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An assumption persists in the educational community that more mature teachers are much more difficult and reluctant to be trained on the effective use of educational technology. To some degree, I think this assumption has been built on by the digital native vs digital immigrant myth. But as someone who has trained teachers of all ages all over the world, I would say that, from my own experience, this hasn’t been the case.

What I have found to be the case is that more mature teachers are:

• less likely to be lured by the shiny hardware and the seemingly wonderful claims made to go along with it.
• more critical and skeptical about the way technology is used in the classroom.
• less confident when using various apps and websites and less likely to explore the different features.
• more easily discouraged by failures.
• less familiar with various tools, applications and services that have become part of everyday life for younger users.
• more likely to be able to see through “technology for technology’s sake” classroom applications.

So how should trainers approach the challenges of working with these teachers? Here are a few tips from my own experience of training older teachers to use technology.

Be sure of your ground pedagogically

So many edtech trainers are great with technology, but much less versed in educational theory and pedagogy. More mature teachers are more likely to have a more robust theoretical understanding, so be prepared to back up your ideas with sound pedagogical insights and try to relate your training back to theories of learning and pedagogical approaches. 

Make sure training is hands-on

Running through a list of tools and ideas in a presentation may have some value, but it doesn’t come anywhere close to the impact of giving teachers hands-on experience and the chance to actually work with the tech to create something. 

Give solid examples of what you have done

Being able to speak from experience about how you have used tech with your own students will have far more impact than theoretical applications of “You could do blah blah blah with your students.” Sharing anecdotes of how you have used technology in your classes, the challenges you have faced and how you have overcome or even been overcome by them can really lend credibility to your training. 

Manage expectations

A positive attitude is great, but be also prepared to point out weaknesses, and potential pitfalls and talk about your own failures. This might help your trainees avoid the same mistakes and stop them from becoming disillusioned. 

Make time to experiment and explore

Don’t be tempted to cram in as many tools, techniques and activities as possible. Incorporate project time into your training so that teachers have the chance to go away and explore the things that interest them most and get their own perspective on how they can use them with students. 

Back up technical training

Learning to use new tools is getting easier all the time, especially on mobile, but it’s still relatively easy for teachers to forget which button to press or which link to follow. So back up any demonstrations with an illustrated step-by-step guide or a video tutorial that teachers can return to later. 

Make their lives easier

Using technologies that can make what they already do a bit easier or a bit quicker is a great way to start. For example, I have a link to a tool that really quickly creates a . Sharing tools like this that start from what teachers already do can really help to get them on your side. 

Do things that can’t be done

One of the most common remarks made by more mature teachers about technology is: “Well, that’s fine, but you can do that without tech by …” If you can show examples of technology use that go beyond what is already possible in the classroom, then you are much more likely to get capture their enthusiasm.��One example of this is the use of collaborative writing tools like����and its ability to track, record and show how students constructed text.

Solve classroom problems

Being able to spot a genuine classroom problem and show how technology can solve it can be very persuasive. One example of this is gist reading which can be very challenging to teach because students tend to ignore time limits. Cue Prompters��can give teachers control of the text and push students to gist read at the speed the teacher chooses. Problem solved. 

Plan with long-term and short-term goals

However inspiring your training session is, and however short or long it is, you should ensure that teachers leave it with a plan.  are great if you have time to work on them with the teachers. If you don’t have time to get them to create individual SMART plans, at least get them to think about the first step or the first technology application they will try in their classroom and what they will do with it. 

Tech can be implemented in CPD

One of the reasons many mature teachers feel less confident with tech is because they often only use it in the classroom. Showing how technology can become part of their own self-guided CPD and professional practice, and helping them to build their PLN can energize their technology use and make their development much more autonomous and long-lasting. 

Make sure everything works

I can’t emphasize this enough. Make sure you have updated all your plugins, browser versions, etc., and check the network and connectivity and make sure everything runs smoothly. Nothing puts teachers off more quickly than seeing the trainer fail.

Having read this list of tips you are likely to think: “But all technology training should be like that!” Yes, you are right it should, but the truth is we are more likely to be able to get away with lower standards when working with teachers who are already more enthusiastic about tech. So the next time you walk into a training room and see some older teachers there, don’t groan with disappointment, but welcome the opportunity to test your skills and understanding with the most critical audience. If you can send them away motivated to use technology, then you know you are on the right track. 

If you've ever had the feeling a word doesn’t look right after you've typed it, you are not alone.��The most commonly misspelled words from this list pose challenges for more people than you think. English native speaker or not, hard-to-spell words are determined to give you a headache. And if bad spelling does happen, it’s usually in very important contexts like a vital application letter or during a conversation with your crush – which can really change the tone and potentially cause confusion or embarrassment.

English has drawn inspiration from many different languages, so it’s perfectly normal to get confused because of its double consonants and silent letters. We all know that moment when you stare at a word for ages and still can’t believe it has two sets of double letters. There are many such examples. In fact, “misspelled” is one of them and people often misspell it.

Here are some of the most commonly misspelled words in English (both British and American, where necessary), along with their common misspellings.

1. Accommodate not accomodate

Also commonly misspelled as:��acommodate

Let’s start strong with a typical example of double consonants – two sets of them.��

2. Acquire not aquire

Think of this rhyme whenever you encounter the word: 'I c that you want to acquire that wire'.

3. Awkward not akward

It also describes how we feel when we realize we’ve just misspelled a word.

4. Believe not belive

Remember the rhyme ‘I before E, except after C’. The same rule applies to 'believe', so use this mnemonic when in doubt.��There are some exceptions to the rule, so be careful.

5. Bizarre not bizzare

It’s bizarre that there is only one Z but that’s the way It is.��

6. Colleague not collegue

Also commonly misspelled as:��collaegue, coleague

It’s hard to get this one right! Make a funny association like 'the big league of the double Ls', you may just win the misspelling match.

7. Embarrassed not embarassed

Also commonly misspelled as:��embarrased

If you remember this one, you’ll reduce the chances of finding yourself in an embarrassing bad spelling situation.��

8. Entrepreneur not enterpreneur

Also commonly misspelled as:��entrepeneur, entreprenur, entreperneur

It’s not only hard to spell, but also hard to pronounce. The origins? It’s a French word coming from the root entreprendre (‘undertake’).

9. Environment not enviroment

The N is silent, so it’s quite easy to misspell this one too. Luckily, it’s similar to 'government' whose verb is 'to govern' which ends in N. A very long, but good association.��

10. Definitely not definately

Also commonly misspelled as:��deffinately, deffinitely, definitley

You’ll definitely get this one right if you remember it’s not a case of double letters. Neither does it feature any As.��

11. Liaison not liasion

There’s a reason why you’re never sure how to spell 'liaison', 'bureaucracy', 'manoeuvre', 'questionnaire' and 'connoisseur'. They do not follow the same patterns because they are all French words.��

12. License not lisence

��In American English, it’s always spelled 'license' – no matter what. On the other hand, in British English, it’s spelled 'license' when it’s a verb and 'licence' when it’s a noun. Once you decide which spelling you’ll use – American or British – it’s best to go forward with that and stick to it.��

13. Publicly not publically

Words ending in 'ic' receive the 'ally' suffix when transformed into adverbs (e.g., organically). But 'public' makes an exception so it’s understandable if you misspell it.

14. Receive not recieve

Remember the 'I before E, except after C' rule? This is the kind of word where the rule applies. It also applies to 'niece' and 'siege', but it doesn’t apply to 'weird' or 'seize'. So remember the rule but keep in mind it has some exceptions.

15. Responsibility not responsability

People often get tricked by this word’s pronunciation. And if you think about it, it does really sound like it has an A in the middle. Safe to say – it doesn’t. So keep an eye out.

16. Rhythm not rythm

This is another borrowed word; in this instance it comes from the Greek word ‘Rhuthmos’ which mean a reoccurring motion.��

17. Separate not seperate

'Separate' is apparently one of the most misspelled words on Google and it’s understandable why. The same as with 'responsibility', its pronunciation can trick you into thinking there’s an E there.

18. Strength not strenght

Even spelling pros will sometimes have to think twice about this one. Our mind is probably used to seeing the H after the G because of words like 'through'. Not this time though (wink wink).

Don’t forget that the same goes for 'length' (and not 'lenght').

19. Successful not successfull

Also commonly misspelled as:��succesful, sucessful

There are so many double consonants in English, that it can become tempting to double them all at times. But for the love of English, don’t do that to 'successful'.

20. Succinct not succint

Some people would say two Cs are enough. This is why the word 'succinct' gets misspelled so frequently. The third S is indeed very soft, but don’t let pronunciation deceive you.

21. Thorough not thurough

You may have heard of this tongue twister: “English can be understood through tough thorough thought, though.” It’s hard not to get confused with so many similar-looking words. You add an O to 'through' and its pronunciation changes completely.

22. Until not untill

In fact, 'until' was spelled with two Ls in the Middle Ages. If it helps you remember, you can think it just lost some weight but getting rid of the last L (unlike 'still').

23. Whether not wether

Not as confusing as the 'through' and 'thorough' example, but still pretty challenging.

24. Which or witch not wich

Do you know which one is which?

Advice to avoid misspellings

One obvious answer would be spell-checkers, but the truth is that spell-checkers won’t actually help you to improve your spelling. You will continue to misspell words and they’ll continue to correct them. This process is passive and won’t stimulate you to learn the correct spelling because somebody else already does the job for you.��

The best advice? Practice, practice and practice!

If you keep attempting to spell challenging words and checking them it will begin to sink in and become second nature over time. Using tools like dictionaries and language learning apps such as Mondly can help you practice and learn spelling. If you persevere and practice you can avoid any spelling mishaps.��

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.  a�Ի� Versant��tests – 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 assessment��to 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.

Versant

The Versant tests are a great tool to help establish language proficiency benchmarks in any school, organization or business. They are specifically designed for placement tests to determine the appropriate level for the learner.

PTE Academic

The  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.