## MYTHS ABOUT MATH

Myth 1: ONLY SOME PEOPLE CAN LEARN MATH

"Everyone is born with the innate ability to do well in math and whether you do well or not, comes from the experiences you've had and the beliefs you hold." Some people think that math is a gift and only smart people can do it, but now brain research shows that the brain is capable of growth.

Myth 2: SMART PEOPLE FINISH MATH TASKS QUICKLY

Myth 3: MATH IS ALL ABOUT RULES AND CALCULATIONS

Myth 4: BOYS ARE BETTER AT MATH THAN GIRLS

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Being fast at math does not necessarily mean that you are smarter at math. Thinking deeply and making connections about math takes time and perseverance. Asking rich questions such as "Why does this work? How is this method connected to other methods? What would a drawing of this situation look like?" can help students to think more deeply to fully understanding a concept and connections.

"Math in the real world is about problem solving, modeling, simulations, thinking out what the questions are, analysing results and critiquing the." (Wolfram, 2013). It is not about rules, and calculations. Classroom experiences are moving towards inquiry

This myth is propagated by the belief that there are some people good at math and some who are not. It also has merit for many people, as they still believe that antiquated research of Fruchter (1954), who claimed that males have enhanced development in spatial perception and mathematical reasoning ability, as they are more logical thinkers (as cited in Hall, 2012). Evidence from EQAO and TIMMS testing demonstrate that this is a fallacy.

Myth 5: PEOPLE WHO MATH MISTAKES ARE NOT GOOD AT MATH

Making mistakes is part of the learning process. People with a fixed minset feel threatened when making errors as it impacts their self-image and self-esteem (Dweck, 2006). Researchers know that the brain is like a muscle, the more you use it the more it grows (Boaler, 2015). More learning occurs becasue there are two synapses firing one for the mistake and one for the correction, making more complex neural networks (Boaler, 2014b).