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The Future of Personalised Learning



Technology-enabled personalized learning systems continue to capture the imaginations of educators, and for good reason. The promise of tailored learning for every student, providing them just what they need when they need it to master a concept at just the right pace and with just the right kind of help to do so all at a massive scale is tantalizing.

Personalized learning refers to instruction in which the pace of learning and the instructional approach are optimized for the needs of each learner. Learning objectives, instructional approaches, and instructional content (and its sequencing) may all vary based on learner needs. In addition, learning activities are made available that are meaningful and relevant to learners, driven by their interests and often self-initiated. (NETP 2016)

But realizing this vision is incredibly complicated. There are so many variables to measure and control for, so many inferences to judge and decision points to set, that the prospect becomes immediately overwhelming. The natural instinct of many designers of these systems has been to reduce the variables as much as possible, thus exerting greater control over the environment and the learner, with the hope of increasing the reliability of outcomes.

Design Continuum: Closed vs Open

In doing so, designers move to one end of a design continuum. That continuum runs from closed and controlled on the one hand vs open and context aware on the other. The closed, controlled approach reminds me of a virtual reality headset. It encloses the learner in the system, isolating them, and attempts to provide for their every need, without referencing a larger context. It reserves most of the control of the system for itself.

In contrast, I think of an open, context aware approach as an augmented reality (AR) display found on modern fighter jets and more and more in the automotive industry. This approach overlays critical digital information on top of the outside world by projecting it onto the cockpit window or the windshield of a car. It provides the pilot or driver constant information synchronized with the changing external conditions. More recently, AR is showing up in the entertainment industry and even in classrooms. It situates and supports the user in a broader context with the goal to prompt and interpret, but not to control the learner.

Closed Personalized Learning Systems

The closed approach to design characterizes the vast majority of the first wave of personalized learning systems in schools and most of the intelligent tutoring systems that preceded them. As a result, most personalized learning systems tend to:

  • Be stand alone solutions
  • Focus on a single subject area
  • Run independently of other data systems
  • Run independently of the teacher’s input and control
  • Limit the learners agency in decision making about their learning

They are algorithm centric; that is, they assume the algorithm is going to teach the student in the best and fastest way possible. If based on sound research from the learning sciences, it may very well be that the algorithm will outperform more naive approaches. But systems architected in this manner may also have significant disadvantages:

  • They tend to isolate the student from peers and teachers
  • They tend to bog down when a student is not just a little bit off, but a lot off (when Hint #3 and Alternative Explanation #5 still doesn’t do the trick)
  • They provide a siloed dataset that relates only to itself and which tends to be reported only within the system
  • They don’t benefit from knowledge that teachers have about students from other contexts or from insight that students might have about themselves
  • They are generally unaware of learning experiences outside of the system that might have changed the mastery level and/or learning needs of students
  • They are entirely dependent upon the itemset within the system to build their adaptations.

Like virtual reality headsets, they create a learning context around the student and immerse them in it. This provides tremendous control to the system designer to eliminate distractions, which can be beneficial to learners who might otherwise become overwhelmed. But it also blocks out other, potentially helpful, sources of input.

Context Aware Personalized Learning Systems

Personalized learning systems could be more powerful, more useful, more relevant, and more accurate if they reoriented from being algorithm centric to being context aware. This approach still runs on algorithms, of course, but, unlike a closed system, a context-aware system is open to other sources of external input that may be able to provide crucial information. These systems tend to leave a significant degree of decision-making up to the user of the system.

Context aware personalized learning systems would have the following characteristics:

1. Be aware of other points of input and use them to inform its algorithm.

a. They would gather information from external assessments; from other learning systems; from teachers, coaches, mentors, and peers; and from available background information on student’s strengths and needs. They would be hungry for data from outside sources and would use this to troubleshoot and adjust their own assertions and predictions about the learner.

2. Be aware of other points of output and allow data to flow out in machine readable formats.

a. They would feed their output data into broader teacher dashboards and enable a view of the whole student by seamlessly contributing their data as one piece of a larger puzzle. They would provide data about learners in formats useful to researchers, administrators, and school counselors, all according to laws and best practices around privacy and security.

3. Be aware of teaching and learning that happens outside of the system.

a. They would include a mechanism to allow a teacher (or the learners themselves) to input that the learner had practiced or mastered material externally to the system. Of course, the system could still provide a brief quiz, test, or activity to verify the accuracy of the report.b. They would include mechanisms that would also allow a teacher to report potential regression, perhaps after a long absence or summer break. The system would then be alerted to follow up to verify that previously mastered concepts were still intact and remediate as needed.c. More advanced systems could also enable and track learning outside of the system and account for it within the system. For example, they could suggest activities outside of the system that the student is either ready for or that the student needs extra help with. After the external activity takes place, they incorporate the results, verifying mastery as needed.

4. Be aware of and responsive to input from external experts, such as teachers and researchers.

a. They would recognize external learning experts and be appropriately responsive to their input. For example, the system might make broader adjustments to its algorithms, increasing or decreasing difficulty levels based on expert input. Of course, the system could still verify this input with additional measurement and could present any discrepancies between the expert’s assessment and the systems assessment back to the expert for consideration.

5. Be aware of and responsive to input from the student.

a. While learners can be unreliable sources of information about their own mastery, systems that collect feedback from them could benefit from understanding their perceived efficacy, progress, and emotional state, all of which impacts learning and performance. Students could share when they were feeling like the system was going too fast or too slow or to cry “Help!” when they were stuck somewhere and the system was not recognizing their distress. Students could also indicate their level of effort or confidence in their responses. This feedback could be used to adjust to their frame of mind and also could be used to improve the system.b. They would allow the learner to have meaningful agency in their learning. This approach would allow the user to make substantial choices (not just choosing whether to do addition first or subtraction first) that allow them to tailor content to their interests and needs. This approach would work to maximize student choice while still providing sophisticated customization of learning.

Of course, these systems also have drawbacks. As alluded to above, student (and teacher) input can be subjective, incomplete, or just plain wrong. That is why systems designed in this way also need safeguards built in to validate the information coming from external sources.

First Steps

A context aware personalized learning system will require significant change. It will certainly require a paradigm shift among many who design these systems. It will require robust and meaningful data interoperability standards among personalized learning systems at a level of granularity that does not presently exist. It also will require a great deal of new engineering and functionality. It is hard to imagine these kinds of systems being fully functional anytime soon. However, we can start now by creating a foundation for this functionality in our current systems by:

  • Complying with interoperable data standards
  • Creating APIs for both receiving and sharing data with other systems
  • Designing systems capable of processing learning data from external activities and assessments
  • Designing algorithms to evaluate external inputs and adapt accordingly
  • Maximizing and prioritizing features that support learning agency

In short, this approach would open up closed systems to make them responsive to other systems and responsive to teachers, learners, and others outside of the system and what they might do to impact learning. More sophisticated implementations would become a partner with the those outside the system in adjusting the algorithms inside the system while also verifying that the changes are justified.

A Crossroads: Virtual Reality or Augmented Reality

We are at a crossroads in our design approach to personalized learning. We can keep burrowing in, creating an ever narrower and more controlled environment, like a VR world that grows ever more elaborate and detailed, yet exists for and isolates a single person inside an enclosed headset. Or we can turn outward to create context aware systems that function like augmented reality displays that sense, interpret, and add value to the wider world of learning, leaving as much agency in the hands of the learner as possible. The path to context aware systems is longer and more difficult, but ultimately may lead to more meaningful learning and engagement that, in my view, is worth the effort.


About the Author

This article was written by Joseph South is Director of the Office of Educational Technology at the U.S. Department of Education.

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Bitcoin is a Bubble and I will tell you why



Humans are considered rational beings. Every once in a while greed and exuberance trumps rationale we end up with a Bubble. Bitcoin is a bubble and it will fall precipitously. Here’s why…

Some Economics

Value of any product is arrived at through a process that matches demand and supply. If there is a lot of demand for a product and few people offering it, the price go up. There is a greater perceived value for it since the supply is limited — Everyone who wants it, cannot have it. The vice versa is also true. But as with most things in life there are certain exceptions to this rule.

High-end luxury products have an aspirational value and hence the higher the price the higher the demand tends to be. These are called Veblen goods. There are also Giffin goods where this effect is seen with inferior goods.

Either way, in all of these cases price is a consequence of consumption.

There is another case where prices can be made to rise artificially, through hoarding and creating artificial scarcity. The hoarder buys large quantity of a good and waits for the price to rise high enough before beginning to sell it slowly to the actual consumer at an elevated price.

Markets play an essential role is matching demand and supply, which results in price discovery. Markets are the price discovery platform that most of us depend on. We have markets for everything, stocks, currency, commodity, bonds, etc. Most of these trades take place through instruments that are representative of the same. Stock is a company is represented by shares — Stock here represents the assets of a company and the ownership is attributed through shares. There are similar trading instruments for everything that is traded.

The place where this trade is managed, which I referred to an a market earlier, is known as an Exchange. An exchange is where trades are executed and the instruments change hands between the buyer and the seller. The job of an exchange is to provide a framework, to regulate and enable the trade to take place.


Let us say you have a Rs. 10/- currency note. You take this note and buy tea from a tea stall. He in turn takes the note and pays for the fuel bill. He in turns takes the note and pays the school fees for his child. The note has been used for several transactions but we do not know where it originated from and how many hands it changed. If this note were an online token we could track it all the way through.

If there are a set number of tokens in circulation and each of the token can be tracked, there is no way that any fake token can be introduced without changing the total number of token in the system. Furthermore if an anomaly is found, it can be quickly tracked back to its origin.

A Blockchain is a chain of records which are called Blocks. Each block represents one transaction and hence the entire history of an single instrument can be tracked from beginning to the end. A blockchain is what makes it possible for us to track every token. Research on blockchain began in 1991 but the distributed blockchain, which is the basis of all modern blockchain was invented in 2008. The distributed blockchain kept the block of records on every computer that is a part of the system. This redundancy is the secret sauce that make blockchain a phenomenal technology.

This makes it near impossible to fake any transaction because that fake transaction. It is not enough to enter a fake transaction in your own block, the same transaction needs to exist in every copy that is part of the system. Each copy is protected by public key encryption on each user’s system (If you wish to know how encryption works). If any anomaly is found, it can be quickly localized and eliminated.


Bitcoin is one of the implementations of blockchain as a currency. Bitcoin tokens can be mined by solving mathematical problems, but the total supply of bitcoin available is limited by the algorithm. The more bitcoins get mined, the harder it becomes to mine further. The mathematical problems are solved using the computer but the problems take longer and longer to solve as times goes on.

Now, once you have these Bitcoins, you need a way to transact, for which bitcoin wallets exist, where these coins get stored. The wallet is your copy of the blockchain.

Some people thought, “Hey! Why not trade bitcoin?” and they created Bitcoin exchanged. Just like a stock exchange, Bitcoin is bought and sold on Bitcoin exchanges. There are several across the world and they execute bitcoin sale and purchase.

Individuals and companies have been mining bitcoins since it was introduced. Today this mining has assumed industrial scale with more and more people getting interested and mining becoming harder and harder. There are entire server farms that are being committed to mining bitcoins and in all likelihood these are being hoarded for a future date when it would likely be sold.

Value of any product finally lies in its consumption

The value of anything is down to consumers finally adopting the product and using it. This is where demand invariably arrives out of. Whether it is businesses or individuals, utilisation is the key. Keeping something does not create value unless it is an antique. Bitcoin is definitely not an antique.


The graph aboves shows the confirmed Bitcoin transactions per day. At its lowermost it is about 130,000 and at its peak its at about 365,000. It averages out at about 275,000 per day.

Let me just add some perspective. Visa processes about 24,000 transactions per second. So in about 12 seconds Visa does the entire days worth of transactions on Bitcoin!

Although this is not a straight comparison since Visa is a method of exchanging money while Bitcoin itself is a store of value. The market capitalisation of Visa as a company stands at USD 230 Billion while that of Bitcoin stands at USD about 70 Billion dollars. A third of the value of Visa??

Comparing it with gold, which is a store of value unlike Visa which is a transaction mechanism akin to Blockchain; Comex which is a commodity exchange based out Chicago (one of many across the world) does about 289,000 gold contracts per day. The number across the world would probably be in the millions, not to mention the transactions that take place through stores, banks and other means.

There are about 16,500,000 Bitcoins available today. Out of this only about 640,000 is exchanged everyday.

Hoarders will dump

I think the value ascribed to bitcoin given its abysmally small circulation is purely due to the hoarding that many are engaging in. Most of the people just buy bitcoin for the purposes of speculation.

People buy bitcoin and then they keep it.Since nobody is selling (Would you, if you know what you have is doubling in value every 6 months?) — Prices rise.People hear prices are rising — They clamor to buyDemand rises — Price risesSome of the early hoarders keep releasing small amounts of it

The above graph illustrates how this works. For price to rise, the demand has to be high; this demand should be powered by consumption and not hoarding.

My take on this is that the price rise of Bitcoin is fake. It is powered by speculators who are willing to pay more and more in the hope that prices would keep rising. The limit on the supply is additionally helpful in driving the prices up and keeping them there.

Looking into the past

There are plenty of cautionary tales of bubbles but for me the one that most closely matches this is — The Tulip Mania.

Tulips by themselves had no great value.

Tulip was a unique flower and was used for royal gifting. The prices of tulips shot up suddenly on speculative purchase of tulip futures. There were, many who made money during the upsurge. After a couple of years of frenzied buying, the demand for buying newer and newer contracts seemed absent. There was no inherent value in it. Panic set in and ultimately it suddenly collapsed in Feb 1637. Within 3 months all of the value was wiped out, because there was none to begin with!

The same is true of Bitcoin today. Its not like Bitcoin is the preferred currency for transaction or that people are switching to transacting through bitcoin at unforeseen pace. A crazy number of speculators are buying into it for the sake of speculation. There is no inherent value and one day in the not so distant future people will realise it.


About the Author

This article was written by Vivek Srinivasan.

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Finding the Market for Your Technology



We come across lots of gifted would-be founders. Some are the business-school or ex-financier type. Others have more specific expertise: product, engineering, AI or machine learning. For those with specific skills, and particularly those with deep technical aptitude in AI/ML – we often find an amazing piece of technology, new process or a proprietary algorithm that has been developed without a market to aim at.

Key takeaways:

  • Not everyone has to have gone to Harvard Business School to identify a killer use case
  • Teach yourself some basic top-down market analysis and layer on some common sense
  • The size of the market will drive who you should be looking to raise from

For all those of you who can identify with this… Forward Partners are here to help. We’re investors in Applied AI businesses. Taking technical know-how and applying it to a big use-case can seem like a daunting task. Hopefully this article can provide you with a 5 minute MBA, at least in regard to finding a market for your work. We’re going to do a ‘top-down’ investigation of a potential use-case for computer vision and, at a later stage, machine learning.

If you’ve been building, for example, technology or a set of algorithms – you’ve possibly been ‘going from the bottom up’. You’ve been applying your set of skills to a problem that you personally know a lot about. That may, or may not, be applicable for a large amount of people or a big market: the use-case. We’ll come to the importance of market size later.

Finding and validating that killer use-case will probably take some top down thinking. The best place to start is to identify industries and verticals where there are big problems yet to be solved by technology in any real way. That sounds a bit abstract but it’s fairly easy to interpolate. A good assumption as to the degree of tech substitution or advancement in a consumer market is the rate of inflation in a given category.

You can see, from this graph, that education and care are areas that are ripe for technology to come in, solve some problems and release some value. Given that 1-on-1 or in-person education is assumed to be the best way to learn for the time being, and thus hard to substitute technology into that equation meaningfully, let’s take healthcare forward.

Now it’s time to do a little bit of common sense validation. What are some possible macro-trends driving increasingly expensive health care? We all know that we are living longer and therefore there are more elderly people. Our environment is also changing, contributing to a wider range of potential health problems that we may suffer from. Knowing this, it’s a decent assumption to think that the price rises in healthcare have been driven by job creation and an increase in manual tasks. I just typed in “rise in number of healthcare workers” into Google and this next barchart is the 4th image result.


This is a really interesting result. We’re getting somewhere with identifying a killer use case: we’ve got a massive market and price rises likely being driven by increased employment in relatively-low skilled jobs. This is an almost perfect use-case for software.

That’s where you come in. If you’re an technical expert, you’ll know best about what is a tractable problem that you could help to solve. Talking to a nurse or medical assistant or two should reveal a couple of insights about what they spend large swathes of time on. I’d guess that you could drive huge efficiencies by helping to solve for the amount of paperwork that has to be done e.g. using computer vision to transcribe physical, handwritten records to digital. That’s no easy task. Nor is deriving insight from the data that you’ll end up with. Though these are the kind of tough problems in markets ripe for disruption that talented founders go after and that VCs love to back.

One important thing to know, regardless of what you’re working on, is that if you’d like to attract institutional funding you’re going to need to go after a big market. At Forward Partners, we need to be able to be convinced that every investment *could* return our fund. We have a £60m fund so that means that if we were to own 10% of your business we’d need to see your business have an exit value of £600m. There aren’t that many markets where that’s achievable, so that should help to narrow it down. The healthcare markets are massive and so the value that can be released by streamlining processes and improving outcomes is often well above the minimum market size bar. If you land on a slightly more niche area, this is something to bear in mind.

The final point is that, much like we’re not expecting the classic MBA-style founder to possess in-depth technical knowledge about computer vision, we’re not expecting founders with highly specific skill sets to come in and hit us with a pitch deck and business plan a la Harvard. There’s a minimum bar though, and hopefully we’ve been able to demonstrate that it’s pretty easy to overcome.


About the Author

This article was written by Matthew Bradley, Investor at Path Forward Path Forward. The Path Forward was developed by Forward Partners, a VC platform that invests in the best ideas and brilliant people. Forward Partners devised The Path Forward to help their founders validate their ideas, build a product, achieve traction, hire a team and raise follow on funding all in the space of 12 months. The Path Forward is a fantastic startup framework for you to utilise as an early stage founder or operator. The framework clearly defines startup creation as being comprised of three steps. The first step of this framework involves understanding customer’s needs.Nic is Head of PR & communications at Forward Partners. Over the course of a 10 year career in communications, he has working with global brands including Orange, Warner Bros., BBC, and

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