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.

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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.

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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.

Educational AI can know about teaching, learners, and subjects like math or history.

I said in AI in Education provides smart knowledge modeling tools that AIEd systems build computational models of the process of teaching, the subject matter being studied and of the learner as they progress. The table below provides some examples of the sorts of information that can be stored in the Pedagogical model, the Domain model and the Learner model.

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To delve deeper into just one of these examples, learner models are ways of representing the interactions that happen between the computer and the learner. The interactions represented in the model (such as the student’s current activities, previous achievements, emotional state, and whether or not they followed feedback) can then be used by the domain and pedagogy components of an AIEd programme to infer the success of the learner (and teacher). The domain and pedagogy models also use this information to determine the next most appropriate interaction (learning materials or learning activities). Importantly, the learner’s activities are continually fed back into the learner model, making the model richer and more complete, and the system ‘smarter’.

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This post is an adapted extract from Intelligence Unleashed published by Pearson.

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.

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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.

Artificial Intelligence in Education provides smart knowledge modeling tools.

At the heart of AI in Education (AIEd) is the scientific goal to “make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.”  In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us deeper, and more fine-grained understandings of how learning actually happens (for example, how it is influenced by the learner’s socio-economic and physical context, or by technology). These understandings may then be applied to the development of future AIEd software and, importantly, can also inform approaches to learning that do not involve technology.

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For example, AIEd can help us see and understand the micro-steps that learners go through in learning, or the common misconceptions that arise. These understandings can then be used to good effect by classroom teachers. AI involves computer software that has been programmed to interact with the world in ways normally requiring human intelligence. This means that AI depends both on knowledge about the world, and algorithms to intelligently process that knowledge.

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This knowledge about the world is represented in so called ‘models’. There are three key models at the heart of any AIEd application: the pedagogical model, the domain model, and the learner model. Take the example of an AIEd system that is designed to provide appropriate individualised feedback to a student. Achieving this requires that the AIEd system knows something about:
• Effective approaches to teaching (which is represented in a pedagogical model);
• The subject being learned (represented in the domain model);
• The student (represented in the learner model);

In my next post I’ll provide examples of the sorts of information that can be found in each of these models.

This post is an adapted extract from Intelligence Unleashed published by Pearson.

 

AI used for Education must be driven by teachers not inflicted on teachers

Teachers need to be central agents in deciding how AI is used in education. Perhaps we should talk about EdAI rather than AIEd to make this point. It is teachers who will be the orchestrators of when, and how, to use AIEd tools. In turn, the AIEd tools, and the data driven insights that these tools provide, will empower teachers to decide how best to marshal the various resources at their disposal.

Orchestrator1More than this, though, teachers – alongside learners and parents – should be central to the design of AIEd tools, and the ways in which they are used. This participatory design methodology will ensure that the messiness of real classrooms is taken into account and that the tools deliver the support that educators need – not the support that technologists or designers think they need.

Teachers who take part in these processes will gain increased technological literacy, new design skills, and a greater understanding of what AIEd systems can offer.

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The increased introduction of AI-powered tools will serve as a catalyst for the transformation of the role of the teacher. AIEd is well placed to take on some of the tasks that we currently expect teachers to do – marking and record keeping, for example.
Freedom from routine, time-consuming tasks will allow teachers to devote more of their energies to the creative and very human acts that provide the ingenuity and empathy needed to take learning to the next level.

As this transformation takes place, teachers will need to develop new skills (maybe through professional development delivered through an AIEd system). Specifically they will need:
• A sophisticated understanding of what AIEd systems can do to enable them to evaluate new AIEd products and make a sound judgement about its value to them, and their students
• To develop research skills to allow them to interpret the data provided by AIEd technologies, to ask the most useful questions of the data, and to walk students through what the data analysis is telling them (for instance, using Open Learner models)
• New teamworking and management skills as each teacher will have AI assistants in addition to their usual human teaching assistants, and they will be responsible for combining and managing these resource most effectively

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Most excitingly, with the evolution of the teacher’s role will also come the evolution of the classroom, as AIEd tools allow us to realise what it is unrealistic to expect any teacher or lecturer to do alone. For example, making the positive impact of one-to-one tutoring available to every child, or realising effective collaborative learning (a difficult activity to keep on track without some form of additional support).

 

This post is an adapted extract from Intelligence Unleashed published by Pearson.

Will Artificial General Intelligence (AGI) cope with ‘messy’ real world learning?

I was catching up with reading my weekend papers and came across the Observer Tech Monthly profile of Demis Hassabis, the founder of DeepMind. I have never met Demis, but the Observer piece echoes the descriptions I have been given by those who do know him. He is incredibly bright and also extremely modest: a nice ‘ordinary’ North London guy. I feel comforted that someone like this is at the forefront of our efforts to extend the boundaries of Artificial Intelligence (AI) and the achievements of DeepMind are certainly impressive. However I am less convinced about the real potential of Artificial General Intelligence or AGI, especially when it comes to its application to education.

download2I can buy into the vision of a world where smart people work with smart machines to solve wicked problems, such as cancer. And I can see that there is indeed too much information for many of us humans to process, so some artificially intelligent help would be great. I like the idea that AGI will “automatically convert unstructured information into actionable knowledge…. to provide a meta-solution for any problem” But that’s where it falls down for me, I can’t believe that the structured knowledge will be applicable to any problem.

IMG_8934The reason I hold this view is twofold. Firstly, much of the knowledge that helps us negotiate our way through the world is highly contextualized. There is significant evidence that a learner’s context impacts significantly upon their learning process and that in essence each individual person has his or her own individualized learning context. Secondly, teaching and learning in the real world provides extremely messy data. It’s this very messiness in teaching and learning settings that is crucially important. Partly because one never knows if what appears to be mess is actually important for learning. For example, a disagreement between two children will probably upset at least one of them and that in turn will impact on their learning. I would need to see some clear examples of contextualized AGI (is that a contradiction in terms?) and its propensity for messy real world learning settings to be convinced that AGI for education is a way forward.

hero-learning-pathsIt’s not just DeepMind whose remarkable systems are not to my mind suitable to take over education. I was on a panel with Jose Ferreira CEO of Knewton last month and it became clear that Jose believes that Knewton is smart enough to play the role of a teacher. It certainly is impressive technology. However, Knewton relies upon ‘clean’ data and that is not what classrooms are like. To my mind the most likely outcome for AI within Education is not for AGI, but for AI components to provide teachers with a selection of smart tools that teachers can use with learners as and when they think it appropriate. It really is the smart combination of Human and Machine intelligence that will win the day.

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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.

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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

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