How to motivate reluctant readers

Sue Alderman
Two children looking over a book together

Reading in English can be one of the most challenging activities for young learners and teenagers, especially when they don’t get much enjoyment from reading in their own language.

These four reading strategies are fun, high-energy, and educational ways of getting even the most reluctant students involved in your reading lessons.

1. Bring outside interests into the classroom

Many students find it hard to get enthused by the reading texts used in their classrooms; they might feature complex vocabulary, be too generic, or just not resonate with their interests. An effective way of reaching out to the more reluctant readers in the class is to use reading materials related to the media they enjoy engaging with in their leisure time.

app’s Marvel series of graded readers provides an ideal opportunity for bringing popular movie culture into your learners’ reading skills development. All of the readers are designed specifically for use in the classroom and feature an integrated skills approach that reinforces vocabulary and helps develop language skills. The readers come with activities to complete throughout the book rather than at the end, and key vocabulary is highlighted and defined.

Excitingly, most of our readers come with downloadable audio files (MP3s), so the students can listen along and hear the stories come to life. The audio can help students model pronunciation, get used to different accents and dialects, and make it even more accessible for students who are still less keen on reading.

2. Gamify the reading experience

By adding simple game dynamics and mechanics to your reading activities, you can add a competitive and fun element to your classes. This could help maintain the interest of learners who might otherwise lose enthusiasm.

The “dictogloss” activity is a good way of adding that extra element as it uses a countdown timer and peer-to-peer interaction to make the reading more of a competitive game.

First, find a good level and age-appropriate story for your students. Before you begin reading the story, tell your students to pay close attention because they are going to re-tell it themselves later.

You will need to read the story to the students in an engaging way, occasionally stopping, and asking students what they think will happen next.

Afterwards, allow the students five minutes to write as much of the story as they can remember in their notebooks.

When time is up, put the students in pairs and allow them to compare stories and correct each other, combining their stories, so they have a complete version. Help students by writing key vocabulary on the board as they request it.

Finally, hand out the original story for students to compare. Get feedback to find out what new vocabulary they have learned and help them make corrections in their stories where needed.

3. Experiment with high-energy activities

Reading doesn’t have to be a sedentary activity. Make use of the classroom space and use movement as a way to motivate and engage your students.

Add a dash of physical activity to your reading task by turning it into a running dictation competition. At the same time, they will practice a whole range of skills; reading, listening, pronunciation, and writing.

Before the class, stick some level-appropriate reading materials to a classroom wall; ideally, you should space it out well and have one reading sheet for every two to four students (the material should be identical).

Put your students into pairs and tell them they are going to have a reading race. Nominate one student to write and another student to dictate.

Students who are writing must sit at a table on the opposite side of the room to the reading material. Students who are dictating must go to the text on the wall, memorize as much of the text as possible, come back to the writer and dictate what they can remember.

Pairs must write as much as they can in four minutes, and when you get halfway through the activity, students should swap roles.

Finally, ask the students to swap their papers and listen to your dictation, making corrections and asking questions as they go. The pair with the longest text and fewest errors is the winner!

4. Go beyond the text

Taking a text and making it into something entirely original can also be a powerful motivator for creative students. Those who complain that reading is boring or too hard will have an extra reason to get through a story if there’s a promise of creative fun at the end of the task.

Tell students that once they have finished reading, they must re-imagine the story and characters and adapt it for a radio show, complete with sound effects, music and scripts.

Depending on how creative your students are feeling, they could write a sequel or a prequel, or adapt the existing story – ideal if you’re using a superhero reader from the Marvel series.

They will need to review vocabulary and pronunciation, remember the details of the original story, explain the characters and their motivations, and plot and write their own scripts. Students can find sound effects on YouTube and record the whole thing on their mobile phones, or a school computer.

By turning a book into a creative project, not only can you motivate students to read, but you will reinforce vocabulary, pronunciation and have a lot of fun doing it.

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    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.  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 app, 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.