Monday, November 20, 2017

Janesville - a town that explains Trump and also why you shouldn't judge or blame people for being poor

Want to know why working America is pissed and rolling with Trump? Read this book. Told with compassion but realism, through the lives of real people in a real town.

Here's a town that implodes when the car plant closes down and 9000 people lose their jobs. GM was a mess – incompetent management, old models, a company that failed to innovate. As if that wasn’t enough Janesville is hit with Biblical levels of rain (climate change?). Journalism at its best, by a Poulitzer-winning writer, Amy Goldstein, written from the perspective of the people affected. 

For over 100 years they had produced tractors, pick-ups, trucks, artillery shells and cars. Obama came and went, the financial crisis hammered them deeper into the dirt but while the banks were bailed by the state, the state bailed on the people. On top of this a second large, local employer, Parker Pens, outsourced to Mexico but the market for upmarket pens was also dying. The ignominy of being asked to extend your wages by a few weeks to go down to Mexico to train their cheaper labour was downright evil.

Then the adjunct businesses started to fail, the suppliers, trades, shops, restaurants, nurseries – then the mortgage and rent arrears, foreclosures, house prices fall and negative equity. As middle-class jobs disappear, they push down on working class jobs and the poor get even poorer.

Family is more important than GM” this is the line that resonated most with me in the book. In this age of identity politics, most people still see a stable family and their community as their backstops. The left and right have lost focus on this. The community didn’t lie down – they fought for grants, did lots themselves to raise money, help each other – but it was not enough. Grants for retraining were badly targeted, training people for reinvention is difficult with monolithic, manufacturing workforces. Some of it was clearly hopeless, like discredited Learning Style diagnosis, overlong courses of limited relevance to the workplace or practice. Problems included the fact that many couldn’t use computers, so there was huge drop out, more debts and little in the way of workplace learning. Those that did full degrees found that what few jobs there were had been snapped up while they were in college – their wages dropped the most, by nearly half. One thing did surprise me, a curious offshoot that was anti-teacher hostility. People felt let down by a system that doesn’t really seem to work and saw teachers as having great holidays, pensions and healthcare, while they were thrown out of work. Teachers and trainers were perceived as benefitting from their misfortune. The whole separation of educational institutions from workplaces seems odd.

Jobs didn’t materialise. What jobs there were, existed in the public sector – in welfare charities and jails. A start-up provided few jobs, many commuted huge distances to distant factories. Even for those in work, there was a massive squeeze on wages, in some cases a 50% cut, sometimes more. In the end jobs came back but real wages fell. Healthcare starts to become a stretch. But it is the shame of poverty, using food banks, homeless teenagers and a real-life tragedy that unfolds that really shocks you.

The book ends with the divide between the winners and losers. This is the divide that has shattered America. Janesville is the bit of America tourists, along with East and West coast liberals, don’t see. The precariat are good people who are having bad things done to them by a system that shoves money upwards into the pockets of the rich. Looked down upon by Liberals, they are losing faith in politics, employers, the media, even education.

Wisconsin turned Republican and Trump was elected. The economist Mark Blyth attributes the Trump win to their wages squeeze and fall in expectations, even hope. People got a whole lot poorer and don’t see a great future for their kids.

A more relevant piece of work than Hillbilly Elegy, with which it is being compared. Final thought –why are journalists in the UK not doing this? Answer – they’re bubble-wrapped in their cozy London lairs, part of the problem and too lazy to get out and do their jobs… writing the same stories about why they don’t like social media, failing to see that they are the purveyors, not so much of fake new but inauthentic news, irrelevant news, news reduced to reporting on shadows within their own epistemological cave…

Saturday, November 18, 2017

Jared Lanier: Dawn of the New Everything: A Journey Through Virtual Reality

As a fan of VR I was looking forward to this book. Lanier is often touted as the inventor, father or, more realistically, the guy who name up with the phrase ‘Virtual Reality’. I’m not sure that any of this is true, and to be fair, he says as much late in the book. The most curious thing about the book is how uninteresting it is on VR – it’s core subject. Lot’s on the early failed stuff, and endless musings on early tech folk, but little that is truly enlightening about contemporary VR.
My problem is that it’s overwritten. No, let me rephrase that, it’s self indulgently overwritten. I’ve always liked his aperçus, little insights that make you look at technology from another perspective, such as ‘Digital Maoism’ and ‘Micropayments’ but this is an over-long ramble through an often not very interesting landscape. He has for many years been a gadfly for the big tech companies but the book is written from within that same Silicon Valley bubble. Critical of how Silicon Valley has turned out he's writing for the folk that like this worls and want to feel it's earlypulse.
He finds it difficult to move out of that bubble. I’m with him on the ridiculous Kurweil utopianism but when Lanier moves out into philosophy, or areas such as AI, it’s all a bit hippy dippy. On AI there’s a rather ridiculous attempt at a sort of Platonic dialogue that starts with a category mistake VR = -AI. No – they are two entirely different things, albeit with connections. Although interesting to describe AI as a religion (some truth in this) as it has it has transhuman aspects, it’s a superficially clever comment without any accompanying depth of analysis.

I was disappointed. You Are Not A Gadget was an enlightening book, this is a bit of a shambles.

Sunday, November 12, 2017

7 ways to use AI to massively reduce costs in the NHS

I once met Tony Blair and asked him “Why are you not using technology in learning and health to free it up for everyone, anyplace, anytime?” He replied with an anecdote, “I was in a training centre for the unemployed and did an online module – which I failed. The guy next to me also failed, so I said ‘Don’t worry, it’s OK to fail, you always get another chance…. To which the unemployed man said 'I’m not worried about me failing, I’m unemployed – you’re the Prime Minister!” It was his way of fobbing me off.

Nevertheless, 25 years later, he publishes this solid document on the use of technology in policy, especially education and health. It’s full of sound ideas around raising our game through the current wave of AI technology. It forms the basis for a rethink around policy, even the way policy is formulated, through increased engagement with those who are disaffected and direct democracy. Above all, it offers concrete ideas in education, health and a new social contract with the tech giants to move the UK forward.

In healthcare, given the challenges of a rising and ageing population, the focus should be on increasing productivity in the NHS. To see all solutions in terms of increasing spend is to stumble  blindly onto a never-ending escalator of increasing costs. Increasing spend does not necessarily increase productivity, it can, in some cases, decrease productivity. The one thing that can fit the bill, without inflating the bill, is technology, AI in particular. So how can AI can increase productivity in healthcare:

1. Prevention
2. Presentation
3. Investigation
4. Diagnosis
5. Treatment
6. Care
7. Training

1. Prevention
Personal devices have taken data gathering down to the level of the individual. It wasn’t long ago that we knew far more about our car than our own bodies. Now we can measure signs, critically, across time. Lifestyle changes can have a significant effect on the big killers, heart disease, cancer and diabetes. Nudge devices, providing the individual with data on lifestyle – especially exercise and diet, is now possible. Linked to personal accounts online, personalised prevention could do exactly what Amazon and Netflix do by nudging patients towards desired outcomes. In addition targeted AI-driven advertising campaigns could also have an effect. Public health initiatives should be digital by default.

2. Presentation
Accident and Emergency can quickly turn in to a war zone, especially when General Practice becomes difficult to access. This pushes up costs. The trick is to lower demand and costs at the front end, in General Practice. First, GPs must adopt technology such as email, texting and Skype for selected
patients. There is a double dividend here, as this increases productivity at work, as millions need not take time off work to travel to a clinic, sit in a waiting room and get back home or to work. This is a particular problem for the disabled, mentally ill and those that live far from a surgery. Remote consultation also means less need for expensive real estate – especially in cities. Several components of presentation are now possible online; talking to the patient, visual examination, even high definition images from mobile for dermatological investigation. As personal medical kits become available, more data can be gathered on symptoms and signs. Trials show patients love it and successful services are already being offered in the private sector.

Beyond the simple GP visit, lies a much bigger prize. I worked with Alan Langlands, the CEO of the NHS, the man who implemented NHS Direct. He was adamant that a massive expansion of NHS Direct was needed but commented that they were too risk averse to make that expansion possible. He was right and now that these risks have fallen, and the automation of diagnostic techniques has risen, the time is right for such an expansion. Chatbots, driven by precise, discovery techniques, can start to do what even Doctors can’t, do preliminary diagnosis at any time 24/7, efficiently and in some areas, more accurately, than most Doctors. Progress is being made here, AI already has successes under its belt and progress will accelerate.

3. Investigation
Technology is what speeds up the bulk of investigative techniques; blood tests, urine tests, tissue pathology, reading of scans and other standars tests, have all benefited from technology. In pathology, looking at tissues under a microscope is how most cancer diagnosis takes place. Observer variability will always be a problem but image analysis algorithms are already doing a good job here. Digitising slides, and scans also means the death of distance. Faster and more accurate investigation is now possible. Digital pathology and radiology, using data and machine learning, is the future. If you need convincing further look at this famous test for radiologists.

4. Diagnosis
AI already outperforms Doctors in some areas, matches them in others and it is clear that progress will be rapid in others. Esteva et al. in Nature (2017) describes an AI system trained on a data set of 129,450 clinical images of 2,032 different diseases compared its diagnostic performance to 21 board-certified dermatologists. The AI system classified skin cancer at a level of competence comparable to the dermatologists. This does not means that Doctors will disappear but it does mean they, and other health professionals, will have less workload and be able to focus more on the emotional needs of their patients. Lots of symptoms are relatively undifferentiated, some conditions rare and probability-based reasoning is often beyond that of the brain of the clinician. AI technology, and machine learning, offers a way forward from this natural, rate-limiting step. We must accept that this is the way forward.

5. Treatment
Robot pharmacies already select and package prescriptions. They are safer and more accurate than humans. Wearable technology can provide treatment for many conditions, as can technology provided for the patient at home. Repeat prescriptions and on-going treatment could certainly be better managed by GPs and pharmacists online, further reducing workload and pressure on patients time. Above all patient data could be used for more effective treatment and a vast reduction in waste through over-prescribing.
Treatment in hospitals through automated robots, such as TUG, are already delivering medication, food and test samples, reducing the humdrum tasks that health professionals have to do, day in, day out. Really a self-driving car, it negotiates hospital corridors, even lifts, using lasers and internally built AI maps. The online management of treatement regimes would increase complaince to those regimes and save costs.

6. Care
Health and social care are intertwined. Much attention has been given to robots in social care but it is  AI-driven personalized care plans and decision support for care workers along with more self-care that holds most promise and is already being trialed. AI will help the elderly stay at home longer by providing detailed support. AI also gives support to carers. It may also, through VR and AR, provide some interesting applications in autism, ADHD, PTSD, phobias, frailty and dementia.

7. Medical education
Huge sums are spent on largely inefficient medical training. There are immense amounts of duplication in the design and delivery of courses. AI created content can create high quality, high-retention content in minutes not months (WildFire). Adaptive, personalized learning gets us out of the trap of batched, one size fits all courses. On-demand courses can be delivered and online assessments, now possible with AI-driven digital identification, keystroke tests and automated marking make assessment easier. Healthcare must get out of the ‘hire a room with round tables, a flipchart and PowerPoint (often awful)’ approach to training. The one body that is trying here is HEE with their E-learing For Health initiative. Online learning can truly reduce costs, increase knowledge and skills at a much lower cost.

Conclusion
It is now clear that AI can alleviate clinical workload, speed up doctor-patient interaction, speed up investigation, improve diagnosis and provide cheaper treatment options, as well as lower the cost of medical training. We have a single, public institution, the NHS, where, with some political foresight, a policy around the accelerated research and application of AI in healthcare could help alleviate the growing burden of healthcare. Europe has 7% of the world’s population, 25% of its wealth and 50% of its welfare spending, so simply spending more on labour is not the solution. We need to give more support to healthcare professionals to make them more effective by taking away the mundane sides of their jobs through AI, automation and data analysis.

Friday, November 03, 2017

EdTech – all ‘tech’ and no ‘ed’ – why it leads to mosquito projects that die….

‘EdTech’ is one of those words that make me squirm, even though I’ve spent 35 years running, advising, raising investments, blogging and speaking in this space. Sure it gives the veneer of high-tech, silicon-valley thinking, that attracts investment… but it’s the wrong word. It skews the market towards convoluted mosquito projects that quickly die. Let me explain why.
Ignores huge part of market
Technology or computer based learning long pre-dated the term EdTech. In fact the computer-based learning industry cut its teeth, not in ‘education’, but in corporate based training. This is where the big LMSs developed, where e-learning, scenario-based learning and simulation grew. The ‘Ed’ in ‘Ed-tech’ suggests that ‘education’ is where all the action and innovation sits – which is far from true.
Skews investment
The word EdTech also skews investment. Angels, VCs, incubators, accelerators and funds talk about EdTech in the sense of schools and Universities – yet these are two of the most difficult, and unpredictable, markets in learning. Schools are nationally defined through regulation, curricula and accreditation. They are difficult to sell to as they have relatively low budgets. Universities are as difficult, with a strong anti-corporate ethos and difficult selling environment. EdTech wrongly shifts the centre of gravity away from learning towards ‘schooling’.
Not innovative
I’m tired of seeing childish and, to be honest badly designed, ‘game apps’ in learning. It’s the first port of call for the people who are all ‘tech’ and no ‘ed’. It wouldn’t be so bad if they really were games' players or games' designers but most are outsiders who end up making poor games that no one plays. Or yet another ‘social’ platform falling for the old social constructivist argument that people only learn in social environments. EdTech in this sense is far from innovative; it’s innocuous, even inane. Innovation is only innovation if it is sustainable. EdTech has far too many unsustainable models – fads dressed up as learning tools and services.
Mosquitos not turtles
Let’s start with a distinction. First, there’s what I call MOSQUITO projects, that sound buzzy but lack leadership, real substance, scalability and sustainability. They’re short-lived, and often die as soon as the funding runs out or paper/report is published. These are your EU projects, many grant projects…. Then there’s TURTLES, sometimes duller but with substance, scalability and sustainability, and they’re long-lived. These are the businesses or services/tools that thrive.
Crossing that famous chasm from mosquito to turtle requires some characteristics that are often missing in seed investment and public sector funding in the education market. Too many projects fail to cross the chasm as they lack the four Ss.:
Senior management team
Sales and marketing
Scalability
Sustainability
There are two dangers here. First, understimulating the market so that the mosquito projects fall into the gap as they fail to find customers and revenues. This is rarely to do with a lack of technical or coding skills but far more often a paucity of management, sales and marketing skills. There’s another danger and that’s bogging projects down in overlong academic research, where one must go at the glacial speed of the academic year and ponderous evaluation, and not the market. These projects lose momentum, focus and, in any case, no one pays much attention to the results. As the old saying goes, “When you want to move a graveyard, don’t expect much help from the occupants.
Either way a serious problem is the lack of strategic thinking and a coherent set of sales and marketing actions. When people think of ‘scale’ they think of technical scale, but that goes without saying on the web, it’s a given. What projects need is market scale. What is your addressable market? This is why the ‘schools’ market is so awful. Where are the budgets? Who are the buyers? Who will you actually sell to? How big is the market? Do you realise that Scotland has a different curriculum? What market share do you expect? Who are your competitors? Answer these questions and you may very well decide to find a proper job.
Conclusion
Education is not necessarily where it’s all at in the learning market. Neither is that, now rather dated, culture of wokplaces with pool tables, dart boards in offices full of primary colours, that look more like playschool than tech startup. We spend only a fraction of our lives in school, less in college and most of it in work. The corporate training and apprenticeship markets have more headroom, offer more room for innovation and have sustainable budgets and revenues.


Wednesday, November 01, 2017

Reasons to abandon multiple-choice questions

Long the staple of e-learning and a huge range of low and high stakes tests, the MCQ should be laid to rest or at least used sparingly. Their huge popularity has been due to several factors. First they are an artefact, a hangover from cardboard templates placed over squares to identify 'Xs". The tools for online learning also make them easy to write. This does not make it easy to write them well, in the same way that giving someone Word doesn't make them a novelist or poet.

But it has several flaws…

1. Probability
25% of getting it right on the standard four-option item, makes it less than taxing. True false, really a two option MCQ, is of course worse. I say 25% by research but Rodriguez (2005) shows that three options are optimal, then it goes to 33%.

2. Unreal
You rarely, if ever in the real world, have to choose things from short lists. This makes the test item somewhat odd and artificial, disassociated from reality.  They seem dissonant, as this is not the way our brains work (we don't cognitively select from four item lists in recall or automaticity). They are also weak on recall and therefore weak on the transfer of knowledge to the real world.

3. Distractors distract
It is too easy to remember the distractor, as opposed to the right answer. The fact that they are designed to distract makes them candidates for retention and so MCQs can become counterproductive. The research shows that four option MCQs often contain spurious and extraneous distractors that just add cognitive load and the chance of false recall.

4. Can be cheated
Pick longest item, second-guess the designer. Look for opposites and internal logic of distractor options. There are credible cheat-lists for multiple choice – Poundstone’s research shows that these approaches increase your chance of getting better scores. (20 cheats here)

5. Surface misleading
Take these two questions.
What is the Capital of Lithuania? Tallin, Vilnius, Riga. Minsk
What is the Capital of Lithuania? Berlin, Vilnius, Warsaw, Helsinki
Surface differences in options make these very different test items. And it is easy to introduce these surface differences, reducing the validity of the test items and test. MCQs often have huge variances depending on the options presented.

6. Difficult to write
I have written a ton of MCQs over 35 years – believe me they are seriously difficult to write. It is easy to select the noun from the text and come up with three other nouns. What is difficult it to test, is real understanding.

7. Little effort
This is the big one. As Roediger and McDaniel state in their book Make It Stick, choosing from a list requires little cognitive effort. You choose from a limited set of options, and do not use effortful recall (which is in itself increases retention).

Conclusion
Multiple-choice is not a terrible test item but it has had its day as the primary test item in online learning. We’re still designing test items in lock-step because the tools encourage us to do so, ignoring morepowerful open-response questions that require recall and the powerful act of writing/typing, which in itself, reinforces learning. New tools, such as WildFire, the AI-driven content creation service, focuses on open-response and effortful learning, for all of these seven reasons and more. The learner has to make more effort as that means deeper processing and higher retention.

Tuesday, October 24, 2017

Kirkpatrick evaluation: kill it - happy sheet nonsense, well past its sell-by-date

Kirkpatrick has for decades been the only game in town in the evaluation of corporate training, although hardly known in education. In his early Techniques for evaluation training programmes (1959) and Evaluating training programmes: The four levels (1994), he proposed a standard approach to the evaluation of training that became a de facto standard. It is a simple and sensible schema but has not stood the test of time. First up - what are the Kirkpatrick's four levels of evaluation?
Four levels of evaluation
Level 1 Reaction
At reaction level one asks learners, usually through ‘happy sheets’ to comment on the adequacy of the training, the approach and perceived relevance. The goal at this stage is to simply identify glaring problems. It is not, to determine whether the training worked.
Level 2 Learning
The learning level is more formal, requiring a pre- and post-tests. This allows you to identify those who had existing knowledge, as well as those at the end who missed key learning points. It is designed to determine whether the learners actually acquired the identified knowledge and skills.
Level 3 Behaviour
At the behavioural level, you measure the transfer of the learning to the job. This may need a mix of questionnaires and interviews with the learners, their peers and their managers. Observation of the trainee on the job is also often necessary. It can include an immediate evaluation after the training and a follow-up after a couple of months. 
Level 4 Results
The results level looks at improvement in the organisation. This can take the form of a return on investment (ROI) evaluation. The costs, benefits and payback period are fully evaluated in relation to the training deliverables. 
JJ Phillips has argued for the addition of a separate, fifth, "Return on Investment (ROI)” level which is essentially about comparing the fourth level of the standard model to the overall costs of training. However, it is not that ROI is a separate level as it can be included in Level 4. Kaufman has argued that it is merely another internal measure and that if there were a fifth level it should be external validation from clients, customers and society. In fact there have been other evalutaion methods with even more levels, completely over-engineering the solution.
Criticism
Level 1 - keep 'em happy
Traci Sitzmann’s meta-studies (68,245 trainees, 354 research reports) ask ‘Do satisfied students learn more than dissatisfied students?’ and ’Are self-assessments of knowledge accurate?’ Self-assessment is only moderately related to learning. Self-assessment captures motivation and satisfaction, not actual knowledge levels.She recommends that self-assessments should NOT be included in course evaluations and should NOT be used as a substitute for objective learning measures.
So Favourable reactions on happy sheets do not guarantee that the learners have learnt anything, so one has to be careful with these results. This data merely measures opinion. 
Learners can be happy and stupid. One can express satisfaction with a learning experience yet still have failed to learn. For example, you may have enjoyed the experience just because the trainer told good jokes and kept them amused. Conversely, learning can occur and job performance improve, even though the participants thought the training was a waste of time. Learners often learn under duress, through failure or through experiences which, although difficult at the time, prove to be useful later. 
Happy sheet data is often flawed as it is neither sampled nor representative. In fact, it is often a skewed sample from those that have pens, are prompted, liked or disliked the experience. In any case it is too often applied after the damage has been done. The data is gathered but by that time the cost has been incurred. More focus on evaluation prior to delivery, during analysis and design, is more likely to eliminate inefficiencies in learning.
Level 2 - Testing, testing
Level 2 recommends measuring difference between pre- and post-test results but pre-tests are often ignored. In addition, end-point testing is often crude, usually testing the learner’s short-term memory. With no adequate reinforcement and push into long-term memory, most of the knowledge will be forgotten, even if the learner did pass the post-test.
Tests are often primitive and narrow, testing knowledge and facts, not real understanding and performance. Again, level 2 is inappropriate for informal learning.
Level 3 – Good behaviour
At this level the transfer of learning to actual performance is measured. Many people can perform tasks without being able to articulate the rules they follow. Conversely, many people can articulate a set of rules well, but perform poorly at putting them into practice. This suggests that ultimately, Level three data should take precedence over Level two data. However, this is complicated, time consuming and expensive and often requires the buy-in of line managers with no training background, as well as their time and effort. In practice it is highly relevant but usually ignored.
Level 4 - Does the business
The ultimate justification for spending money on training should be its impact on the business. Measuring training in relation to business outcomes is exceedingly difficult. However, the difficulty of the task should, perhaps, not discourage efforts in this direction. In practice Level 4 is often ignored in favour of counting courses, attendance and pass marks.
General criticisms
First, Kirkpatrick is the first to admit that there is no research or scientific background to his theory. This is not quite true, as it is clearly steeped in the behaviourism that was current when it was written. It is summative, ignores context and ignores methods of delivery. Some therefore think Kirkpatrick asks all the wrong questions, the task is to create the motivation and context for good learning and knowledge sharing, not to treat learning as an auditable commodity. It is also totally inappropriate for informal learning.
Senior managers rarely want all four levels of data. They want more convincing business arguments. It's the training community that tell senior management that they need Kirkpatrick, not the other way round. In this sense it is over-engineered. The 4 linear levels too much. All the evidence shows that Levels 3 and 4 are rarely attempted, as all of the effort and resource focuses on the easier to collect Levels 1 and 2. Some therefore argue that it is not necessary to do all four levels. Given the time and resources needed, and demand from the organisation for relevant data, it is surely better to go straight to Level four. In practice, Level 4 is rarely reached as fear, disinterest, time, cost, disruption and low skills in statistics mitigate against this type of analysis.
The Kirkpatrick model can therefore be seen as often irrelevant, costly, long-winded, and statistically weak. It rarely involves sampling, and both the collection and analysis of the data is crude and often not significant. As an over-engineered, 50 year old theory, it is badly in need of an overhaul (and not just by adding another Level).
Models and messages
Models such as ADDIE, Malsow's pyramid, VAK learning styles Myers-Briggs and Kirkpatrick send the wrong message. They seem as though they are scientific and certain when they are neither. Kirkpatrick gives the illusion of certainty, but s Wll ThalHeimer showed, Kirkpatrick didn;t come up with the four-level model, he uses Katzell's work. Read Kirkpatrick's paper, as it is there on the first page. It is anot a researched model, it was lifted from someone else and was well marketed. Kirkparick mentions Katzell in his first 1956 paper but never again after 1960. The KIrkPatrick model is not only badly researched, it is downright misleading. It simplifies and suggests a model that starts with learner perceptions and proceeds in a linear fashion to business impact, but as the earlier levels are irrelevant, people set of at Level 1 but the journey is so long they never get to Level 4. It's easy doing smile sheets, hards to measure business impact.
Alternatives
Evaluation should be done externally. The rewards to internal evaluators for producing a favourable evaluation report vastly outweigh the rewards for producing an unfavourable report. There are also lots of shorter, sharper and more relevant approaches; Brinkerhoff’s Success Case Method, Daniel Stufflebeam's CIPP Model, Robert Stake's Responsive Evaluation, Kaufman's Five Levels of Evaluation, CIRO (Context, Input, Reaction, Outcome), PERT (Program Evaluation and Review Technique), Alkins' UCLA Model, Provus's Discrepancy Model and Eisner's Connoisseurship Evaluation Model. However, Kirkpatrick is too deeply embedded in the culture of training, a culture that tends to get stuck with theories that are often 50 years, or more, old.
Evaluation is all about decisions. So it makes sense to customise to decisions and decision makers. And if one asks ‘To what problem is evaluation a solution’ one may find that it may be costs, low productivity, staff retention, customer dissatisfaction and so on. In a sense Kirkpatrick may stop relevant evaluation.
Conclusion
Kirkpatrick’s four levels of evaluation have soldiered on for nearly 60 years as, like much training theory, it is the result of strong marketing, now by his son James Kirkpatrick, and has become fossilised in ‘train the trainer’ courses. It has no real researched or empirical background, is over-engineered, linear and focuses too much on less relevant Level 1 and 2 data drawing effort away from the more relevant Level 4. Time to Kill Kirkpatrick.
Bibliography
Kirkpatrick, D. (1959). Techniques for evaluation training programmes.
Kirkpatrick, D. (1994). Evaluating training programmes: The four levels.
Kirkpatrick, D. and Kirkpatrick J.D. (2006). Evaluating Training Programs (3rd ed.). San Francisco, CA: Berrett-Koehler Publishers
Phillips, J. (1996). How much is the training worth? Training and Development, 50(4),20-24.
Kaufman, R. (1996). Strategic Thinking: A Guide to Identifying and Solving Problems. Arlington, VA. & Washington, D.C. Jointly published by the American Society for Training & Development and the International Society for Performance Improvement
Kaufman, R. (2000). Mega Planning: Practical Tools for Organizational Success. Thousand Oaks, CA. Sage Publications.
Sitzmann, T., Brown, K. G., Casper, W. J., Ely, K., & Zimmerman, R. (2008). A review and meta-analysis of the nomological network of trainee reactions. Journal of Applied Psychology93, 280-295.
Sitzmann, T., Ely, K., Brown, K. G., & Bauer, K. N. (in press). Self-assessment of knowledge: An affective or cognitive learning measure? Academy of Management Learning and Education.

Gagne's 9 dull Commandments - why they cripple learning design...

50 year old theory
It is over 50 years since Gagne, a closet behaviourist, published The Conditions of Learning (1965). In 1968 we got his article Learning Hierarchies, then Domains of Learning in 1972. Gagne’s theory has five categories of learning; Intellectual Skills, Cognitive strategies, Verbal information, Motor skills and Attitudes. OK, I quite like these – better than the oft-quoted Bloom trilogy (1956). Then something horrible happened.

Nine Commandments
He claimed to have found the Nine Commandments of learning. A single method of instruction that applies to all five categories of learning, the secret code for divine instructional design. Follow the linear recipe and learning will surely follow.

1 Gaining attention
2 Stating the objective
3 Stimulating recall of prior learning
4 Presenting the stimulus
5 Providing learning guidance
6 Eliciting performance
7 Providing feedback
8 Assessing performance
9 Enhancing retention and transfer to other contexts

Instructional designers often quote Gagne, and these nine steps in proposals for e-learning and other training courses, but let me present an alternative version of this list:

1 Gaining attention
Normally an overlong animation, coporate intro or dull talking head, rarely an engaging interactive event. You need to grab attention not make the learner sit back in their chair and mind.
2 Stating the objective
Now bore the learner stupid with a list of learning objectives (really trainerspeak). Give the plot away and remind them of how really boring this course is going to be.
3 Stimulating recall of prior learning
Can you think of the last time you considered the details of the Data Protection Act?
4 Presenting the stimulus
Is this a behaviourist I see before me? Yip. Click on Mary, Abdul or Nigel to see wht they think of te data Protection Act - cue speech bubble... or worse some awful game where you collect coins or play the role of Sherlock Holmes....
5 Providing learning guidance
We’ve finally got to some content.
6 Eliciting performance
True/False or Multiple-choice questions each with at least one really stupid option (cheat list for MC here).
7 Providing feedback
Yes/no, right/wrong, correct/incorrect…try again.
8 Assessing performance
Use your short-term memory to choose options in the final multiple-choice quiz.
9 Enhancing retention and transfer to other contexts
Never happens! The course ends here, you’re on your own mate….

Banal and dull
First, much of this is banal – get their attention, elicit performance, give feedback, assess. It’s also an instructional ladder that leads straight to Dullsville, a straightjacket that strips away any sense of build and wonder, almost guaranteed to bore more than enlighten. What other form of presentation would give the game away at the start. Would you go to the cinema and expect to hear the objectives of the film before you start?
It’s time we moved on from this old and now dated theory using what we’ve learnt about the brain and the clever use of media. We have AI-driven approaches such as WildFire and CogBooks that personalise learning.....

And don’t get me started on Maslow, Mager or Kirkpatrick!