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 http://serc.carleton.edu/details/images/26492.html)

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, http://chilab.asu.edu/papers/Wylie_Chi_SelfExplanation.pdf

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. http://www.icde.org/filestore/Resources/Reports/CAPITALfinalreport.pdf




Well designed Artificial Intelligence can bring the ‘Internet of Thingking’ to learners and teachers.

I continue with the theme of Artificial Intelligence and how its tools and techniques can usefully be applied to education. The falling cost of technology combined with the increasing ubiquity and acceptance of Artificial Intelligence bring huge possibilities for building better software and hardware to support people to learn, teach, work and manage more effectively than ever before. The idea of an intelligent assistant for everyone is now a realistic possibility. Imagine how much more satisfying a teacher’s job might become if they had their own personal Artificially Intelligent teaching assistant who could, under their direction, take over teaching a group of students who need help with a particular area of the curriculum: quadratic equations in maths perhaps. Of course, for this to work, it’s essential that the design of such assistants is driven by the needs of teachers and learners and the principles of the Learning Sciences, rather than the capabilities of the technology and its developers. So we need to keep raising our voices for user centred design for EdTech.Girl manager, suitable for use in dialogs with other characters.


But the future of Artificial Intelligence within education can be about so much more than bigger and better systems, there are opportunities to offer something different to teachers and learners. For example, there are some interesting opportunities that arise from the continuing development of wearable computing and the Internet of Things: the network of objects or “things” with embedded computing systems, sensors and network connectivity that can be interconnected with any other network enabled objects or machines. If we think about the Internet of Things as a set of tools through which students can learn about other subjects, such as science or engineering, then we can use them to embrace and enhance the increasingly popular practice-based approach to teaching. Practice-based activities build on the popularity of the ‘Makers Movement’[1] and differ considerably in what they ask students to do and what they are trying to teach. They tend to be open-ended and hands-on, they involve collaborative problem solving processes and can include physical computing and the Internet of Things as tools for learning.


Internet of Things technologies will make it much easier for Artificially Intelligent systems to model the physical body and its movements. These models will then be available for use in educational systems that can be designed to help with tasks such as designing and constructing an artefact in engineering, setting up an experiment in science and simpler individual tasks, such as drawing or writing. The combination of  principles and techniques from the Learning Sciences, the Internet of Things technologies as learning tools and the application of Artificial Intelligence to support the learning process can ensure that the Internet of Things becomes the Internet of Thingking for learners and teachers.

[ File # csp2108410, License # 1307856 ] Licensed through http://www.canstockphoto.com in accordance with the End User License Agreement (http://www.canstockphoto.com/legal.php) (c) Can Stock Photo Inc. / lenm

[ File # csp2108410, License # 1307856 ]
Licensed through http://www.canstockphoto.com in accordance with the End User License Agreement (http://www.canstockphoto.com/legal.php)
(c) Can Stock Photo Inc. / lenm


[1] Halverson, E. R., & Sheridan, K. M. (2014). The Maker Movement in Education. Harvard Educational Review, 84(4), 495-506.

More Intelligence Unleashed: Artificial Intelligence and Natural Man, we need people driven not data driven intelligence.

1980469Last week we held the 2nd in the series of events jointly hosted by London Knowledge Lab and Pearson. This event made me think back to when I first studied AI, some years ago, I loved the fact that it is an interdisciplinary subject that combines psychology, computer science, linguistics, philosophy …. I also loved the fact that even then it was full of promise. Coming from an Education background I could see straightaway that I wanted to know more about how AI and education could be combined. I joined the AIED community and enjoyed my research but there seemed little public appetite for AI with respect to Education. But now AI is everywhere and there is real interest in how AI can improve and support teaching and learning. And here tonight we are going to take a very practical approach and come up with ideas about how AI can address some of the important challenges within our education system. So how might AI enrich education?

Picture2For me the important thing is to look for ways in which artificial intelligence and human intelligence can be combined in complimentary unions.

For example, how about a system that collects data about classroom interactions between teachers and learners, learners and learners and learners and technology – a learner could carry this with them as as part of their persthinking caponal computing device as they attend different classes about maths, geography or English etc. The data collected by the system would be subjected to a series of AI analysis methods that would generate outputs that reflect individual learners’ metacognitive development and suggestions about how this could be improved. Teachers would concern themselves with teaching the specific subjects and the AI technology would link these together and provide invaluable information about one of those all important 21st Century skills.

Or how about taking advantage of the fact that there is now strong evidence to demonstrate the effectiveness of well designed one-to-one tutoring systems that use AI techniques to provide individualized tutoring to a learner. This kind of tutoring could be used to great effect with learners who are struggling and whose parents can’t afford to pay for a home tutor to make sure the child gets into a good school (or annex). It could be funded using pupil premium funds and might help to ‘level the playing field’ a little.


World Economic Forum Report: New Vision for Education, the case of the missing learner.

On 25th September the What the Research Says event at the London Knowledge Lab discussed the World Economic Forum report – New Vision for Education: unlocking the potential of technology.  The presentations from the event can be found on the Resources page of this blog.

imgresThe report stimulated a lively discussion and the meeting benefitted greatly from the presence on-line of Elizabeth Kaufman and Jessica Boccardo from Boston Consulting group who had co-authored on the report. Those present came from a wide range of backgrounds, both within and outwith academia, including commercial developers, think tanks, publishers and educators. All believe in the importance of evidence-based models of innovation and development.


The emphasis upon 21st Century skills was seen as positive and timely. However, there was much discussion about the nature of foundational skills and in particular, whether these are the same now as they were a decade ago, for example is the numeracy now the same numeracy as it was in the millennium? In fact should we be asking: are there new kinds of knowledge that need to be added to the agenda? Foundational literacies, competencies and character qualities are not necessarily mutually exclusive and should not be addressed in isolation. Not all skills are measurable, for example, creativity. In addition, measurement alone is insufficient, what is required is the creation of circumstances in which skills are developed and supported. The group also wondered why the European Commission lifelong learning indicators were not referenced.

Developing comparable indicators to measure progress globally is a huge challenge given the diversity of contexts, likewise, consensus on definitions and globally uniform standards. Context needs to be attributed beyond the country level only – there can be variations at regional, district, school and teacher levels.

child Head.Children Learn to think

child Head.Children Learn to think

There was much agreement that technology can support the development of 21st Century skills and that the potential extended beyond the examples within the report. For example,

  • Adaptive technologies for learning powered by Artificial Intelligence can support foundational skills, but they can also support curiosity, structure personalised feedback, support self-regulation, metacognition and communication;
  • Open data can be used to model good practices and can support skills development specifically: critical skills, analytic skills, research skills, teamwork skills & citizenship skills;
  • There are tensions with the adoption of games that need to be addressed for progress to be made. The reasons for this include: Educational game designers ignoring the importance of the social interactions around games and the fact that games do not fit easily within educational structures.
  • Big data and learning analytics are important technologies missing from the report.



The closed loop model is appealingly straightforward. However the discussion identified concerns with this approach that included: A lack of openness for teachers and learners to engage with the process at every stage, for example to negotiate and con-construct learning objectives. Instruction is only one form of educational approach and that severely limits the application of the closed loop model. Despite an emphasis upon context within the report, there appears to be no accounting for learner context in the closed loop model. A spiral model has been tried and tested and shown to be effective, might that be more appropriate?

The report’s reference to ‘Abundant high-quality content’ was challenged and the group noted that in the Bridge example, teachers had spent considerable time developing lesson content.

The groups experience questioned how many teachers actually use online CPD?

There was much agreement that more evidence is required and that there is a wealth of evidence available within academic institutions. Could this be capitalised upon?

There was also much agreement that a multi-stakeholder approach is essential and suggest the addition of researchers and learners.

(Artificial)Intelligence Unleashed on Education: reasons to be cheerful, part 1

Tomorrow evening I am going to an event being hosted jointly between Pearson and the London Knowledge Lab. It will be the first of three seminars that explore the relationships between Artificial Intelligence and Education.


This first seminar poses several questions and I am discussing each of these over a few blog posts:
“Education is a key area in which AI is increasingly present in tools such as adaptive curricula, online personalized tutors, and teachable agents. So should we be worried? What do we know about how smart technologies work, and what might be realistically possible in the near and distant future? And how can artificial intelligence be best, and most responsibly, leveraged to support teachers in their work to improve outcomes for learners?”
Here are some initial thoughts about each of these:

So should we be worried?

It’s perfectly understandable to be worried about things we don’t really understand and since I suspect that most people don’t understand AI, then that suggests that most people may well feel at least a little apprehensive about AI in the classroom.The truth of the matter is that AI as applied to education is mainly done through building computer models of a particular curriculum, a way of teaching and of the learners who use the AI software. The computer models allow information about learners’ interactions with the AI software to be captured, analysed and used to predict what educational interactions would best suit each particular learner who is using the AI software. A good example of AI based educational software can be found at Carnegie Learning. Their Cognitive Tutors use a ‘model tracing’ approach whereby a subject expert is asked to provide a detailed account of the possible ways in which a student might successfully and unsuccessfully tackle the problems contained within a specified curriculum. The expert’s account is then used as the basis for a computer model of the possible solutions and errors a student might make. As the student progresses through each problem, their path is traced over the model in order to predict what their next steps might be and therefore how the tutor can offer appropriate support. cognitiveTutor_big

These adaptable or personalised software based tutors are not something that we should worry about. They work well for well defined areas of the curriculum, but they do not replace teachers, rather they are complimentary to teachers, because they can free up teacher time to spend on teaching and learning interactions that are not readily replaced by technology.

However there are worries associated with the large-scale uptake of such AI software IF it is seen by managers and administrators as a way of making efficiency savings, rather than a way of maximising the variety of teaching tools that are being used to support learners.

For anyone who wants the research on AI and Education their are several decades of work to be found in the AIED community in their journal and conferences.

OECD Report – Students, Computers and Learning: 3 ways we can do so much better

The 200 page report published yesterday by the OECD is packed with tables and figures that tell a story about the state of 15-year-olds’ educational attainment in maths, reading, science and digital skills in 2012 across the participating countries.

CO7Qte2WUAI_FpkThe negative message from this report has received considerable publicity: countries that have invested heavily in ICT for education do not show improved student achievements in reading, mathematics and science. Less use of the internet is linked to better reading performance and frequent use of technology in school is linked to lower performance. The UK did not participate in this study, but findings being presented to the British Educational Research Association today (Thursday) appear to back it up.

All this sounds very depressing, but it is not the key message we should take away from the report. Instead we should be asking why technology use is not linked to improved attainment and what we should be doing about it.

Andreas Schleichler, OECD director for education and skills, says we must provide teachers with environments that support 21st Century teaching and learning and students with 21st century skills. He states that “Technology is the only way to dramatically expand access to knowledge” and that’s a very important message to take away.

This link between technology and 21st Century teaching and learning is also reflected in another report published earlier this year by the World Economic Forum (WEF): New Vision for Education, Unlocking the Potential of Technology. This report found that technology was an important factor in successful project-based, experiential and inquiry-based learning.

This finding is also reflected in the OECD data. For example, students reported that their teachers used computers to a greater extent in teaching for real-world problems, particularly related to maths and that these teachers were also more inclined and better prepared for student-oriented teaching practices, such as group work, individualised learning and project work and more likely to use digital resources.

There is strong evidence to support the effectiveness of these learning activities and of technology’s important role. For example WEF found that education technology was key to the successful teaching of 21st-century skills such as communication, creativity, persistence and collaboration. So why are the overall PISA findings still negative about technology and attainment?

Three things we can do

I suggest that at least part of the reason for the negative link between computer use and attainment can be found when we explore what students most commonly do with computers. Unsurprisingly it is not project-based, experiential learning. Students’ use of computers at school is dominated by browsing the internet, with 42% of students doing this once a week or more. The activity performed the least frequently was using simulations (11%). When students did schoolwork at home, once again browsing was the most popular activity. It’s good for students to do a certain amount of unguided browsing, but more importantly they need to be provided with principles and structures to help them perform more strategic searches.

  1. The first thing that we can do, therefore, is to raise the game for students and ensure that their time with technology is spent more productively.

It’s clear that what students do with computers makes a difference to their learning. But what students do is also related to their socio-economic background. In 2012 96% of 15-year-old students in OECD countries reported having a computer at home, so almost everyone has access to technology.

That’s not to say that the hardware divide has been completely eradicated; lower socio-economic groups are likely to have less sophisticated technology. However these older technologies are perfectly capable of supporting learning if the student knows how to use it effectively. The OECD report illustrates that what people do with media is more important than the technologies and connectivity available to them – and also more resistant to change. In their free time disadvantaged students tend to prefer online chat over e-mail, and playing video games rather than reading the news or obtaining practical information.

  1. The second thing that we can do is to focus attention on helping disadvantaged learners to use the technology available to them more effectively.

Finally, let me return to Andreas Schleichler, who states that countries need to “invest more effectively and ensure that teachers are at the forefront of designing and implementing this change.”

I would go one stage further. Researchers who work with educational technology, and for whom this blog post will hold no surprises, need to be better at communicating what their research can say both to the teachers who use the technology and the developers who build the technology. Unfortunately, (as I have observed before) research is typically conducted in isolation from technology developers. This makes little sense at a time when technology has become consumerised, even for the poorest families, and there is increasing evidence about how to make it effective as a learning tool.

  1. The third thing we can do is to create better communication channels between teachers, technology developers and researchers. Achieving this would be a ‘win win’: Improved learning, better teaching, better research.


HackEd15 computer science in the raw

Unconstrained by any exam syllabi or the curriculum our young hackers from Greig City Academy got to grips with electronics, design, programming, group work, communication and presentation in a very, very short space of time. They went from novice to accomplished in less than 48 hours. This 2 minute video summarizes their activity.

The students worked in three groups, each of which worked on solving a real world problem. One group developed a robotic guide dog called DogBot that would help blind people find their way around the world. Another built a prototype for a sensortive glove with the strap line – ‘it’s all in the hand’. The glove aimed to enable people to complete everyday activities like switching on the lights with a flick of the wrist without being near the light switch. The students had to work out how the different sensors, such as the gyroscope, worked and then write the code to interpret the data sensed into actions. The third group developed a prototype for a coin sorting device that would collect the coins dropped in the playground. Students needed to build a physical coin sorter and link it to the arduino device and sensors that detected the presence of metal.

To find out more, you can check out the tweets as compiled into a storify at: sfy.co/p0AVQ and we will be populating the HackEd15 website with more details about the event and these very short videos explain what each of the groups did.

Come Hack with us

Once again we are working with a group of teenagers to help them to instantiate their ideas using technology. This time our theme is the Internet of Things and smart cities.



The applications the students develop will be exploring the use and purpose of sensor-based applications.  The hardware and software we will be using to help with rapid prototyping and experimenting with the students’ ideas will be the arduino and sketches which are a C/C++ language (http://arduino.cc ).


The students have already been thinking about their ideas, which include

  • Connecting to being mobile:
    • From transport to life style stages e.g. waiting for the bus or walking home
  • Interacting with your environment
    • Helping the environment to be more intelligent e.g. when crossing the road – staying green for longer
  • Reading on the go:
    • Making everyday objects interactive screens
  • Smart furniture
    • Smart bicycle parks
    • Safe place detectors
    • Event alerts from water sprays, moving objects to sensing objects activities

These will be developed through the Hack event into prototype designs. The students will present their work on the main stage at the London Festival of Education.