Analytics and Adaptive Learning
The "one-size-fits-all" approach to education doesn't make the grade.
What we need is more adaptive learning. It's the antithesis of those outdated and unimaginative teaching methods that made some classes feel like torture sessions designed to kill us through sheer boredom.
And thanks to the latest advances in AI and machine learning, adaptive learning now has the potential to provide everyone with the kind of education they need and deserve
What is adaptive learning?
Adaptive learning is based on the premise that every student has unique educational needs. Adaptive learning practices identify these needs and adjust teaching methods or resources accordingly.
It's been around for a long time. We've all experienced it, if only in analogue form. Setting students in school is the obvious example. This is when schools test students and then group them by ability in specific subjects. Someone excellent at reading and writing but not so great at algebra can be in the top set for English and the middle for mathematics.
The only problem with this type of adaptive learning is that it's not really that adaptive. And it's definitely not personalised.
Adaptive learning technology
Adaptive learning technology offers a more tailored learning experience. Again, adaptive learning technology is nothing new. However, until relatively recently, it was limited by what is known as the rules-based system.
The rule-based system creates several different learning pathways through a set syllabus. Students take regular quizzes and assessments, determining which channels match their learning styles and needs. Like those Choose Your Own Adventure books, readers participate in the story by making choices. The choices take you to a different page, which branches the story along various branches.
You've probably figured out where we're going with this one.
Like a choose-your-own-adventure book, a rules-based adaptive learning platform can feel personalised. But, in reality, the user is simply selecting from a set of predetermined (and very limited) options.
Artificial Intelligence and adaptive learning technology
Artificial intelligence is a game-changer for adaptive learning technology. By integrating sophisticated machine learning into software and platforms, programmers have switched from a rules-based system to a recommendation system.
In simple terms, the recommendation system uses machine learning and artificial intelligence to create ever-evolving learning programs for each student. There are no set pathways. Instead, learning pathways are designed and customised based on the user's performance and other factors, including skill and competency level, knowledge gaps, and learning styles. This isn't choosing your own learning pathway; it's creating your own learning pathway
AI-powered adaptive learning is like having your very own personal tutor, revision coach, academic adviser, and mentor all rolled into one. They never get tired or frustrated, no matter how many times you need to ask the same question. And they never take a day off. With adaptive learning, world-class, 24-7, personalised education support is, quite literally, always at your fingertips.
Examples of adaptive learning software
So what do these adaptive learning platforms look like?
Atom Learning is one of the UK's leading online adaptive edtech companies. It has two platforms: Atom Nucleus and Atom Prime.
Atom Nucleus provides revision materials and personalised learning experiences for pupils at home. Atom's machine learning algorithm adapts the difficulty of the questions and exercises to suit the user's learning style. It also paces learning to match their ideal learning speeds. Activities include live or prerecorded lessons in English, math, science, verbal reasoning, and non-verbal reasoning. Or users can take fun quizzes, tests, and unlimited mock exams or practice papers, all with real-time feedback features.
Atom Prime is an online learning platform designed for schools. It uses the same adaptive technology and machine learning algorithm as Atom Nucleus, although there are a few extra benefits for teachers. With the Nucleus platform, teachers can better manage mixed-ability classes and use data-driven insights to provide parents with more detailed reports on their child's academic performance.
Atom Prime and Atom Nucleus sum up everything that is genuinely great about AI and analytics learning. Thirty school children could be using the same Atom platform in the same classroom, yet each would have a completely different learning experience designed solely for them.
Moreover, through a data and analytics-led approach, Atom's platforms find the optimal learning point for every user. Optimal learning occurs when we feel challenged but not overwhelmed. It's often described as a flow state, a period in which we are immersed in a task and energised by the thrill of pushing our skillset to the very limit.
How analytics improves learning
Analytics provides the crucial insights necessary for truly individual learning. The analytics approach empowers every student, regardless of their learning style or other personal factors. It gives all students the same chance to become the best version of themselves, fulfilling their full potential and maximising their long-term life opportunities.
Analytics programs can increase accountability for teachers and students, measure student progress, inform curriculum decisions, and identify students at risk of failing or dropping out.
It can also spot additional learning needs that would have otherwise gone unnoticed. In fact, this is one of the most important and exciting developments in the space. In a research paper titled Learning Analytics for Inclusive Higher Education, computer science researcher Weiqin Chen highlights how an increasing number of educators and designers are working on adaptive learning interfaces for students with learning and health disabilities.
To summarise, the benefits of learning analytics are:
- Measuring key indicators of student performance.
- Supporting student development.
- Understanding and improving the effectiveness of teaching practices.
- Informing institutional decisions and strategy.
- Identifying students with special learning or educational needs.
- Providing equal opportunity to all students, regardless of any disabilities.
The future of analytics and adaptive learning
In his recent book, Advancing the Power of Learning Analytics and Big Data in Education, edtech marketing and technology expert Ahmet Dogukan outlined a three-point mission statement for the future of adaptive learning development.
Dogukan believes those working in the industry should focus on the following:
- Predicting future student performance based on past learning patterns.
- Providing students with unique feedback tailored to their answers, intervening where they have diﬃculty.
- Personalising each student's learning process, revealing their strengths and encouraging their development in other areas.
But how does it happen?
It all comes back to the analytics.
"Data analytics is the most important force in increasing learning efficiency. Learners have different personality traits, learning styles, learning backgrounds, learning needs, expectations, interests. and learning speeds."
"Data analysis must be customised to the student's needs. One of the most important points to be considered in data learning analytics is the need to follow a bottom-up approach, where apps and online courses are designed and adapted to relevant information about each individual user."
Dogukan makes a solid - if slightly convoluted - point. But we'll let him off. Academics have their own unique way of speaking to each other.
At its core, what Dogukan is saying is as obvious as it is important. If education is going to become more personalised, then it has to...yes, you guessed it...actually become personalised. In other words, these adaptive apps and platforms must feel like they were designed for the person using them, even when thousands of others are using them simultaneously.
How higher education is improving student performance with data
Researchers at Georgia State University used the data-centric, user-focused approach to create a bespoke adaptive learning platform that identified students at risk of falling behind or dropping out. The predictive analytics tool raised student performance and graduation rates by 8%, preventing thousands of young people from leaving college with nothing except a giant mountain of debt.
At the University of Florida, Spanish language instructors Anne Prucha and Kacie Tartt worked with adaptive learning app designers from Pegasus Innovation Lab to create cloud-based learning software based on several course textbooks. When tested in a pilot programme, the software — which crafts individual learning paths for students based on their performance — led to a 23% increase in better learning outcomes, with more students earning A, B or C grades.
What's next for adaptive learning technology?
The boom times are coming for anyone designing and delivering these new, innovative adaptive learning platforms.
According to the Astute Analytica study on the global adaptive learning software industry, the market is expected to grow by 20% over the next decade, taking the total market value to over $700 million.
The report predicts a massive adoption uptake by schools, colleges, universities, businesses, and governments looking to provide free learning resources designed to boost digital inclusion rates.
Other primary growth factors include:
- The entrance of major tech players into the space.
- High levels of technological penetration and awareness.
- The continuing shift toward software as a service (SaaS) licensing and delivery models.
As for the next-generation platforms and apps, who knows what future innovators will come up with as machine learning and AI evolve at exponential rates? Like in any creative tech space, the most exciting and game-changing future applications are the ones we can't even imagine yet. Exciting times ahead.
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