How data and analytics are changing the way we learn the English language

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As demand for English language learning continues to grow in popularity across Asia, innovations in learning technologies are changing how educators in the region are approaching the challenge of equipping non-native speakers with the proficiency required to succeed in academia and the professional world.   

The increased functionality of tech-assisted study, which allows teachers to collect and analyse student performance in near real-time and use that data to customise the learning experience, is having a profound effect on how teachers and students are approaching English language teaching (ELT), and some of the region’s most enthusiastic language learners are benefiting.  

Emerging economies such as Vietnam and Thailand have an especially “huge demand and appetite for language learning,” driven largely by the recognition that being able to comprehend and converse in another language, particularly English, is a powerful driver for, primarily, career advancement, says Stuart Connor, Pearson Asia’s Qualifications & Assessment Director.  

The governments of both countries are recognising this demand and are shaping their English language and vocational curriculums to give their citizens the helping hand they need to prosper in the global economy. They are “acutely aware of how important English is going to be to future prosperity, to driving a growing economy, and to attracting more foreign direct investment,” says Stuart.  

Of course, preparing learners of English for a successful future call for the right course materials, learning environments and qualifications. This includes setting high benchmarks for success, such as using materials based on international ESL (English as a Second Language) standards and aiming for a level of B1, or intermediate, level as measured in the Common European Framework of Reference (CEFR), according to Stuart.   

Deep dive into data

As commendable as these ambitious targets are, the following questions remain:

  • At what level are students starting their language-learning journey?
  • Is there enough time to get students to the level expected by the time they graduate?
  • Are teachers sufficiently qualified or skilled to be able to teach the skills that need teaching?
  • Do teachers have the resources they need to be able to drive improvements?  

To that end, gathering and analysing learning and proficiency data is increasingly becoming part of an educator’s toolbox. Each student has their own needs, and it's important to have individual learning pathways, points out Kayo Taguchi, Pearson Asia’s ELT Portfolio Manager. Knowing exactly what a learner’s true level is, and having clear goals for progress over a specific period of time, are key to managing their language development.   

“Everyone learns at a different pace. In the same class, you could have slow learners as well as fast learners,” says Kayo. “Each of them has different strengths and challenges and these need to be addressed.”   

This is made possible by the continuous collection and analysis of data, which can identify strengths and weaknesses at a granular level. When this information is fed back into the learning process, it helps to create a feedback loop that enables the creation of a unique, customised and effective learning experience for the student. As Stuart notes, “The feedback cycle of teach, learn, assess – it's just ongoing.”  

Having that level of insight, Kayo says, is key to keeping students enthusiastic and inspired to continue learning. “Being able to identify an individual’s strengths and challenges will help educators build student motivation,” she says, adding that tech-assisted learning environments can be invaluable to the process.  

The future of language learning  

So, how does technology assist educators in the quest to teach better? “Pearson uses a range of tools, including artificial intelligence, to gather and analyse data on the learning process in order to decipher patterns and create portraits of a classroom and its individual students at scale and at speed,” according to Stuart.   

Pearson’s data-driven analytics abilities mean that it can capture highly specific details, and present the information quickly and in a way that teachers can understand. They can then use that knowledge to make better decisions around how they teach, and how they focus and curate each learner’s approach.   

For instance, Pearson uses machine learning to rapidly and accurately score tests and break down each student’s performance by skill, even speaking skills. And if a learner has a specific weakness say, at a certain level of speaking in a certain context, there will be feedback and recommendations as to which particular sections of the courseware can effectively address this particular gap in their skill level, all powered by technology, all without human intervention.  

Educators are also acquiring the ability to impart training, gather data and analyse performance remotely, something that is increasingly becoming important. “We're having to completely change our teaching methodologies due to the coronavirus pandemic, as we move at unprecedented speeds towards remote and online learning,” says Stuart.   

Pearson is adapting to the new ground realities of an increasingly digital world by integrating assessments into courseware that can be accessed digitally through the company’s learning platforms.  

Ultimately, it’s clear that however the world may look when we return to a “new normal”, the influence of technology and data on pedagogy is real and here to stay.