AI and personal analytics provide a ‘fitbit’ for the Intellect

It is far too long since I last posted to this blog: too many jobs and too little time would be my fist attempt at an excuse. But, perhaps it is just that I am not effective enough, that I need better self-regulatory skills, more intelligence and a better understanding of my own strengths and weaknesses. I talk quite a lot about intelligence and about how AI developers have not yet designed artificially intelligence systems that understand themselves and have metacognitive awareness, but maybe I too lack these abilities? So, how might I become more self-effective?

This thought is one that I intend to worry at while I am completing a research trip to the University of Sydney to work with my colleague Judy Kay. We are working on Personal Analytics for Learners (PALs), or more precisely interface designs for PALs (or iPALs).

In order to help me thing this through I wanted to learn more about some of the work that Judy and her colleague Kalina Yacef have been doing in collaboration with medics and health professionals to develop better data analytics and interfaces for personal health information for education. For example, the iEngage project provides a digital platform for children with information, education and skills to help them to achieve their physical activity and nutritional goals. It connects with ‘misfit‘ activity trackers to provide continuous feedback and summarise the daily activity on a dashboard.

To this end, I bought myself a ‘misfit’: a somewhat cheaper version of a ‘fitbit’ with a great name :-). I am now tracking my sleep and my pulse as well as my physical activity and diet in order to try and understand more about my personal wellbeing. This is nothing new and millions of other people do this too. I notice that popular technology stores stock a good range of fitness tracking devices and increasingly more reasonable prices.

IMG_4190  IMG_4191.jpg

So, in order to also help me be better at understanding my mind and my cognitive progression and metacognitive skills and regulation, I now need a ‘mindset” to help me track how well I am thinking, learning and regulating my working and learning. The interface to such a ‘mindset’ is the idea behind the iPAL that Judy and I are currently designing. I find it interesting to speculate about the kinds of data that we could collect about our intellectual and social interactions that would help us track and better understand our intellectual mental wellbeing as well as our physical welding and fitness. This kind of ‘fitbit’ for the mind might help me to be less distracted by non-priority activities and spend more time on priorities, such as writing.

A search for ‘fitbit for the mind’ yields some hits, though not terrifically interesting ones. There is an article in new scientist about eye-tracking to tell you more about your reading habits, and a mindfulness app that can be linked to fitbit data. The problem here is that we are being offered some automatic tracking of just one type of mental activity – reading, or mindfulness and actually we need something way more sophisticated to tell us about how we our intelligence and self-awareness is progressing. Perhaps something that looks at multiple data sources and provides us with an overview of our activity in a way that motivates us to want to know more about our intellectual fitness in the same way that activity trackers help us understand more about our physical fitness.

Earlier this month, there was a more interesting article in Newsweek that talks about ‘iBrain’ and the possibility for us to be able to track our brain’s electrical output and see markers for the likely occurrence of a range of mental health disorders from anxiety, depression, and schizophrenia, to dementia and Alzheimer’s before symptoms appear. Such information might help early intervention and monitoring. This reminds me of the rise of personal DNA services, such as 23 and me. If people are interested in their DNA and what it might tell them about how they should adjust their lifestyles to avoid certain conditions that they look to be susceptible to, then maybe people are also curious about their intelligence and how they can understand it better.


Over the next few blog posts I plan to explore what such a device might be like, what data it might collect and how I might best benefit from the sorts of information it could provide.

Annotation may be old-fashioned, but it is effective for learning and can be supported with technology

5510a4270443ecab4f9b41f11c9e8152When I was at school and university I spent a lot of time taking notes. These notes were sometimes much better than others, but they were always invaluable for revision – even if only to identify the gaps in my knowledge.

I still take notes, in meetings and at talks, for example. Increasingly these notes are technology based and much more structured than those of my youth. In fact, part of the reason I blog is to help myself remember key findings and issues as well as to flag them up for others. Another of the 19 learning acts that I have been discussing is:

Annotation. This can be described as an interaction with existing knowledge material (particularly text) such that the learner is able to construct a personal elaboration of that material. This might take the form of elaborative commentary closely attached to the original (“marginal notes”) or it might be a form of personal précis or reflection (“summarising” etc.). Knowledge is thereby elaborated or personalised.

This practice suits situations where a body of well-formed material is available for study. Its cognitive benefit arises from the effort of actively re-casting material and selectively linking it with existing knowledge. Clearly annotation is something that can be done more or less skilfully and demands practice and support.

Technology can structure annotation as well as providing an infrastructure for composition and storage. There are many tools for writing and filing the results of annotation and a whole range of practices for organisation.

Strategic browsing can support learning, but many learners do not have the requisite skills

I want to return to the 19 Learning Acts that have been observed to occur when students are learning with digital media and to consider the act of Browsing.

A while ago I wrote about the OECD report into 15-year-olds’ educational attainment in maths, reading, science and digital skills. The negative message from this report was that less use of the internet is linked to better reading performance and frequent use of technology in school is linked to lower performance. BUT digging deeper into the data revealed that what students most commonly do with computers is dominated by browsing the internet, with 42% of students doing this once a week or more. When students did schoolwork at home, once again browsing was the most popular activity.

So what is browsing and how can it be more effective?

Browsing is an interaction with knowledge structures whereby the learner searches for relevant items. This search may be improvised and opportunistic: this is the more traditional sense of “browsing” OR it can be guided by principled rules in which case we would call it “strategic” browsing. It is fine for learners to take part in some exploration through unguided browsing, but it is essential that educators also help learners to develop the skills necessary to become more strategic.


The sorts of skills needed can be found in many accounts of 21st Century skills,  for example, that proposed by the World Economic Forum. These include critical thinking and persistence, both of which would help learners to be more strategic in their browsing, particularly if we add in a good dose of meta cognition. Critical thinking and meta cognitive skills can be taught and they have been show to support learning.

Learning with technology: exposing expert knowledge through rich media and interactivity

cartoonI was talking at a school governors’ conference last week and decided to introduce the concept of the Learning Acts as a way to explore how technology can best be used to support learning within schools (and beyond). I have blogged about the 19 Learning Acts[1] that have been observed to occur when students are learning with digital media.

The governors I spoke to found the idea of the Learning Acts extremely helpful and I thought therefore it would be worth thinking about some of them in more detail here.



The firsrowlandson_-_chemical_lecturest Learning Act I’m going to consider is Exposition. This act can be thought of as a learner’s private interaction with the author or speaker who is presenting a narrative account of knowledge. The expert’s Knowledge is thereby “exposed” to the learner. A lecture or a television programme, are examples of Exposition and these can be very effective.


However, in order for Exposition to be most productive for learners it should be used with expertise that can be transmitted in a structured narrative form.


blueplanet_cmyklrgThe success of Exposition in support of learning lies in the depth of the private interactions that are elicited from the (otherwise passive) learner. Technology can offer an excellent means to support such depth of interaction through the use of material that is vivid or representationally rich. The learner will need to use or develop the knowledge construction skills to enable them to process the information presented by experts and to construct their own understanding: their own narrative.

Of course the real art of teaching through the Learning Acts is in the way the different acts are blended together and again technology can help. For example, we could blend Browsing, Ludic and Simulation Learning Acts into an activity or a lesson and use well designed Interactive Video to support students. Interactive Video is an increasingly popular presentation format with powerful examples available on-line, for example the Channel 4 video at the link below.

Watch out for discussions of more Learning Acts in future blog posts.


Interactive Video Example



[1] Manches, A., Phillips, B., Crook, C., Chowcat, I., & Sharples, M. (2010). CAPITAL-Curriculum and Pedagogy in Technology Assisted Learning.

The Bett(er) the Learning Science, the more intelligent the technology

In my trip to Bett this year I was lucky enough to speak in the arena on the opening day when the place was abuzz. I spoke about the Learning Sciences and in particular the concepts of Metacognitive theory and theory of Deliberate Practice. The first of these concepts is about thinking about our own thinking. It can be thought of as Metacognitive Awareness and Metacognitive Control, and it is important for learning. Metacognition has been shown to be a better predictor of primary school children’s performance than IQ tests, and Metacognitive ability has been shown to have a positive impact on learning across the ages. The great thing about Metacognition is that it can be taught to metacognition-image-e1411763056642learners of all abilities, and in particular it can be taught by intelligent software as well as by human teachers. The second concept, that of Deliberate Practice confirms the adage that I was all too frequently reminded about as a child: Practice makes Perfect. Well, yes it does, provided that it is effort full, that the learner is motivated and that the practice is accompanied by appropriate feedback and the capacity for learners to see their own success. The qualities of practice just listed are within the skill sets of intelligent software as well as being in the skill sets of intelligent humans. Both human and machine are enriched by our knowledge of these concepts.

I was accompanied on the stage at Bett by David Levin the CEO of McGraw Hill . David charted his company’s development path from books, through e-books to adaptive software. This adaptive software enables each learner to take a personalized variable learning path through the educational material about his or her subject. The software tracks each learner’s progress and identifies areas of weakness so that help can be provided. For teachers, this type of software provides detailed monitoring information straight away and for the developers of the educational content used by the system there is also speedy feedback about what is working well and what needs redevelopment.


In addition to David’s presentation, I was really struck by the increased appearance of Adaptive Systems or software at Bett. These are systems that use Artificial Intelligence (AI) techniques to  support student learning by adapting to what the student needs: just like a skilled human tutor. dcqehohll3cy5ytjkdvqThis adaptation might, for example be to change the difficulty of the task the student has been set, or it could be to increase or decrease the extent of the helpful interventions that the software provides while the student is trying to solve a problem. This type of software uses AI to provide such tutorial individualization and I believe that AI may finally have achieved a position that will enable it to provide benefits for Education akin to those Siri has provided for personal assistance.

industry-40-and-its-technological-needs-12-638The adaptive software I saw at Bett was very like the adaptive systems that have been developed and studied by the Artificial Intelligence and Education community for several decades now. Indeed the whole topic of Artificial Intelligence (AI) is very much in the news at present, with much attention being paid to its role in the Fourth Industrial Revolution, in particular the way in which AI is replacing people in an increasing variety of jobs.


brainology-some-amazing-facts-you-didnt-know-about-your-brain_519a2c0a6a4c9_w1500One area that the AIEd community has paid increased attention to is the use of findings from the Learning Sciences to inform the design of AIEd systems. For example, psychologists, such as Carol Dweck, have spent many years developing a theory about motivation that is based upon the idea that some people have a growth mindset, whilst others have a fixed mindset. Those with a growth mindset believe that their abilities can be developed, but those with a fixed mindset believe that intelligence or learning ability is simply a fixed trait. There is a growing body of evidence that shows students’ mindsets play a key role in their success. And again, as with metacognition, students can be taught that the brain is like a muscle that gets stronger and works better the more it is exercised. They can also be taught that every time they stretch themselves, work hard, and learn something new, their brain forms new connections and that, over time, they become smarter. There is growing evidence to demonstrate that changing students’ mind-sets can have a substantial impact on their grades and achievement test score.

Human teachers can of course help learners to develop a growth mindset, and so can AIEd systems. In both instances: the human and the AI, the teaching is better for being informed by the Learning Sciences.



Why Educational Technology needs the Learning Sciences

I very much enjoyed myself at the EdTech Forum organised by Founders Factory and at Bett. On both occasions I spoke about the Learning Sciences and we had some great discussions. BUT what are the Learning Sciences and why are they so important for EdTech?


The Learning Sciences is an interdisciplinary research field that aims to increase our understanding of the learning process and to engage in the design and implementation of learning innovations, such as those enabled by technology. The disciplines encompassed include cognitive science, educational psychology, computer science, anthropology, sociology, information sciences, neurosciences, education, design studies, and instructional design.

This increased understanding about how people learn ought to  underpin the design of all EdTech, but sadly it does not. A couple of examples might help here.

The Self-Explanation Effect

Micheline Chi now at Arizona State University has done some fascinating work on what she calls: The Self-explanation effect and cognitive engagement. Simply put, students learn better when they explain to themselves the material they are working on. The self-explanation effect has been studied across age groups, subjects, and educational contexts. Evidence shows that a learner explaining an idea to themselves will learn more, because the process of self-explaining is a constructive learning activity.


(Table from

Constructive learning is a form of active learning. Evidence suggest that it is useful to consider 4 types of engagement:

  • Passive engagement: e.g. hearing an explanation
  • Active engagement: e.g. summarising an explanation
  • Constructive engagement: e.g. explaining to oneself
  • Interactive engagement: e.g. explaining to another

Studies have shown that students gain the most knowledge and improve their understanding during interactive engagement, followed by constructive engagement and active engagement, the least learning gains were achieved after passive engagement.

For EdTech this is relevant and has for example been used to inform the design of prompts and scaffolds within EdTech applications to encourage self-explanation in learners.

The Acts of Learning

The self-explanation effect is a very specific example of ways that the Learning Sciences are producing findings that are relevant for EdTech developers and users. A more general example can be found in that of Learning Acts: a product of the CAPITAL project. The table below illustrates the 19 Learning Acts that have been observed to occur when learning occurs with digital media, these are grouped into four meta-categories.

For example, a standard video would promote and support Exposition as a Learning Act. This Act is characterised as a private interaction with an author or speaker who is presenting a narrative account of knowledge to a learner. Knowledge is thereby “exposed”. This practice is well suited to situations where expertise can be transmitted in structured narrative form. The success of this form of learning depends on the depth of the private interaction elicited from the (otherwise passive) learner. Thus technology may support such depth of interaction by making material vivid or representationally rich. Interactive video is likely to also promote Learning Acts such as Browsing, Ludic, Simulation and Problem Focussed. The effectiveness with which the range of different Learning Acts is supported through the design of interactive EdTech will impact upon its Learning effectiveness.


For more information about the self-explanation effect see for example,

For more information about the Learning Acts see – Manches, A., Phillips, B., Crook, C., Chowcat, I., & Sharples, M. (2010). CAPITAL-Curriculum and Pedagogy in Technology Assisted Learning.