Showing posts sorted by relevance for query Roger schank. Sort by date Show all posts
Showing posts sorted by relevance for query Roger schank. Sort by date Show all posts

Tuesday, November 13, 2012

Roger Schank: Only two things wrong with education: 1) What we teach; 2) How we teach


I first saw Roger Schank talk in Denver, Colorado, over 25 years ago and have barely disagreed with a word he’s said since. Schank is a critic of the current educational system, pointing to 19th century curriculum, teaching by telling, lectures, memorisation and standardised tests, as structures and techniques that distort learning. I’ve seen him ask audiences of academics to tell him the quadratic equation, taught to most children – they can’t. I’ve seen him ask audiences about the safety briefing on a 737, something they’ve seen dozens of times – they can’t.
With characteristic boldness, Schank often starts with the statement, “There are only two things wrong with education: 1) What we teach; 2) How we teach it.” So let’s look at his work through these two lenses.
1. What we teach
Schank’s research took him back to the 1892 curriculum in the US, where he found that the current subjects were fossilised into a curriculum designed for testing and to filter students for university. The very idea of a fixed curriculum seems odd to Schank, as it fixes knowledge and we mostly forget the stuff we’re asked to remember.
His bĂȘte noire is ‘maths’. Our obsession with maths and standardised tests impoverishes education. In fact the two are linked. Maths is popular because it is easy to test. Driven by PISA tests, which he debunks by showing that their supposed relevance is bogus, the world has become addicted to tests not performance. Algebra, in particular, he sees as a hangover from a fossilised curriculum.
Similarly with the sciences; physics, chemistry and biology, STEM subjects, he thinks, are overrated. Sure we need to learn how to write well in English but that comes through regular practice, not occasional essays. As for languages, Roger has lived abroad and as he speaks French, he finds the French taught in school laughable, as it rarely results in any real success and is not the language spoken in France. The classroom, he claims is not the place to learn a language, especially in a country where there’s no real opportunity for immersion or practice.
In short, school he thinks, has turned into a funnelling process for Universities. This is a big mistake. His solution is to have lots of curricula and allow people to follow their curiosity and interests, as this is what drives real, meaningful and useful learning, as opposed to memorisation and hoop jumping. Organise school, not around subjects, but cognitive processes that match what we do in the real world.
Higher education?
The idea that everyone should go to college he thinks absurd. It’s fine for some but not all. With impeccable, academic credentials, and a background in cognitive science, computer science and education, he explodes the view that Higher Education has of itself, as the pinnacle of teaching competence and achievement. Professors like research and mostly see teaching and undergraduates as something to be avoided. In any case, he thinks, they’re often very poor teachers, relying on stale lecture series that teach what they research.
To cut to the quick, Schank things Higher Education is a con. You pay through the nose for not very much more than a three or four year vacation and a good social life. The courses are poor and the system designed to select researchers.
2. How we teach it
Schank has a strongly libertarian view in that he wants to abandon lectures, memorisation and tests. Start to learn by doing and practice, not theory. Stop lecturing and delivering dollops of theory. Stop building and sitting in classrooms. We need to teach cognitive processes and acquire skills through the application of these processes, not fearing failure.
What most people fail to realise about Schank is that his recommendations are based on a lifetime academic interest and contributions to cognitive science and a deep understanding of these processes.
Script theory
Based on an examination of language and memory, Schank explored the idea of personalised scripts in learning. This personalised, episodic model of memory led to a theory of instruction that exposed learners to model scripts by allowing them to experience the process of building their own scripts. We need scripts for handling meetings, dealing with customers, selling to others and so on. Knowledge is not a set of facts, it’s a set of experiences. This is not taught by telling, it is taught by doing, ‘there really is no learning without doing’. Interestingly, recent memory research confirms this view.
Learning by doing
He rejects the idea that we have to fill people up with knowledge they’ll never use. Too much education and training tries, and fails, to do this. We need to identify why someone wants to learn then teach it. In this sense he puts motivation and skills before factual knowledge. One can pull in knowledge when required.
Meaningful stories (scripts) lie at the heart of his instructional method. These contextualise learning and link to previous schema. A fierce critic of lectures and classroom education and training, he has developed simulation methods for exposing learners to script building environments, where they can learn by repeated exposure to failure and ultimately success. Expectation failure is when things turn out to be different from what you expected. This is when you learn. Breaking with traditional linguists and theorists of learning, he sees learning as a difficult and messy process, where failure is the primary driver. We match incoming problems to past experiences. Case-based reasoning is therefore instructive, where we learn by doing what we want to do. We also learn by making mistakes and reflecting on what those mistakes were and what we can do about them. Learning by doing, works. Learning by telling, doesn't.
In e-learning this means using case-based instruction, emotional impact, video, role-playing, storytelling. Learners are put into situations that seem realistic to them, to solve problems, and possibly fail, and have someone help them out. Design is hard, reworking the thing into a case-based scenario; something that seems like a goal someone has, then to helping them accomplish it - that's learning.
Story-Centred Curricula
He prefers to deliver learning from mentored experience, not from direct instruction presented out of context. Fictional situations are set up in which students must play a role. They need to produce documents, software, plans, presentations and such within a story describing the situation. Deliverables produced by the student are evaluated by team members and by mentors. The virtual experiential curricula are story centred. Story-Centred Curricula are carefully designed apprenticeship-style learning experiences in which the student encounters a planned sequence of real-world situations constructed to motivate the development and application of knowledge and skills in an integrated fashion.
Cognitive processes

In his latest book Teaching Minds: How Cognitive Science Can Save Our Schools he focuses on cognitive processes as the basis for learning interventions.

 

Conscious Processes
1. Prediction: determining what will happen next 
2. Modeling: figuring out how things work
3. Experimentation: coming to conclusions after trying things out
4. Values: deciding between things you care about 
Analytic Processes
1. Diagnosis: determining what happened from the evidence
2. Planning: determining a course of action
3. Causation: understanding why something happened
4. Judgment: deciding between choices
Social Processes
1. Influence: figuring out how to get someone else to do something that you want them to do 
2. Teamwork: getting along with others when working towards a common goal 
3. Negotiation: trading with others and completing successful deals
4. Description: communicating one’s thoughts and what has just happened to others 
These are the skills one needs to master. By allowing users to fail in controlled environments, he saw that instruction is not about telling, it’s about real or fictionally constructed experience, involvement and practice, including the experience of failure.
Online education
In fact most current online education he sees as just a change in venue, not a change in method. He argues for much more problem solving, simulation and learning by doing. He is also critical of MOOCs largelyjust lectures on line interrupted by quizzes and discussion groups” and he has little time for Coursera and Udacity, which he sees as replicating poor college courses.
Conclusion
Schank has turned most instructional methods on their head by rejecting the subject-led, academic approach for a more meaningful, experiential, learn by doing method. Using sound principles in cognitive science, he uses case-based scenarios and stories are used to create contexts in which learners succeed, and just as importantly fail. As time passes, Schank seems to become more and more relevant. He’s seen as a heretic but most of the actors in education know in themselves that he’s exposing some deep truths.
Bibliography
Schank, R.C. (1975). Conceptual Information Processing. New York: Elsevier.
Schank, R.C. (1982a). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press.
Schank, R.C. (1982b). Reading and Understanding. Hillsdale, NJ: Erlbaum.
Schank, R.C. (1986). Explanation Patterns: Understanding Mechanically and Creatively. Hillsdale, NJ: Erlbaum.
Schank, R.C. (1991). Tell Me a Story: A New Look at Real and Artificial Intelligence. New York: Simon & Schuster.
Schank, R.C. & Abelson, R. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Earlbaum Assoc.
Schank, R.C. & Cleary. C. (1995). Engines for education. Hillsdale, NJ: Erlbaum Assoc.
Schank, R.C (2005). Lessons in e-Learning. Pfeiffer.
Schank, R.C (2011). Teaching Minds: How Cognitive Science Can Save Our Schools

Sunday, April 12, 2020

Schank – Scripts and doing…

Roger Schank is a critic of the current educational system, pointing to 19th century curriculum, teaching by telling, lectures, memorisation and standardised tests, as structures and techniques that distort learning. With characteristic boldness, Schank often starts with the statement, “There are only two things wrong with education: 1) What we teach; 2) How we teach it.” So let’s look at his work through these two lenses.

1. What we teach

Schank’s research took him back to the 1892 curriculum in the US, where he found that the current subjects were fossilised into a curriculum designed for testing and to filter students for university. The very idea of a fixed curriculum seems odd to Schank, as it fixes knowledge and we mostly forget the stuff we’re asked to remember.
His bĂȘte noire is ‘maths’. Our obsession with maths and standardised tests impoverishes education. In fact the two are linked. Maths is popular because it is easy to test. Driven by PISA tests, which he debunks by showing that their supposed relevance is bogus, the world has become addicted to tests not performance. Algebra, in particular, he sees as a hangover from a fossilised curriculum.
Similarly with the sciences; physics, chemistry and biology, STEM subjects, he thinks, are overrated. Sure we need to learn how to write well in English but that comes through regular practice, not occasional essays. As for languages, Roger has lived abroad and as he speaks French, he finds the French taught in school laughable, as it rarely results in any real success and is not the language spoken in France. The classroom, he claims is not the place to learn a language, especially in a country where there’s no real opportunity for immersion or practice.
In short, school, he thinks, has turned into a funneling process for Universities. This is a big mistake. His solution is to have lots of curricula and allow people to follow their curiosity and interests, as this is what drives real, meaningful and useful learning, as opposed to memorisation and hoop jumping. Organise school, not around subjects, but cognitive processes that match what we do in the real world.

Higher education?

The idea that everyone should go to college he thinks absurd. It’s fine for some but not all. With impeccable, academic credentials, and a background in cognitive science, computer science and education, he explodes the view that Higher Education has of itself, as the pinnacle of teaching competence and achievement. Professors like research and mostly see teaching and undergraduates as something to be avoided. In any case, he thinks, they’re often very poor teachers, relying on stale lecture series that teach what they research.
To cut to the quick, Schank things Higher Education is a con. You pay through the nose for not very much more than a three or four year vacation and a good social life. The courses are poor and the system designed to select researchers.

2. How we teach it

Schank has a strongly libertarian view in that he wants to abandon lectures, memorisation and tests. Start to learn by doing and practice, not theory. Stop lecturing and delivering dollops of theory. We need to teach cognitive processes and acquire skills through the application of these processes, not fearing failure. What most people fail to realise about Schank is that his recommendations are based on a lifetime academic interest and contributions to cognitive science and a deep understanding of these processes.

Script theory

Based on an examination of language and memory, Schank explored the idea of personalised scripts in learning. This personalised, episodic model of memory led to a theory of instruction that exposed learners to model scripts by allowing them to experience the process of building their own scripts. We need scripts for handling meetings, dealing with customers, selling to others and so on. Knowledge is not a set of facts, it’s a set of experiences. This is not taught by telling, it is taught by doing, ‘there really is no learning without doing’. Interestingly, recent memory research confirms this view.

Learning by doing

He rejects the idea that we have to fill people up with knowledge they’ll never use. Too much education and training tries, and fails, to do this. We need to identify why someone wants to learn then teach it. In this sense he puts motivation and skills before factual knowledge. One can pull in knowledge when required.
Meaningful stories (scripts) lie at the heart of his instructional method. These contextualise learning and link to previous schema. A fierce critic of lectures and classroom education and training, he has developed simulation methods for exposing learners to script building environments, where they can learn by repeated exposure to failure and ultimately success. Expectation failure is when things turn out to be different from what you expected. This is when you learn. Breaking with traditional linguists and theorists of learning, he sees learning as a difficult and messy process, where failure is the primary driver. We match incoming problems to past experiences. Case-based reasoning is therefore instructive, where we learn by doing what we want to do. We also learn by making mistakes and reflecting on what those mistakes were and what we can do about them. Learning by doing, works. Learning by telling, doesn't.
In online learning this means using case-based instruction, emotional impact, video, role-playing, storytelling. Learners are put into situations that seem realistic to them, to solve problems, and possibly fail, and have someone help them out. Design is hard, reworking the thing into a case-based scenario; something that seems like a goal someone has, then to helping them accomplish it - that's learning.

Story-Centred Curricula

He prefers to deliver learning from mentored experience, not from direct instruction presented out of context. Fictional situations are set up in which students must play a role. They need to produce documents, software, plans, presentations and such within a story describing the situation. Deliverables produced by the student are evaluated by team members and by mentors. The virtual experiential curricula are story centred. Story-Centred Curricula are carefully designed apprenticeship-style learning experiences in which the student encounters a planned sequence of real-world situations constructed to motivate the development and application of knowledge and skills in an integrated fashion.

Cognitive processes

In his latest book Teaching Minds: How Cognitive Science Can Save Our Schools he focuses on cognitive processes as the basis for learning interventions.

Conscious Processes
1. Prediction: determining what will happen next 
2. Modeling: figuring out how things work
3. Experimentation: coming to conclusions after trying things out
4. Values: deciding between things you care about 
Analytic Processes
1. Diagnosis: determining what happened from the evidence
2. Planning: determining a course of action
3. Causation: understanding why something happened
4. Judgment: deciding between choices
Social Processes
1. Influence: figuring out how to get someone else to do something that you want them to do
2. Teamwork: getting along with others when working towards a common goal 
3. Negotiation: trading with others and completing successful deals
4. Description: communicating one’s thoughts and what has just happened to others
These are the skills one needs to master. By allowing users to fail in controlled environments, he saw that instruction is not about telling, it’s about real or fictionally constructed experience, involvement and practice, including the experience of failure.

Online education

In fact most current online education he sees as just a change in venue, not a change in method. He argues for much more problem solving, simulation and learning by doing. He is also critical of MOOCs largely “just lectures on line interrupted by quizzes and discussion groups” and he has little time for MOOCs, which he sees as replicating poor college courses.

Influence

Schank has turned most instructional methods on their head. He rejects the subject-led, academic approach, for a more meaningful, experiential, learn by doing method. Using sound principles in cognitive science, he uses case-based scenarios and stories are used to create contexts in which learners succeed, and just as importantly fail. As time passes, Schank seems to become more and more relevant. He’s seen as a heretic but most of the actors in education know in themselves that he’s exposing some deep truths.

Bibliography

Schank, R.C. (1975). Conceptual Information Processing. New York: Elsevier.
Schank, R.C. (1982a). Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press.
Schank, R.C. (1982b). Reading and Understanding. Hillsdale, NJ: Erlbaum.
Schank, R.C. (1986). Explanation Patterns: Understanding Mechanically and Creatively. Hillsdale, NJ: Erlbaum.
Schank, R.C. (1991). Tell Me a Story: A New Look at Real and Artificial Intelligence. New York: Simon & Schuster.
Schank, R.C. & Abelson, R. (1977). Scripts, Plans, Goals, and Understanding. Hillsdale, NJ: Earlbaum Assoc.
Schank, R.C. & Cleary. C. (1995). Engines for education. Hillsdale, NJ: Erlbaum Assoc.
Schank, R.C (2005). Lessons in e-Learning. Pfeiffer.

Sunday, December 17, 2017

10 uses for Chatbots in learning (with examples)

As chatbots become common in other contexts, such as retail, health and finance, so they will become common in learning. Education is always somewhat behind other sectors in considering and adopting technology but adopt it will. There are several points across the learner journey where bots are already being used and already a range of fascinating examples.
1.    Onboarding bot
Onboarding is notoriously fickle. New starters in at different times, have different needs and the old model of a huge dump of knowledge, documents and compliance courses is still all too common. Bots are being used to introduce new students or staff to the people, environment and purpose of the organisation. New starters have predictable questions, so answers can be provided straight to mobile, directed to people, processes or procedures, where necessary. It is not that the chatbot will provide the entire solution but it will take the pressure off and respond to real queries as they arise. Available 24/7 it can give access to answer as well as people. What better way to present your organization as innovative and responsive to the needs of students and staff?
2.    FAQ bot
In a sense Google is a chatbot. You type something in and up pops a set of ranked links. Increasingly you may even have a short list of more detailed questions you may want to ask. Straight up FAQ chatbots, with a well-defined set of answers to a predictable set of questions can take the load off customer queries, support desks or learner requests. A lot of teaching is admin and a chatbot can relieve that pressure at a very simple level within a definite domain – frequently asked questions.
3. Invisible LMS bot
At another level, the invisible LMS, fronted by a chatbot, allows people to ask for help and shifts formal courses into performance support, within the workflow. LearningPool’s ‘Otto’ is a good example. It sits on top of content, accessible from Facebook, Slack and other commonly used social tools. You get help in various forms, such as simple text, chunks of learning, people to contact and links to external resources as and when you need them. Content is no longer sits in a dead repository, waiting on you to sign in or take courses, but is a dynamic resource, available when you ask it something.
4. Learner engagement bot
Learners are often lazy. Students leave essays and assignments to the last minute, learners fail to do pre-work, and courses– it’s a human failing. They need prompting and cajoling. Learner engagement bots do this, with pushed prompts to students and responses to their queries. ‘Differ’ from Norway does precisely this. It recognizes that learners need to be engaged and helped, even pushed through the learning journey, and that is precisely what 'Differ' does.
5. Learner support bot
Campus support bots or course support bots go one stage further and provide teaching support in some detail. The idea is to take the administrative load off the shoulders of teachers and trainers. Response times to emails from faculty to students can be glacial. Learner support bots can, if trained well, respond with accurate and consistent answers quickly, 24/7.
The Georgia Tech bot Jill Watson, and its descendants, responds in seconds. Indeed they had to slow its response time down to mimic the typing speed of a human. The learners, 350 AI students, didn’t guess that it was a bot and even put it up for a teaching award.
6. Tutor bots
Tutor bots are different from chatbots in terms of the goals, which are explicitly ‘learning’ goals. They retain the qualities of a chatbot, flowing dialogue, tone of voice, exchange and human (like) but focus on the teaching of knowledge and skills. Straight up teaching is another approach, where the bot behaves like a Socratic teacher, asking sprints of questions and providing encouragement and feedback. This type of bot can be used as a supplement to existing courses to encourage engagement. Wildfire, the AI content generation service uses bots of this type to deliver actual teaching on apprenticeship content, as a supplement to courses, also built using AI, in minutes not months. Once the basic knowledge has been acquired, the bot tests the student as well as getting them to apply their knowledge.
7. Mentor bot
The point of a bot may not be to simply answer questions but to mentor learners by providing advice on how to find the information on your own, to promote problem solving. AutoMentor by Roger Schank,  is one such system, where the bot knows the context and provides, not just FAQ answers but advice. Providing answers is not always the best way to teach. At a higher-level chatbots could be used to encourage problem solving and critical skills, by being truly Socratic, acting as a midwife to the students behaviours and thoughts. Roger Schank is using these in defence-funded projects on Cyber Security.
As the dialogue gets better, drawing not only on a solid knowledge-base, good learner engagement through dialogue, focused and detailed feedback but also critical thought in terms of opening up perspectives, encouraging questioning of assumptions, veracity of sources and other aspects of perspectival thought, so critical thinking could also be possible. Bots will be able to analyse text to expose factual, structural or logical weaknesses. The absence of critical thought will be identified as well as suggestions for improving this skill by prompting further research ideas, sound sources and other avenues of thought. This ‘bot as critical companion’ is an interesting line of development.
8. Scenario-based bots
Beyond knowledge, we have the teaching and learning of more sophisticated scenarios, where knowledge can be applied. This is often absent in education, where almost all the effort is put into knowledge acquisition. It is easy to see why – it’s hard and time consuming. Bots can set up problems, prompt through a process, provide feedback and assess effort. Scenarios often involve other people this is where surrogate bots can come in.
9. Practice bots
Practice bots, literally take the role of a customer, patient, learners or any other person and allows learners to practice their customer care, support, healthcare or other soft skills on a responding person (bot). Bots that act as revision bots for exams are also possible.
A bot that mimics someone can be used for practice. For example, the boy with attitude ‘Eli’, developed by Penn State, that mimics an awkward child in the classroom. It is used by student teachers to practice their skills on dealing with such problems before they hit the classroom. Duolingo uses bots after you have gathered an adequate vocabulary, knowledge of grammar and basic competence, to allow practice in a language. This surely makes sense.
10. Wellbeing bots
If a bot is being used in any therapeutic context, its anonymity can be an advantage. From Eliza in the 60s to contemporary therapeutic bots, this has been a rich vein of bot development. There is an example of the word ‘suicidal’ appearing in a student messenger dialogue, that led to a fast intervention, as the student was in real distress. Therapeutic bots are being used in controlled studies to see of they have a beneficial effect on outcomes. Anonymity, in itself, is an advantage in such bots, as the learner may not want to expose their failings.
Bots such as ‘Elli ‘ and ‘Woebot’ are already being subjected to controlled trials to examine the impact on clinical outcomes.
Bot warning
The holy grail in AI is to find generic algorithms that can be used (especially in machine learning) to solve a range of different problems across a number of different domains. This is starting to happen with deep learning (machine learning). The idea is that the teacher bot will replace the skills of a teacher, not just be able to tutor in one subject alone, but be a cross-curricular teacher, especially at the higher levels of learning. It could be cross-departmental, cross-subject and cross-cultural, to produce teaching and learning that will be free from the tyranny of the institution, department, subject or culture in which it is bound. Let’s be clear, this will not happen any time soon.  AI is nowhere near solving the complex problems that this entails. If someone is promising a bot will replace a teacher – show them the door. Bots will augment not automate teaching.
We have to be careful about overreach here. Effective bots are not easy to build, have to be ‘trained (in AI-speak ‘unsupervised’) and are difficult to build. On the other hand trained bots, with good data sets (in AI-speak ‘supervised’), in specific domains, are eminently possible. Another warning is that they are on a collision course with traditional Learning Management Systems, as they usually need a dynamic server-side infrastructure. As for SCORM – the sooner it’s binned the better. Bots fit n more naturally into the xAPI landscape.
Conclusion
Chatbots have real potential in a number of learning activities, all along the learning journey, not as a general; ‘teacher’ but in specific applications within specific domains. They need to be trained, built, tested and improved, which is no easy task, but their efficacy in reducing the workload of teachers, trainers, lecturers and administrators is clear. The dramatic advances in Natural Language Processing have led to Siri, Amazon Echo and Google Home. It is a rapidly developing field of AI and promises to deliver chatbot technology that is better and cheaper by the month.
As a bot does not have the limitations of a human, in terms of forgetting, recall, cognitive bias, cognitive overload, getting ill, sleeping 8 hours a day, retiring and dying - once on the way to acquiring, albeit limited, skills, it will only get better and better. The more students that use its service the better it gets, not only on what it teaches but how it teaches. Courses will be fine-tuned to eliminate weaknesses, and finesse themselves to produce better outcomes.
We have seen how online behaviour has moved from flat page-turning (websites) to posting (Facebook, Twitter) to messaging (Txting, Messenger). We have seen how the web become more natural and human. As interfaces (using AI) have become more frictionless and invisible, conforming to our natural form of communication (dialogue), through text or speech. The web has become more human.
Learning takes effort. Personalised dialogue reframes learning as an exploratory, yet still structured process where the teacher guides and the learner has to make the effort. Taking the friction and cognitive load of the interface out of the equation, means the teacher and learner can focus on the task and effort needed to acquire knowledge and skills. This is the promise of bots. But the process of adoption will be gradual.

Finally, this at last is a form of technology that teachers can appreciate, as it truly tries to improve on what they already do. It takes good teaching as its standard and tries to support and streamline it to produce faster and better outcomes at a lower cost. It takes the admin and pain out of teaching. They are here, more are coming.

Tuesday, October 09, 2012

Calculators: Education stuck in pre-calculator age


Archaeological evidence for an abacus goes back to 5th century BC Greece, however, there is indirect evidence of their use in Mesopotamia, Egypt and Persia. It is still widely used in Asia. The humble electronic calculator was the first computer to impact teaching and learning. It quickly replaced mechanical slide rules and mechanical calculators in the 1970s. Calculators now include scientific, algebraic, trigonometric  and graphing functions.
Education is still stuck in pre-calculator age
Everyone’s miserable about maths: employers, politicians, teachers and especially learners, many who fail and hate the subject with a passion. Indeed, governments have become obsessed with the subject, largely on the hysteria surrounding the PISA rankings.
One issue that is receiving intense attention is ‘calculation’, which is kicking up a storm in maths education. The ubiquity of calculators has led some to question the way we teach maths in schools. They claim that the world has changed from analogue to digital and the teaching of maths needs to respond accordingly.
Some argue that calculators have led to a reduction in numeracy and maths skills. They recommend not using calculators in schools until a certain level of competence in mental arithmetic is reached. Others argue that the traditional focus on ‘calculation’ needs to be replaced by a more sophisticated curriculum of solving problems using maths. Why teach long division, when you are unlikely to ever use it in real life? Calculators can also be used to do the necessary calculation spadework on algebra, trigonometry and graphics.
Maths need exaggerated
Some, like Roger Schank, believe that the need to learn maths is grossly exaggerated as only a tiny proportion of adults will use the maths that is taught, beyond basic arithmetic. His point is that most of what is taught, especially algebra, is of no real practical use and does not help people to think logically. He often asks highly educated audiences to tell him the quadratic formula – few ever answer. Sure, some will need maths in their later career, so says Roger, let them learn it later. Roger has traced this obsession with maths back to early 19th century curriculum choices and claims that this is a historical problem, fuelled by the fact that maths is easy to test, especially ‘calculation’
Too much calculation
Conrad Wolfram decries the focus on ‘calculation’ in school maths. We spend most of our time teaching calculations by hand, which any calculator and computer can do better than any human. Practical, mental arithmetic is fine, but what are these numeracy basics? Automation pushes the user towards using the tools in more sophisticated ways. Maths is not calculation and over the last thirty years calculation has been automated by calculators. Education is still stuck in a pre-calculator age.
Far better to understand what you’re trying to achieve. He recommends that programming is a better way to do maths. It makes maths more practical and academic at the same time. He goes further and argues that the obsession with calculation in maths kills off the initiative, intuition and perseverance that maths needs. In other words we’re turned off maths by maths. Students learn to look for and apply formula, which they then proceed to calculate. Text books are full of primitive, dry, exercises that seem like chores. Many now argue that real life problems should stimulate mathematical enquiry through the use of more word based problems.
Calculators and computers
A calculator is pretty standard as a native application on PCs, Macs and mobile devices. Tills automatically calculate the correct change for customers. Calculators are therefore embedded in newer forms of technology making them more readily available. This is one potential use of mobile devices in schools that teachers should consider.
Conclusion
Maths is forced, by law, upon people who see it as lacking relevance and don’t want to learn it, taught by people who, because they’re good at maths, often don’t know how to teach it. Yet the curriculum is aimed, largely at those very few who will use high-level maths professionally.

Tuesday, June 13, 2017

10 recommendations on HE from OEB Mid-Summit (Reykjavik)

Iceland was a canny choice for a Summit. Literally in sight of the house where Reagan and Gorbachov met in 1986 (Berlin Wall fell in 1989), it was a deep, at times edgy, dive into the future of education. When people get together and face up to rifts in opinion and talk it through – as the Reagan-Gorbachov summit showed, things happen – well maybe. Here's my ten takeaways from the event (personal).
1. Haves-have nots
First the place. Iceland has eemerged up through the Mid-Atlantic Ridge, which still runs right through the middle. Sure enough, while here, there were political rifts in the US, with the Coney-Trump farrago, and a divisive election in the UK. It is clear that an economic policies have caused fractures between the haves and the have-nots. In the UK there’s a hung Parliament, country split, Brexit negotiations loom and crisis in Northern Ireland. In the US Trump rode into Washington on a wave of disaffection and is causing chaos.
But let’s not imagine that Higher Education lies above all of this. Similar fault lines emerged at this Summit. As Peter Thiel said, Higher Education is like the Catholic Church on eve of Reformation, “a priestly class of professors….people buying indulgences in the form of amassing enormous debt for the sort of the secular salvation that a diploma represents”. More significantly he claims there has been a ‘failure of the imagination, a failure to consider alternative futures’. Culture continues to trump strategy.
Higher Education is a valuable feature of cultural life but people are having doubts. Has it become obese? Why have costs ballooned while delivering the same experience? There are problems around costs, quality of teaching and relevance. Indeed, could Higher Education be generating social inequalities? In the US and UK there was a perception, not without truth, that there is a gulf between an urban, economically stable, educated elite and the rest, who have been left to drift into low status jobs and a loss of hope for their children. The Federal debt held on student loans in the US has topped 1.5 trillion. In the UK, institutions simply raise fees to whatever cap they can. The building goes on, costs escalate and students loans get bigger. Unlike almost every other area of human endeavor, it seems there has been little effort to reduce costs and look for cost-effective solutions.
Recommendation: HE must lower its costs and scale
2. Developed-developing
The idea that the current Higher Education model should be applied to the developing world is odd, as it doesn’t seem to work that well in the developed world. Rising costs, student and/or government debts, dated pedagogy and an imbalance between the academic and vocational, renders application in the developing world at best difficult, at worse dangerous. I have been involved in this debate and it is clear that the developing world needs vocational first, academic second.
Recommendation: Develop different and digital HE model for developing world
3. Public-private
In an odd session by Audrey Watters, we had a rehash of one of her blogs, about personalized learning being part of the recent rise in ‘populism’. She blamed ‘capitalism’ for everything, seeing ‘ideology’ everywhere. But as one brave participant shouted behind me “so your position is free from ideology then?” It was the most disturbing session I heard, as it confirmed my view that the liberal elite are somewhat out of touch with reality and all too ready to trot out old leftist tropes about capitalism and ideology, without any real solutions. The one question, from the excellent Valerie Hannon, stated quite simply, that she was “throwing the baby out with the bath water”. Underlying much of the debate at the summit lay an inconvenient truth that Higher Ed has a widespread and deep anti-corporate culture. This means that the public and private sectors talk past, and not to, each other. This is a real problem in EdTech. Until we start talking to each other, like Reagan and Gorbachov, this wall will not fall.
Recommendation: Stop talking past each other, talk to each other
4. Research-practice
Session after session laid out established and recent research in cognitive psychology and educational research, which showed the redundancy of the lecture as a core pedagogic principle. Data was shown of shockingly low attendance in lectures from both the US and the UK. The illusion that Higher Ed teaches critical thinking was also exposed by Ben Nelson (critical thinking by the way isn’t really a thing in itself). Arun’s study in Academically Adrift, of 2332 students, in 23 institutions, over 4 years, showed a worrying lack of success in critical thinking, complex reasoning and written communication. Harold Beckering gave a brilliant talk on how we learn through the correction of errors, yet teaching methods fail to recognize this core cognitive fact. Roger Schank eviscerated current pedagogy with its lazy obsessions with lectures, and marking. Think of parents, he pleaded, did you ever give your kid a test or mark them?
Recommendation: Don’t lecture me!
5. Teaching v research
Astin’s study of 24,847 students, in 309 institutions, looked at the correlation between ‘faculty orientation towards research’ and ‘student/teaching orientation’ and found them to be strongly negatively correlated. Student orientation was also negatively related to compensation, with a significant institutional price to be paid, in terms of student development, for a very strong faculty emphasis on research”. This should come as no surprise. Research skills require systematic thinking, attention to detail, understanding of methods and analysis. Teaching skills require social skills, communication skills, the ability to hold an audience, keep to the right level, avoid cognitive overload, good pedagogic skills and the ability to deliver constructive feedback. An additional problem being the exponential growth of Journals and, some would say, 2nd and 3rd rate research. The swing away from teaching towards research over the lasy 6o years has been well documented by Jencks, Boyer, Massy and Bok.
Recommendation: Research is not a necessary condition for teaching – break the link
6. Building v online
Most campuses look as though they’ve been built by committee, often a rather ugly assembly of disparate buildings – that’s because they have been built departmentally. The architecture reflects the fractured, departmental nature of the organisation. Encouraged by endowments, where alumni want their name, if not in lights, in concrete – the building goes on. Yet the occupancy rates of University buildings shows an appalling return on investment. At the same time there is often a small and tactical approach to online delivery. It is perhaps time to consider, what John Daniel called, a ‘default to digital’ for some courses.
Recommendation: Build less. Balance out the capital budget with a substantial digital budget
7. Inside-outside
HE is unlikely to change from inside, as culture trumps strategy. Substantial, strategic change  - online courses, rebalancing academic/vocational, pedagogic and technology shifts are more likely to come from outside of academe, influence and action through political policies, technological shifts, new models such as MOOCs/online courses and use of technology by students. Sure there’s some good and real change happening within HE but they tend to be, and remain, outliers. The core system is in stasis.
Recommendation: Open up to outside, not just with technology but culturally
8. Tech v anti-tech
In technology, AI was the hot topic, and rightly so. I gave a session devoted to its application in learning, others also, and it was a recurring theme. To be honest, AI is not really the right phrase, let’s just call it smart software. We had a marvelous talk from Nell Watson on the transformative nature of machine learning, another from Valerie Hannon making a similar point about the complexity of the problems we face and the need for smart, technological solutions in education. Peter O’Driscoll also showed how tech ‘jerks’ people around in institutions but rather than retreat into culturally safe, luddite shelters, we need to embrace the technology to do good.
Recommendation: Embrace transformative technology
9. Culture v strategy
Culture trumps strategy. Budgets, chasing ratings, quality systems, building programmes, obsession with lectures, research-driven teaching, an anti-corporate, internal-looking culture always trumps strategy. Change management (planned and executed) is the way to go and we can learn a lot about how this is done in the outside world – not by writing reports but by creating a sense of urgency and sustained action. No matter how many summits, reports and horizon scans we have – ‘the best way to predict the future is to create it’ (Alan Kay). That means recognising the issues and taking a strategic approach to solutions.
Recommendation: Strategic, costed initiatives with change management
10. Academic v vocational
There’s always been a tension between these two but the pendulum may have swung way too far towards the academic. Roger Schank and I made passionate pleas for more learning by doing and more apprenticeships. It’s no accident that Germany is Europe’s strongest economy – they have balance in their educational system. Guess what happens – within 48 hours Trump issues a major policy announcement recommending precisely this. We’ve already done this in the UK with 0.5% of payroll (by law) going towards apprenticeships.
Recommendation: Rebalance academic and vocational
Conclusion

As if by magic, which of course it is not, within 48 hours of our Summit, there was a major briefing from the White House about building skills and apprenticeships, exactly what Roger and I had been talking about. (There is a link which will be revealed later.) It’s a pity that it’s taken a Trump to get this going – but hey – I don’t care where it comes from – good policy is good policy. It is an example of what I was talking about. Paraphrasing Alan Jay, we can take the future into our own hands or let it just happen.