Tuesday, May 16, 2023

Smart algorithms work for Google Facebook & Amazon can they work for learning?

Algorithms are everywhere
Christopher Steiner in Automate This: How algorithms came to rule the world describes how algorithms have pretty much got everywhere these days. Use the web, you’re using algorithms. Engage with the financial world, you’re engaging with a world driven by algorithms. Look at any image on any screen, use your mobile, satnav and any other piece of technology and you’re in the world of algorithms. From markets to medicine, the 21st century is being shaped by the power of algorithms to do things faster, cheaper and better than we humans, and their reach is getting bigger every day.
Learning is algorithmic
Our brains clearly take inputs but it is the processing, especially deep processing, that shapes understanding, memory and recall. The brain is not a vast warehouse that stores and recalls memories like video sequences and text, it is a complex, algorithmic organ. However, it would be a mistake to see the brain as a ‘simple’ set of algorithms for learning. Take ‘memory’ alone. We have many types of memory; sensory memory, working memory, long-term memory, flash-bulb memory, memory for faces, and remarkably, memory for remembering future events. Huge gains have been made in understanding how these different types of memory work in terms of our computational brain. Even if one rejects the computational model of the brain, alternative models do seem to require algorithmic explanations, namely inputs, processing and outputs. Learning is therefore always algorithmic.
You’d also be mistaken if you thought that the world of online learning has been immune from this algorithmic reach. From informal to formal online learning, from search to sims, efficient online learning has already been heavily powered by algorithms.
Every time you search for something on Google you’re using seriously powerful algorithms to crawl the web and match its findings to your search item, then produce a set of probable results. It’s algorithms do many other things you may not be aware of, such as identify cheats who create lots of artificial links and keywords to boost their ratings. Every time you use Facebook algorithms determine your newsfeed. The more you engage with someone, the more likely you are to see their posts, and the more Facebook people in general that engage with posts, determines how often that post is presented to others. Every time you use Amazon to see book rankings or get recommendations or offers, they use what they know about you and others to offer you choices. Wouldn’t it be astonishing if this did not happen more and more in online learning?
Even in formal learning, there are algorithms galore. In flight simulators, it’s not just the image-rendering and motion modelling that’s driven by algorithms, it’s also the learning through scenarios and feedback. In games-based learning there are algorithms aplenty. I first implemented algorithms in a BT simulator in the 1980s. What’s new is the fact that the restraints we had then have all but disappeared, with faster processing, better memory and ability to gather large amounts of data for use by adaptive algorithms. They’re here and they’re here to stay.
Teaching is essentially algorithmic, as the teacher (agent) gathers data about the environment (knowledge and students) and attempts to deliver recommendations or responses, based on their experience. In some cases, such as search and citation-driven research, algorithms do this much faster and better than humans. Teachers, tutors and lecturers, most of whom teach many students, have limited abilities when it comes to gathering data about their students, so no matter how good they are at teaching, tailored, personalised feedback and learning is difficult. It is also difficult to identify what students find difficult to learn. What, for example, are the most difficult concepts or weaknesses in the course? Again, data gathered from individual students and large numbers of students, allow algorithms to do their magic.
The bottom line, and there is a bottom line here, is that warm-body teachers are expensive to train, expensive to pay, expensive to retire, variable in quality and, most important of all, non-scalable. This is why education continues to be so expensive. There is no way that current supply can meet future demand at a reasonable cost. The solution to this problem cannot be throwing money at non-scalable solutions. Smart, online solutions that take some elements of good teaching, and replicate them in software, must be considered. As Sebastian Thrun says, ““For me, scale has always been a fascination—how to make something small large. I think that’s often where problems lie in society—take a good idea and make it scale to many people.”
We have seen how smart algorithms, in Google. Facebook and Amazon can significantly enhance users’ experiences, so how can they enhance the learners’ experiences? It is often thought that data is the key factor in this process but data is inert and only becomes useful as input to algorithms that interpret that data to produce useful actions and recommendations. The quality and quantity of data is important but it is the quality of the algorithms that really counts.
Smart software, formula-based algorithms, based on the science of learning, can be used to take inputs and make best guesses based on calculated probabilities. Multiple, linked algorithms can be even smarter. They may use knowledge of you as a learner, such as your past learning experiences and so on. Then, dynamically track how you’re getting on through formative assessment, how long you’re taking in a task, when you get stuck, keystroke patterns and so on. Add to this data gathered from other groups of similar learners and the system gets very smart. So smart, it can serve up optimised, learning experiences that fit your exact needs, rather than the next step in a linear course. The fact that it also learns as it goes make it even smarter.
Iceberg principle
When we learn, most of the processes going on in our brain are invisible, namely the deep processing involved in memory and recall. This activity, that lies beneath consciousness, is where most of the consolidation takes place. Similarly, it is sometimes difficult to see adaptive, algorithmic learning in practice, as most of the heavy lifting is done by algorithms that lie behind the content. Like an iceberg the hard work is done invisibly, below the level of visible content. Different students may proceed through different routes on their learning journey, some may finish faster, others get help more often and take longer. Overall, adaptive, algorithmic systems promise to take learners through tailored learning journeys. This is important, as the major reasons for learner demotivation and drop-out are difficulties with the course and linear, one size-fits-all courses that leave a large percentage of students behind when they get stuck or lost.
One symptom of the recognition of the need for adaptive, algorithmic learning is a recent poll by Inside Higher Ed and Gallup of college and university presidents, who saw more potential in ‘adaptive learning’ (66%) to make a “positive impact on higher education” than they did MOOCs (42%). The 2013 Insider Higher Ed Survey of College and University Presidents. Conducted by Gallup. Scott Jaschik and Doug Lederman, eds. March 2013.

Why would this be so? Well, it’s not only college and University Presidents, the Gates Foundation have also backed ‘adaptive learning’ as an investment. Read their two recent commissioned reports, as well as their funded activity in this area. One UK company, singled out by these reports, as a world class adaptive, algorithmic learning company is CogBooks, who have already taken adaptive learning to the US market. The reason is that it is seen as a way to tackle the problem of drop-out and extended, expensive courses. Personalised learning, tailored to every individual student, has come of age and may just be a solution to these problems.

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