students to certain exam tasks, or a way to challenge students once they’ve built their confidence in certain areas.

  • A teachet stood in front of a class in front of a board, smiling at his students.

    How to assess your learners using the GSE Assessment Frameworks

    By Billie Jago
    Reading time: 4 minutes

    With language learning, assessing both the quality and the quantity of language use is crucial for accurate proficiency evaluation. While evaluating quantity (for example the number of words written or the duration of spoken production) can provide insights into a learner's fluency and engagement in a task, it doesn’t show a full picture of a learner’s language competence. For this, they would also need to be evaluated on the quality of what they produce (such as the appropriateness, accuracy and complexity of language use). The quality also considers factors such as grammatical accuracy, lexical choice, coherence and the ability to convey meaning effectively.

    In order to measure the quality of different language skills, you can use the Global Scale of English (GSE) assessment frameworks.

    Developed in collaboration with assessment experts, the GSE Assessment Frameworks are intended to be used alongside the GSE Learning Objectives to help you assess the proficiency of your learners.

    There are two GSE Assessment Frameworks: one for adults and one for young learners.

    What are the GSE Assessment Frameworks?

    • The GSE Assessment Frameworks are intended to be used alongside the GSE Learning Objectives to help teachers assess their learners’ proficiency of all four skills (speaking, listening, reading and writing).
    • The GSE Learning Objectives focus on the things a learner can do, while the GSE Assessment Frameworks focus on how well a learner can do these things.
    • It can help provide you with examples of what proficiencies your learners should be demonstrating.
    • It can help teachers pinpoint students' specific areas of strength and weakness more accurately, facilitating targeted instruction and personalized learning plans.
    • It can also help to motivate your learners, as their progress is evidenced and they can see a clear path for improvement.

    An example of the GSE Assessment Frameworks

    This example is from the Adult Assessment Framework for speaking.

    As you can see, there are sub-skills within speaking (andfor the other three main overarching skills – writing, listening and reading). Within speaking, these areproductionandfluency, spoken interaction, language range andaccuracy.

    The GSE range (and corresponding CEFR level) is shown at the top of each column, and there are descriptors that students should ideally demonstrate at that level.

    However, it is important to note that students may sit across different ranges, depending on the sub-skill. For example, your student may show evidence of GSE 43-50 production and fluency and spoken interaction, but they may need to improve their language range and accuracy, and therefore sit in a range of GSE 36-42 for these sub-skills.