This week, we have a wide-ranging look at articles that have implications for the future of learning, starting with the idea that we should "Specialize most of the time, but spend time understanding the broader ideas of the world."
This is a fascinating article from Farnam Street about the pros and cons of generalised or inter-disciplinary learning versus specialisation. The question threading through this is 'Should I specialise, or become a polymath?', while suggesting that that if a person can't adapt, changes can be perceived as threats instead of opportunities. The article describes various costs and benefits to each approach, with persuasive arguments made for settling on the idea that interdisciplinary knowledge allows us to "see with new eyes".
The Generalised Specialist specialises most of the time, but takes time to become actively informed about and developing some knowledge in other disciplines. The article describes this as being fundamental in a time of rapid change, and that school curricula do not provide opportunities for general specialisation to develop.
Is it time to make this change happen in our schools?
Data analytics and machine is becoming a big part of education, and these methods are being applied to the question of how to raise student achievement. In the second article, McKinsey & Company has done deep analysis of achievement data from PISA exam results globally, and have identified some critical factors in raising student achievement across all global regions, with two included in this link being:
1. The right mindset matters more than socioeconomic background.
2. Students who receive a blend of teacher-directed and inquiry-based instruction have the best outcomes, and is 'something skin' to a universal learning style.
In most classrooms globally, the predominant method of instruction is teacher-directed, whole-class instruction, with authentic inquiry not happening. If a blended approach is desirable, how can we develop a learning system to make it happen? How do we develop institutional and pedagogical capability?
Next we explore how the nature of work is changing. Freelancers make up 35% of the total workforce in The United States, and total numbers have increased by 1 million freelancers per year for the last 3 years. Entrepreneur.com reports that freelancers tend to make more, enjoy more freedom and flexibility, and use technology to increase income and grow their business. There remain challenges: ensuring that they have access to health and retirement benefits is key, along with ensuring that clients pay on time and as agreed.
Reading around key personal and professional characteristics that influence the success of freelance workers, the following can be identified:
1. Strong intrinsic motivation.
2. Excellent communication and presentation skills.
3. Resilience, grit and responding constructively to feedback.
4. Constant retraining and learning.
5. They view themselves as entrepreneurs, always seeking new opportunities.
Are those who choose to become freelancers being taught these skills in schools - are they prepared? Does the design of our education and learning systems reflect this new and growing reality of work?
We have some interesting reading from thebaffler.com today about the rapid reemergence of behaviourism (conditioning) in education through apps that are now in wide-spread use among some schools and districts. Schools can track behaviour, reward it, share incidents and provide rewards for positive behaviour. Where this might become problematic is that the 'correct behaviour', although largely defined by school administrators, is determined by Silicon Valley engineers. The surveillance and control of student activities is raising privacy concerns, and it's important to ask questions about the role of technology in our schools and unintended consequences, especially as the use of big data becomes more widespread.
It's not hard to foresee a near-future in which schools constantly monitor thousands of data points in real time, from lateness, noise levels, movement around school and amount/type of activity, student engagement and achievement, with everything being optimised and setting completely personalised student goals, again communicated in real time.
Could there be any possible unintended consequences here?
Along with the hype and attention surrounding the potential of Artificial Intelligence (AI) right now, especially following the achievements of Alphago, there is criticism that AI in its present form can only tackle certain classes of problems. The Allen AI Science Challenge was an attempt to use AI to test modelling, reasoning, use of language and common sense knowledge to answer a series of 8th Grade Science questions.
While not designed to test AI to its limits, answering 8th Grade science questions do test capabilities commonly associated with human intelligence and require a range of responses, from the simple to the complex. Teams had 4 months to write software that could solve the questions, and once the tests were taken the results are fascinating. The winning AI scored 59%, and although AI clearly has some way to go, don't be surprised to see these test results climb higher very quickly in the near future.
Would science tests then be rendered obsolete? What are the implications for learning?
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