5 ways to support students with dyslexia

Anna Hasper
A Teacher sat with a child at a desk in a classroom helping them with their writing,

Children seem to be starting English lessons younger than ever, often before they can even read and write. This means that learning differences like dyslexia may not have yet made themselves apparent.

While it’s not a language teacher’s role to diagnose specific learning needs, it is important for us to monitor our young learner students’ progress. If we think a student might be showing signs of dyslexia (or another learning difference), we should feel comfortable referring parents to the right place early on. This can make a huge difference in the learning process.

There are many forms of dyslexia and it affects students in various ways. However, some may include the following:

  • having difficulty reading (especially aloud)
  • struggling with spelling
  • problems remembering the sequence of things
  • finding it hard to follow instructions
  • misbehaving or disrupting the class
  • being very quiet or shy (especially when doing reading or writing activities)
  • falling asleep in class.

Dyslexia is not a learning disability; it’s a learning difference.

What do Magic Johnson, Richard Branson and Tom Cruise have in common? They all have dyslexia. So learners with dyslexia are certainly not less capable; in fact, they often excel in spatial thinking and creativity. The difference is that their brain works differently, so they find visual processing and using their working memory challenging. For example, they may struggle to remember what was said and face challenges when trying to link sounds to letters.

The most common issues are related to reading, spelling and writing, but dyslexia can also impact concentration span and planning skills. And all these challenges have a severe impact on learners’ self-esteem.

Providing effective learning opportunities for young learners with dyslexia might require teachers to reframe how they see dyslexia. Avoid seeing it as a dis-ability, but rather as a form of neurodiversity: the brain functions and learns in different ways.

Creating the conditions for learning

Many – if not most – young learner teachers feel they are not appropriately trained to deal effectively with learners who have dyslexia in a classroom context.

In an ideal world, all EAL and mainstream teachers would receive in-depth training to better deal with neurodiversity in the classroom. But for now, let’s explore some modifications that help create a more enabling learning environment in which all learners – with or without dyslexia – can progress.

1. Getting to know them

If we want all learners to progress to their next level, we need to get to know them. Only then can we provide learning opportunities that start where they are. Get to know their strengths, weaknesses and interests as well as their learning profile; where do they like to work, who do they work well with and what kinds of tasks engage them fully? These are the starting principles of differentiated teaching and all learners will profit from you taking the time to get to know them beyond their name.

Top tip:

Observations are an extremely useful tool to gain insight into learners’ levels and learning preferences. My favorite activity is to get young learners to create a personal profile.

This can be done in their first language – at home with parents – or as a shared writing activity in class. You provide the stem sentences, and learners complete them with drawings or words. You can hang the profiles on the wall and use them to start talking about ‘differences and similarities’. Alternatively, you can have a learner present their buddy to the class based on their profile, depending on the level and age you teach.

2. Creating a collaborative culture in the classroom

If we want learners to help each other in class, we need to create a culture of ‘helping hands’. Focusing on developing good relationships in your classroom, between you and the learners but also between learners, is vital for a collaborative culture. Use activities that focus on building understanding through sharing ideas. Integrating collaborative learning activities will help to establish supportive relationships and makes struggling learners feel more confident in the classroom. They know they can first talk things through with others and ask them for help before completing a task independently. This will benefit all learners, not only learners with dyslexia.

Top tip:

Think-pair-share is a well-known collaborative activity and can easily be adapted to include some movement too in the form of HuSuPuWu!

This activity will help learners share ideas and allow for differentiated thinking time. Ask your young learners a question you want them to respond to, give them thinking time and tell them to put their hand up when they are ready to talk (Hu).

Encourage them to look around, find another person with their hand up and stand up (Su) to walk over and pair up (Pu).

Together they share ideas before returning to their place and writing up their ideas (Wu).

This will be especially beneficial for students who need more time to process, love to move and want to get confirmation or support.

3. Providing multi-sensory tasks and activities

Providing multi-sensory activities is already common practice in most young learner classrooms. It allows learners to process information using their stronger senses while strengthening their weaker areas.

Multi-sensory teaching (MST) acknowledges that all brains learn in unique, different ways and is a well-known method used when working with dyslexic students in their mother tongue. So instead of only telling the story, find images that illustrate the events, draw a story path for learners to follow, or get them to visualize the story.

Doing this increases the ‘routes of memory’ as Kormos (2017) calls it, and enables information to reach the brain via different pathways, visual and auditory, which strengthen the message.

Top tip:

When learning new words, break them into syllables by clapping when you say them. Then show the word and break it up visually (e.g. fri-end), and get them to make the word with playdough or in shaving foam as they say it. Get them to keep saying it as they write it and then check it.

4. Setting clear, manageable instructions

Because dyslexia often impacts working memory, following instructions can be even more challenging than it already is for young learners. We need to reduce the processing load by breaking up instructions into manageable, achievable steps.

Focusing on just a small amount of information better enables learners with dyslexia (Kormos & Smith, 2012) and to be honest, all young learners – and our classroom management – can benefit from this.

Also, check whether you need to ‘tell’ it or can you ‘show’ the instructions? Presenting instructions in a multisensory way where you, for example, use the whiteboard to visualize the instructions, and use gestures and body language to support your oral input will facilitate understanding.

Top tip:

Learners benefit from talking things through as talk plays an integral part in meaning-making. So why not get learners to turn to their elbow buddy and repeat what they need to do in their own words? Another effective way would be to record the instructions so they can listen back as many times as they need.

5. Adapting your materials

Being aware of what works best for the unique brains of learners with dyslexia allows us to tweak existing materials to make learning more accessible. Think about the color of paper you copy on or the background color of your slides. Learners with dyslexia cope better with colored backgrounds as it reduces word blurring. When learning to write new words in their workbook, use a highlighter to highlight the area between the middle lines where the body of the letters needs to be written.

Top tip:

Nowadays, many young learner coursebooks have audio resources available, but not always for readers or stories. Use assistive technology to get the selected reading text recorded. Struggling readers can listen to the audio as they read the text alone. In this way, they will feel that they are reading independently whilst working on letter sound correlation as well as the rhythm of the language.

The English language classroom can be stressful for learners with specific learning needs. Now, we don’t need to – and can’t – ‘fix’ learners, but we should try to ‘fix’ the environment and provide an enabling, inclusive learning environment for all. By tweaking our teaching, we might better enable learners who face challenges, ensure they feel supported in their learning and allow them to bloom in our classroom.

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    Can computers really mark exams? Benefits of ELT automated assessments

    By ɫèAV 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.  and 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 ɫèAV, we have developed a range of assessments using AI technology, including  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.