Here’s why your attitude is more important than your intelligence

When it comes to success, it’s easy to think that people blessed with brains are inevitably going to leave the rest of us in the dust. But new research from Stanford University will change your mind (and your attitude). Psychologist Carol Dweck has spent her entire career studying attitude and performance, and her latest study shows that your attitude is a better predictor of your success than your IQ. Dweck found that people’s core attitudes fall into one of two categories: a fixed mindset or a growth mindset. With a fixed mindset, you believe you are who you are and you cannot change. This creates problems when you’re challenged because anything that appears to be more than you can handle is bound to make you feel hopeless and overwhelmed. People with a growth mindset believe that they can improve with effort. They outperform those with a fixed mindset, even when they have a lower IQ, because they embrace challenges, treating them as opportunities to learn something new.

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Common sense would suggest that having ability, like being smart, inspires confidence. It does, but only while the going is easy. The deciding factor in life is how you handle setbacks and challenges. People with a growth mindset welcome setbacks with open arms. According to Dweck, success in life is all about how you deal with failure. She describes the approach to failure of people with the growth mindset this way,

“Failure is information—we label it failure, but it’s more like, ‘This didn’t work, and I’m a problem solver, so I’ll try something else.’”

Regardless of which side of the chart you fall on, you can make changes and develop a growth mindset. What follows are some strategies that will fine-tune your mindset and help you make certain it’s as growth oriented as possible.

Don’t stay helpless. We all hit moments when we feel helpless. The test is how we react to that feeling. We can either learn from it and move forward or let it drag us down. There are countless successful people who would have never made it if they had succumbed to feelings of helplessness: Walt Disney was fired from the Kansas City Star because he “lacked imagination and had no good ideas,” Oprah Winfrey was fired from her job as a TV anchor in Baltimore for being “too emotionally invested in her stories,” Henry Ford had two failed car companies prior to succeeding with Ford, and Steven Spielberg was rejected by USC’s Cinematic Arts School multiple times. Imagine what would have happened if any of these people had a fixed mindset. They would have succumbed to the rejection and given up hope. People with a growth mindset don’t feel helpless because they know that in order to be successful, you need to be willing to fail hard and then bounce right back.

Be passionate. Empowered people pursue their passions relentlessly. There’s always going to be someone who’s more naturally talented than you are, but what you lack in talent, you can make up for in passion. Empowered people’s passion is what drives their unrelenting pursuit of excellence. Warren Buffet recommends finding your truest passions using, what he calls, the 5/25 technique: Write down the 25 things that you care about the most. Then, cross out the bottom 20. The remaining 5 are your true passions. Everything else is merely a distraction.

Take action. It’s not that people with a growth mindset are able to overcome their fears because they are braver than the rest of us; it’s just that they know fear and anxiety are paralyzing emotions and that the best way to overcome this paralysis is to take action. People with a growth mindset are empowered, and empowered people know that there’s no such thing as a truly perfect moment to move forward. So why wait for one? Taking action turns all your worry and concern about failure into positive, focused energy.

Then go the extra mile (or two). Empowered people give it their all, even on their worst days. They’re always pushing themselves to go the extra mile. One of Bruce Lee’s pupils ran three miles every day with him. One day, they were about to hit the three-mile mark when Bruce said, “Let’s do two more.” His pupil was tired and said, “I’ll die if I run two more.” Bruce’s response? “Then do it.” His pupil became so angry that he finished the full five miles. Exhausted and furious, he confronted Bruce about his comment, and Bruce explained it this way: “Quit and you might as well be dead. If you always put limits on what you can do, physical or anything else, it’ll spread over into the rest of your life. It’ll spread into your work, into your morality, into your entire being. There are no limits. There are plateaus, but you must not stay there; you must go beyond them. If it kills you, it kills you. A man must constantly exceed his level.” If you aren’t getting a little bit better each day, then you’re most likely getting a little worse—and what kind of life is that?

Expect results. People with a growth mindset know that they’re going to fail from time to time, but they never let that keep them from expecting results. Expecting results keeps you motivated and feeds the cycle of empowerment. After all, if you don’t think you’re going to succeed, then why bother?

Be flexible. Everyone encounters unanticipated adversity. People with an empowered, growth-oriented mindset embrace adversity as a means for improvement, as opposed to something that holds them back. When an unexpected situation challenges an empowered person, they flex until they get results.

Don’t complain when things don’t go your way. Complaining is an obvious sign of a fixed mindset. A growth mindset looks for opportunity in everything, so there’s no room for complaints.

Bringing It All Together By keeping track of how you respond to the little things, you can work every day to keep yourself on the right side of the chart above.

Read the original article here

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How old is Computer Science (and why does it matter)?

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The field of mathematics is at least 5,000 years old; we can trace its origins to Mesopotamia. Physics is at least 2,500 years old; in classical Greece, scholars knew the Earth was round. Chemistry dates from about 250 years ago, to the late 1700s. Some consider the work of Antoine Lavoisier, “who developed a law of conservation of mass that demanded careful measurement and quantitative observations of chemical phenomena,” as marking the beginning of modern chemistry.

What about Computer Science?

We can go back to Charles Babbage, and his work on the Difference Engine and the Analytical Engine, beginning in the 1820s. That’s about 200 years ago. The theoretical foundations for computing date from the early 1900s. These were established by the invention of the lambda calculus, by Alonzo Church in the 1930s, and the Turing machine formalism, by Alan Turing in 1936.

Fun facts: (a) Lambda calculus is a way of describing computations via compositions of mathematical functions. Understanding it provides an incredible insight into recursion, but doesn’t help you understand how to build a computer. (b) The Turing machine abstraction, on the other hand, describes a “tape” which has a linear series of memory cells, a “head” for reading and writing data to the cell underneath the head, and a set of rules for deciding what to do at each step and which way the head should move next. It’s a lot more like an actual machine (and hence its name). (c) Also, Alonzo Church (inventor of the lambda calculus) was the doctoral adviser of Alan Turing!

It was a decade after this work, in the late 1940s, that the idea of a stored-program computer was introduced, by John von Neumann. I’d say these are the key moments in the history of the ideas behind computing.

When did Computer Science professionalize?

Another way of marking history is to look at professional organizations. The Association for Computing Machinery (ACM) was founded in 1947, and SIGCSE, the Special Interest Group for Computer Science Education, held its first annual Symposium in 1970 (next year in 2019 will be its 50th meeting!). At the university level, “departments of Computer Science” didn’t become widespread until the 1980s—about 35 years ago! The Texas Computer Science Teachers Association was founded in 2004—meaning next year will be our fifteen year. By comparison, the US-based National Council of Teachers of Mathematics (NCTM) inaugurated its first president in 1920—it’s nearly 100 years old!.

What about computing in K–12 schools?

Seymour Papert, with Cynthia Solomon, and others, did their foundational work on Logo beginning in the late 1960s. In the United States, it wasn’t until computers like the Texas Instruments TI-99/4 (1981), the Apple IIe (1983) and the IBM PCjr (1984) shipped that computers started to enter schools in large numbers. That’s also about 35 years ago.

Why does this history matter?

We need to remember that we all are the pioneers at the beginning of a vast intellectual and cultural journey. At the higher ed level, what’s remarkable about Computer Science curricula is that smart minds don’t agree! Some CS departments start with Java and teach the machine late. Others start with C, introduce the machine early, and teach abstract principles late. That’s just one example of the diversity in university CS curricula. There are many others. Compare this to mathematics and physics. In those disciplines, everyone knows that the “correct answer” is to teach differentiation followed by integration (Calculus I and II) and mechanics followed by electromagnetism (Physics I and II). Practically every first year science and engineering student across the United States will take courses in that sequence. Our field is nothing like this. We are still figuring out what works. (Probably, lots of sequences will work, and I personally hope that we never arrive at a “best” answer.) In K–12, we are exploring integrating Computer Science into other subjects—for example, using modeling and simulation in understanding science. Ours is the really exciting time. We should revel in being the pioneers—our work has the chance to set the direction in our field for a long time to come.

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The Fibonacci Sequence

Every year teaching Computer Science, the Fibonacci sequence comes up in some format. Whether its challenging grade 10 students when they learn looping, or going over looping again for grade 11 students when they transition to Java, or for grade 12 students when they learn recursion, the Fibonacci sequence is always one of my “go to” examples. I recently heard of this NOVA episode about it. This relates to my Theory of Knowledge classes as well: