What is rapid prototyping and how can it apply to the ELT classroom?

Nicole Kyriacou
A teacher stood in front of her class with her students raising their hands
Reading time: 5 minutes

Tom Chi is an internet veteran with quite a resumé. His roles have been many and varied – from astrophysical researcher to Fortune 500 consultant and corporate executive, developing new hardware and software products and services.

He worked on Microsoft Outlook when it was in its infancy, was a major influence in taking Yahoo Search from 0 to 90 million users and is now Head of Product Experience at Google X – Alphabet’s secretive division focused on creating technological innovations for the future. It has produced the self-driving car and Google Glass, and its Project Loon aims to provide internet to every square inch of the earth.

At Google X, Tom was in a unique position – always having to think five, ten or even more years ahead in order to conceptualize and build the technology of the future. As you might imagine, this is far from an easy task; not only do the ideas have to be original, but they have to meet people’s future needs – something that is not easy to predict.

So, how does Tom and the others at Google X deliver their vision for the future using today's materials and technology?

That’s where Rapid Prototyping comes in. It’s a concept that allows teams to experiment, learn and adjust prototypes quickly and cheaply, so that projects (and products) get off the ground. Failure is seen as a starting block and an inevitable part of the learning process. Following his workshop, we are going to look further at rapid prototyping and how it can relate to the ELT classroom.

Integrating rapid prototyping into ELT teaching
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What are the rules of rapid prototyping?

According to Tom, rapid prototyping follows four main principles:

Rule #1:

You must find the quickest path to experience. Ideas are nothing until they have been tested; prototyping is the quickest path to go from guessing into direct experience. You will soon see an idea’s strengths, weaknesses and potential once you have tried it.

Rule #2:

Doing is the best kind of thinking. People are very good at imagining things, but until we try them, we won’t know what works and what doesn’t. By actually doing something, we’ll come up with new ideas, new challenges and new solutions.

Rule #3:

Prototyping follows a distinct pathway: from conjecture, to experimentation, to results and finally decision-making. Ìý

Prototyping rule #4:

Prototyping helps us reason in time and space: instead of lots of planning, imagination and guesswork, it makes us build something real, considering real use cases and real situations.

How does rapid prototyping affect progress?

The rapid-prototyping learning loop follows this pattern:

  1. Building a variation
  2. Testing with customers
  3. Observing results
  4. Adjusting from results

This process allows us to increase the chances of success in any given project dramatically. For example, if an idea has a 5% chance of success, by trying it 20 times there is a 64% chance of success and by trying it 50 times, there is a 92% chance of success.

Considering that each time an idea is prototyped, learning takes place, the chances of success are likely even higher with every trial.

But wait, what has rapid prototyping got to do with the ELT classroom?

Tom shows how a technology giant like Google can innovate and produce results quickly and efficiently through rapid prototyping – and all the while, he is explaining how much faster learning is when we experiment and do things.

Encouraging project-based work

Of course, our students are not innovating or building new products for tech companies, they are aiming to learn a language. But as Tom said, learning by doing is much faster and more effective than simply conjecturing and talking about theories.

Following his first rule of prototyping (find the quickest path to experience), we need to give students the experience of using the language as fast as possible. Project and task-based learning allows students to build their vocabulary, and test their grammar and overall communications skills in an authentic way.

This also covers Tom’s second rule (doing is the best type of thinking). If we can tap into our students’ creativity, we can allow them to experiment with language, discover what they know and what they don’t know, and then really work on learning the things they need for certain tasks.

When it comes to Tom’s third rule (conjecture, experimentation, results, decision-making), the teacher has more responsibility. We need to look at how we benchmark, measure and analyze our learner’s progress. Without a pathway, our students will not know how they are progressing and may easily lose motivation. The students therefore need to have a firm idea of their abilities, when they need to learn and how they are currently performing. We can then make decisions regarding individual class plans and syllabi.

Finally, by exposing our students to authentic materials, we cover Tom’s fourth rule (reason in time and space). Authentic readings, listenings and videos give learners the opportunity to work with real-world language, trying out what they are learning in authentic contexts. It helps them imagine using the language outside of the safe environment of the classroom too, giving them the challenge they need to push them into learning faster.

Fostering a growth mindset

The very fact that Tom’s team is able to imagine and prototype what seem like impossible ideas – that then have the potential to change the world – is awe-inspiring.

We can all learn from his vision, tenacity and methodology. At the heart of the experimentation and learning at Google X lies a growth mindset.

If we help our students develop a growth mindset, they will see failure as an opportunity to learn, as well as a challenge as a chance to grow, and feedback as a constructive way to improve.

Learning is a dynamic process. As teachers, it’s important for us to look outside the world of education to find inspiration and ideas. We hope this has sparked your curiosity and added a dash of inspiration for your future classes.

Read our post 'Fostering critical thinking in the classroom' and the webinar 'Develop a growth mindset'.

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  • Hands typing at a laptop with symbols

    Can computers really mark exams? Benefits of ELT automated assessments

    By ÃÛÌÒapp 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.  a²Ô»å VersantÌýtests – 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 assessmentÌýto 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.â€

    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 ÃÛÌÒapp, 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. 

    ÃÛÌÒapp English International Certificate (PEIC)

    ÃÛÌÒapp English International Certificate (PEIC) also uses automated assessment technology. With a two-hour test available on-demand to take at home or at school (or at a secure test center). Using a combination of advanced speech recognition and exam grading technology and the expertise of professional ELT exam markers worldwide, our patented software can measure English language ability.