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"emotional-learning" Posts

The Machine Learning Revolution

During the “automation scare” in the 1950s, people were intrigued yet suspicious about the power of computers. Would they someday send us into permanent unemployment? Could robots eventually take over the world?

The original goal of artificial intelligence was to build a person out of silicone. Although scientists made quick progress in building computers and robots that amazed us, they never came close to actually replicating the human brain.

“Machine learning” was first introduced 50 years ago. This concept focuses on computers’ ability to “learn” not through human programming, but through experience and pattern identification. For example, machine learning allows a computer to become a masterful chess player by observing good and bad moves and learning from mistakes. While a computer looks at every possible move up to 20 to 40 moves ahead, humans use more conceptual skills to decide how to make moves.

In a recent PBS NOVA documentary “The Smartest Machine on Earth,” researchers explore powerful new tools in computing, like “Watson,” a supercomputer with a brain, or central processing unit, that can process 500 gigabytes, or the equivalent of a million books, per second. In 2009, Jeopardy producers came to IBM to size up Watson’s abilities.

You might remember when 74-win Jeopardy! champion Ken Jennings went head-to-CPU with Watson a few years back. During Watson’s “training” for the match, the scientists at IBM had to constantly expose Watson to large amounts of possible answers to questions so that it would have sufficient rules and logic to come up with correct answers.

Having studied thousands of questions, within a few milliseconds Watson analyzed every possible answer. It learned to make statistical judgments based on how pieces of evidence work together in the database of information scientists gave to it. In the end, the competition was close, but Watson pulled ahead and won the show.

Scientists who worked with Watson point out that there are two ways of building intelligence. We can either write down the recipe or let it grow by itself. It’s clear that we don’t know how to write down the recipe, according to scientists. Machine learning enables computers to grow their own intelligence.

Scientists continue to debate the ability of machines to truly displace us. There is so much that we know that we don’t even realize we know, such as the fact that ice is cold and sandpaper is rough. Our common sense knowledge seems too complex to program into a computer. Human intelligence is deeply rooted in language and emotion.

Without experience or emotion, can computers ever understand the world the way we do?

machinebloggraphic3 ILG

They don’t connect to human cognition on an emotional level, such as the way a symphony or a play can move us. Language and context are barriers for understanding. For example, figuring out the meaning of a sentence like “I shot an elephant wearing pajamas” is very difficult for computers. Was the shooter wearing pajamas or was the elephant? Was a camera or a gun used to do the shooting?

Of course machine learning goes far beyond winning trivia game shows. It’s driving a computing revolution. According to an article in Wired magazine , today artificial intelligence isn’t trying to re-create the brain. Instead, it relies on machine learning, massive data sets, sophisticated sensors, and clever algorithms to master discrete tasks. “In short, we are engaged in a permanent dance with machines, locked in an increasingly dependent embrace.”

Machine learning makes it possible to predict the weather days in advance. It lets companies like Amazon or Zappos suggest products for you based on what you’ve chosen before. It allows doctors to better diagnose medical conditions.

It’s even helping us communicate with people through speech recognition, which was once though impossible. Computers are now trained with millions of patterns of human speech, and the accuracy continues to improve. There are even apps for the iPad and iPhone that you can use to quickly record something and translate it on the spot when talking to a person who speaks another language.

IBM imagines a time when a computer will operate like the one in Star Trek—as information-seeking tools that communicates with us to ensure we get what we want. This thinking signals a shift in the way we use and accept computers in our lives, compared to the fear and suspicion we felt half a century ago.

IdeaLearning Group selected to speak at ASTD Cascadia Annual Conference

Jillian Douglas and Jennie Thede of IdeaLearning Group have been selected to present at the 2012 ASTD Cascadia Conference. Their session is titled Your Brain is Not a Bucket: Learning through Experience.

The brain is not a finite container that you fill up. Instead, the brain is a complex system of dynamic connections that interpret, process, and organize an amazing amount of information. Most of our learning takes place through direct experience as opposed to formal instruction. In this session, we look at the shortcoming of traditional lecture-based training and education through the filter of brain-based learning research. What roles do experience and sensory intake play in learning? How can we implement brain-based learning principles to get back to the basics and create richer, long-lasting learning experiences? This hands-on workshop will inspire you to reframe the way you train using practical brain-based tools and strategies with your learners.

It’s Not How Much You Know, It’s How You Think

In case it isn’t obvious, Jillian and I are fascinated by brain research that explores how people learn. Last week we looked at emotional learning. This week we’re taking a closer look at cognitive learning, which incorporates emotions and thought into the learning process. This theory gives less importance to the role of behavior and instead embraces the learner’s thought processes and other internal insights unique the individual.

Cognitive learning theory focuses on learning through understanding and meaning, as opposed to memorizing facts and figures. We observe, listen, touch, read, or experience new information before processing and then recalling it. Although it might seem passive, cognitive learning is quite active. The difference is that all the activity is going on inside the processing and storage areas of the brain.

Roger Schank, a leading visionary in cognitive science and learning, defines learning not as an accumulation of knowledge, but as an improvement in one’s own cognitive processes. In his book Teaching Minds: How Cognitive Science Can Save Our Schools, he outlines twelve learning processes organized into three categories:

  • Conceptual processes: prediction, modeling, experimentation, evaluation
  • Analytic processes: diagnosis, planning, causation, judgment
  • Social processes: Influence, teamwork, negotiation, describing

According to the ASTD Handbook, the cognitive view of how learning takes place is “based on how information is processed, stored, and retrieved in the mind, rather than on how behavior changes.” The approach helps learners remember information, comprehend how things work, and refer to new procedures when needed.

That’s a high-level view of cognitive learning theory. So how do we use cognitive learning theory to actually design training for adults? Here’s what the ASTD Cognitive Training Model recommends:

  • Gain attention: Start with a relevant way to grab the learners’ attention right away. Focus the attention on the new knowledge to generate encouragement and excitement for learning.
  • Recall and relate: Draw a connection between the information the learners already know and the new knowledge they’re about to gain. Highlight similarities between the old and new knowledge throughout the course.
  • Structure content: Be clear about the desired behaviors and knowledge. Organize new information into bite-size pieces to avoid cognitive overload and to promote learning that will stick.
  • Use visuals throughout: Along with organized on-screen text, graphics and animations may help introduce and support new content.
  • Assimilate the old and new knowledge: Use real-life or realistic examples to demonstrate how the new knowledge works in a context that appeals to the learners.
  • Strengthen the new knowledge: Engage learners by having them do something with their new knowledge, such as through interactive exercises, games, or on-the-job application of their new skills.

The Role of Emotions in Learning

Let’s say you’re in charge of creating a program that influences a change in behavior: reducing texting and driving among new drivers. You have the choice to either create a document that can be emailed to students, or to create a visual piece that gets your message across.

Which would you choose? A PowerPoint file that reviews reasons why drinking and texting is bad…

…or a wrecked car display at the school with a reminder to avoid texting and driving?

Which do you think will evoke more emotions?

Which do you think will come to the driver’s mind as he or she decides whether or not to text and drive? Why?

Emotions act as the framework that learners of all ages use to interpret meaning. They play a major role in defining our personalities. We make thousands of decisions every day based on our emotions. In his book Brain-Based Learning, Eric Jensen describes that emotions also have the ability to influence how we learn by:

  • Helping us figure out what’s real and what we believe and feel
  • Activating long-term memory; the more intense the amygdala arousal, the stronger the imprint
  • Helping us make faster decisions by using gut judgment
  • Helping us engage our values while making decisions

The old way of thinking was that rational decision-making was the way to go; eliminate feelings and let the pros and cons guide the way. But modern brain research casts a new light on the important role of emotions in learning and decision-making.

According to research by the Center for Development and Learning, the brain relies on emotions to drive action. The limbic system in the middle of the brain is where we determine and manage our emotions and behavior. In response to internal and external stimuli, the amygdala releases chemicals that stimulate our brain, which can help us process and remember information. When the limbic system receives information, it sets the “emotional tone” of the information before sending it to the cortex for processing.

When the brain interprets information as positive, it sends off a signal of purpose and excitement and directs our behavior toward a goal. The result is motivated learning, thinking, and enhanced memory. But when information is interpreted in a negative manner, chemicals are released in the bloodstream that produce a range of stress-related bodily responses, like sweaty palms, internal tension, and increased blood pressure. These emotions often prevent us from learning and remembering.

Trainers and educators can enhance learners’ ability to absorb new information by consciously allowing emotions to help shape their experience instead of shutting them out. Here are some ideas for incorporating emotional aspects of learning into your curriculum.

  • Provide projects that are personally meaningful to the learners.
  • Design a classroom environment that’s comfortable and allows for non-threatening collaboration.
  • When reviewing goals for learning, ask your learners why they want to reach them, and encourage them to share their answers with other participants. According to Jensen, “It is the emotions behind the goals that provide the energy to accomplish them.”
  • To minimize stress, make sure helpful resources are available for every learner.
  • Try to engage as many senses as possible. When multiple senses are engaged, the brain has a very rich learning experience.
  • Encourage learners to discuss feelings and emotions that relate to the new material.

Is emotional learning part of your training program? Ask us how we can help.

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