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Problem Solving

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Defining deliberate practice

The term "deliberate practice" is mostly attributed to Karl Anders Ericsson, an influential figure in the field of performance psychology. Deliberate practice turns amateurs into professionals. It creates top performers in any field.

Doing something regularly but mindlessly is not the same as practicing it. Deliberate practice means repeatedly performing a set of activities with the intention of improving the specific skill.

@jackh255

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Problem Solving

While engaging in deliberate practise, we are always looking for errors or areas of weakness. Once we identify it, we establish a plan for improving it. If one approach does not work, we keep trying new ones until it does.

In using deliberate practice, we can overcome many limitations we might see as fixed. We can go further than we ever thought possible.

Deliberate practice is a universal technique employed in any area you're trying to be the best at or get a little bit better at.

For example, competitive fields with clear measurements such as music, dance football, horse riding, swimming, or chess. But we can also improve performance in fields such as teaching, nursing, surgery, therapy, programming, trading, writing, decision-making, leadership, studying, and communication.

Our nature is to choose the easiest thing. When we practice something a lot, we develop habits that make the task almost effortless. While it may be helpful, it can interfere with our improvement.

Deliberate practice means finding the weak areas that impact your overall performance and then target those.

  • Break the skill you want to improve into the smallest possible parts.
  • Then work through them in a logical order, starting with the fundamentals.
  • Put your practice sessions on your calendar.

Most often, deliberate practice is repeated frustration and failure. Similar to a baby learning to walk, we will often fall for every step we take. That is the point. Since deliberate practice targets our weakest areas, it means doing the stuff we're not good at. We will get frustrated.

Set a short, ambitious goal once a month, not impossible, then challenge yourself to it.

Deliberate practice is very challenging and impossible to do all day long. At the high end, top practitioners rarely spend more than three to five hours per day on deliberate practice. More hours often result in diminishing returns. One hour per day is enough for substantial improvements, especially when it's consistently done over a long period.

Enough rest and recovery are vital. During deliberate practice, we need to switch to relaxing activities to feel refreshed.

Practicing something without knowing if you are getting better is pointless.

  • Identify the most important metrics related to performance. Keep a record of them each time you practice.
  • Be careful of vanity metrics. For example, if you use deliberate practice to improve email marketing skills to get more customers for your business. An increase in email subscribers is a vanity metric; the number of paying customers is a useful metric.

Deliberate practice is most effective when used with a coach some of the time to give feedback, point out problems, suggest techniques for improvement, and provide vital motivation.

A coach can see your performance from a different perspective. If you don't have access to a coach, built the skill of metacognition (knowledge about your own knowledge), where it becomes possible to coach yourself systematically.

Geoff Colvin
"…A critical part of self-evaluation is deciding what caused those errors. Average performers believe their errors were caused by factors outside their control: my opponent got lucky; the task was too hard; I just don’t have the natural ability for this. Top performers, by contrast, believe they are responsible for their errors."

Persisting with deliberate practice needs a lot of motivation. However, the motivation needs to come from within, not from external rewards or to avoid a negative consequence. We need to enjoy getting better for its own sake.

  • Make a list of reasons you want to work on a skill and the benefits you've experienced.
  • Keep a "motivation diary" for one week, scoring your motivation level every fifteen minutes during each pratice session.
  • Find a cheerleader to help sustain motivation.
  • Deliberate practice tends to fast track progress, but truly mastering a skill is a lifelong process.
  • There are no examples of people labelled as prodigies that did not put in enormous amounts of deliberate practice.
  • Creative genius tends to come after ten years of studying relevant knowledge and developing skills, research found.
  • Those who eventually gain recognition produce an enormous quantity of less remarkable work.

The deeper we focus during deliberate practice sessions, the more we get out of them.

  • If you can pay your full attention for only five minutes in practice, then take a break every five minutes. Increase your focus time as you get better.
  • Figure out which practice activities have the biggest potential to increase your performance. Plan to do them first before considering activities that offer marginal gains.

The spacing effect refers to how we can better remember information if we learn them in multiple sessions with increasingly longer intervals between them. It is nearly impossible to practice something once and expect it to stick.

Every time you're learning a new part of a skill, make a schedule for when you'll review it. Typically, it involves going over information after an hour, then a day, then every other day, then weekly, then fortnightly, then monthly, then every six months, then yearly.

Deliberate practice is more complex and nuanced than we like to believe.

  • Deliberate practice is necessary but insufficient. Once you reach a high level for any skill, everyone at that level is engaging in a lot of deliberate practice. Then more luck and randomness, innate talents or physical advantages matter.
  • Practitioners miss out on other parts of life as they devote most of their time to practice.
  • Sometimes, we can practice past the point where it serves us. We need to know when to pivot.
Design Thinking And Our Life

Design thinking is a methodology to innovate new designs into products and services and can be applied to many other problems in our lives, including how we live them.

Designers who get stuck in various design problems, complications and unforeseen situations try to find innovative solutions and work on their problems in a creative way. This is because what they are doing hasn’t been done before.

Getting stuck in obstacles is a constant, common process in life, and while building any new design. We can get unstuck by doing what designers do: Reframing our beliefs, assumptions and situations.

Dysfunctional people, who are stuck in an old belief pattern will say things like:

  • We must know our passion.
  • We should know what to do by the time we are out of college.
  • We should keep optimizing to be at our best.

All of these belief patterns assume that life is a linear, logical process, which it is definitely not.

We all want life to be meaningful and purposeful. When we connect the dots of life, trying to find out who we are, what we believe and what we do, our life story, however crooked, begins to take shape.

We can jot down our life views, our work life views, our relationships, and try to experience life in the way it gives us meaning.

Gravity problems are the things that you cannot change. You cannot do anything but accept gravity, and some problems are just there, whether we acknowledge them or not.

A work through or a work around strategy is required to handle a gravity problem.

An Odyssey plan or a rubric is when you have to ask certain questions with different sets of base standards, omitting certain parameters or constraints and imagining the result. It generates many ideas out of one idea.

Imagining different life designs and the outcome can trigger new ideas that didn’t exist before due to mental constraints.

We can use new learning experiences to build a prototype of what we need in our minds. It can be a prototype conversation or experience.

Example: If we want to know how something works, we can intern there for a week to get hands-on knowledge with real-world experience.

Likewise, If we have a feeling of uncertainty and doubt about how something works, we can check it out as a demo, and find more in-depth details for the same.

  • We need to make the right choice, as with every choice there is lost opportunity (called opportunity cost) and a time, energy and resource commitment.
  • Making the perfect choice is arduous and will consume a lot of time, while still not feeling satisfactory, due to regret, second guesses and even Fear Of Missing Out (FOMO) where we need to check and understand everything that is happening around us.
  • A good enough choice which is not changeable will make you happier and relaxed.

It is a set of three lists used to classify educational learning objectives into levels of complexity and specificity. They concentrate specifically on learning objectives in the cognitive domain (knowledge-based), affective (emotion-based) and psychomotor domains (action-based).

These three models were named after Benjamin Bloom, the author of Taxonomy of Educational Objectives: The Classification of Educational Goals.

Bloom’s taxonomy: learning objectives
  • Knowledge-based objectives. Instead of just memorizing facts, the goal of this area is to be capable to remember, understand, apply, analyze, create, and evaluate our knowledge.
  • Emotion-based objectives. This domain measures how much you care about what you are studying: passive attention (the lowest level), active participation, valuation, organization, and characterization (the highest level, where you care enough to generate your own take on the knowledge).
  • Action-based objectives. This domain measures your ability to actually manipulate relevant tools.

DOVE stands for: Deferral of judgment; Off-beat ideas; Vast quantities of ideas; Elaboration/expansion of ideas.

The ROPE method of brainstorming stands for:

  • Record all ideas
  • Original ideas are encouraged (no stupid ideas)
  • Put off evaluation (judgment can be done later)
  • Expand on other’s ideas.
  • What are the consequences of believing this?
  • How consistent is the information?
  • What assumptions have been made here?
  • How accurate is the data or information?
  • What is the meaning of this?
  • What are the main points?
  • What prejudice is being shown here?
  • What other points of view could be expressed?
  • What evidence is there to support the position or claims?
  • What examples are there to back-up the position or claims?
  • How relevant is the position or claims?
  • How reliable is the information?

By repeating the question “why?” five times in a row, you explore the cause-and-effect relationships underlying a particular problem; the primary goal of the technique is to determine the root cause of a problem.

This method means using a thinking buddy to debug a problem together, then share back what you found with the team.

  • Think: each person thinks about the problem and writes down their thoughts.
  • Pair: consolidate individual thoughts together.
  • Share: share back with the rest of the team.
How to improve your knowledge

According to the Falsification Principle of Karl Popper, we cannot prove the validity of a hypothesis. We can only disprove it.

However, we can get closer to the truth by improving our knowledge, using inductive or deductive reasoning. Both are based on evidence, but provide different ways of evaluating the facts.

Inductive reasoning involves looking for a trend or a pattern, then using the observations to formulate a general truth. For example, "When I eat peanuts, my throat swells up and I have difficulty breathing. Therefore, I'm likely allergic to peanuts."

Limitations:

  • Inductive reasoning leads to uncertain conclusions as there is no way to prove the veracity.
  • Inductive reasoning can also lead to wrong conclusions. We may mistake correlation with causation, or apply the particular to the general.

Deductive reasoning starts from established facts, then applies logical steps to reach a conclusion. For example, "Bachelors are unmarried men. Jack is unmarried. Therefore, Jack is a bachelor."

Limitations:

  • If the premise or the logic applied is flawed, deductive reasoning can lead to the wrong conclusions. For example, "All dogs are animals; all animals have four legs; therefore, all dogs have four legs," is false because injury or species characteristics mean not all animals have four legs.
  • It is impractical to use on a daily basis, as it requires a factual premise to start from, which we don't always have access to.
  • Building a premise on an outdated hypothesis can lead to the wrong conclusion.

Depending on the nature of the task, inductive and deductive reasoning can be used in combination.

Researchers use inductive reasoning to formulate theories and hypotheses. Then they use deductive reasoning for evaluating their theories in specific situations.

An algorithm is a set of instructions

The instructions tell a computer how to transform a set of facts into useful information.

The facts are data. The useful information is knowledge for people, instructions for machines or input for another algorithm. Typical examples are sorting sets of numbers or finding routes through maps.

To a computer, input is the information needed to make decisions.

For example, if you get dressed, you will need information such as what clothes are available to you, then you might consider the temperature, the season, and personal preferences.

Computation is the heart of an algorithm and involves arithmetic, decision-making, and repetition.

To apply this to getting dressed, you make decisions by doing some math on input quantities. Wearing a jacket might depend on the temperature. To a computer, part of getting dressed algorithm would be "if it is below 50 degrees and raining, then pick the rain jacket and a long-sleeved shirt."

The last step of an algorithm is output - expressing the answer.

Output to a computer is usually more data. It allows computers to string algorithms together in complex ways to produce more algorithms. Output can also present information, such as putting words on a screen.

It can sometimes be too complicated to spell out a decision-making process. Machine learning tries to "learn" based on a set of past decision-making examples.

Machine learning is used for things like recommendations, predictions, and looking up information.

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