Learned Helplessness

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Contents


Some Initial Thoughts

Students vary considerably in their speed and depth of learning. A good education system works toward each individual student being all that they can be.

To be all that you can be, you must work hard, learn to know yourself, learn to make effective use of the aids to learning that are available, and learn to take responsibility for yourself.

I think the idea of learned helplessness is quite applicable in math education. Many people seem to learn "I can't do math." That is (in my opinion) almost always the result of learned helplessness.

Keith Devlin's work (2000) tells us that few exceptions, a person who can learn to speak and listen can learn math.

Carol Dweck's work and the work of others gives us good insight into how learned helplessness can occur. Her work suggests that it is a result of just giving up trying. Persistent trying with little or no0 success leads to giving up, and this giving up can transfer to other areas.


It is easy to create math education environments in which students cannot succeed even when they try hard. The problems and tasks are significantly above their current levels of math maturity (math cognitive development) and math knowledge and skills.

Carol Dweck

Carol Dweck: https://www.stanford.edu/dept/psychology/cgi-bin/drupalm/cdweck

Quoting from

https://www.stanford.edu/dept/psychology/cgi-bin/drupalm/system/files/cdwecklearning%20success.pdf

Most students aim to succeed on academic tests. Yet, there is increasing evidence that the likelihood of their success is influenced not only by actual ability, but also by the beliefs and goals that they bring to the achievement situation (Elliot and Dweck, 2005). One framework that has been informative in addressing not only how these beliefs affect overall performance, but also how they affect rebound following failure, concerns individuals’ theories of intelligence (TOI; Dweck and Sorich, 1999). Previous behavioral studies have shown that students who believe that intelligence is a fixed quantity (‘entity theorists’) are particularly vulnerable to decreased performance when they realize they are at risk of failing, whereas students who view intelligence as acquirable (‘incremental theorists’) appear better able to remain effective learners.
These outcomes may be rooted in the different goals that follow from holding either a fixed or an acquirable view of intelligence (Dweck and Leggett, 1988; Hong et al., 1997; Mueller and Dweck, 1998; Sorich-Blackwell, 2001). Entity theorists tend to be more concerned with besting others in order to prove their intelligence (‘performance goals’), leaving them highly vulnerable to negative feedback. As a result, these individuals are more likely to shun learning opportunities where they anticipate a high risk of errors, or to disengage from these situations when errors occur. Indeed, when areas of weakness are exposed, they often will forego remedial opportunities that could be critical for future success (Chiu et al., 1997). In contrast, incremental theorists are more likely to endorse the goal of increasing ability through effort and are more likely to gravitate toward tasks that offer real challenges (‘learning goals’). In addition, in line with their view that there is always potential for intellectual growth, they are more willing to pursue remedial activities when they experience academic difficulty.

Mihály Csíkszentmihályi

Quoting from http://en.wikipedia.org/wiki/Flow_%28psychology%29

Flow is the mental state of operation in which a person in an activity is fully immersed in a feeling of energized focus, full involvement, and success in the process of the activity. Proposed by Mihály Csíkszentmihályi, the positive psychology concept has been widely referenced across a variety of fields.

References

Glenn, David (5/9/2010). Carol Dweck's attitude: It's not about how smart you are. The Chronicle of Higher Education. Retrieved 5/11/2010 from http://chronicle.com/article/Carol-Dwecks-Attitude/65405/?sid=at&utm_source=at&utm_medium=en.Quoting from the article:

What Dweck has wondered about for the last 40 years, more than any other single question, is how people cope with failure. In graduate school at Yale University, she became intrigued by Martin E.P. Seligman's model of "learned helplessness." In a famous series of experiments in the mid-1960s, Seligman and his colleague Steven F. Maier demonstrated that dogs that were subjected to random, uncontrollable electric shocks usually became helpless over time. That is, even if they were moved into an environment in which they could prevent the shocks by pressing a lever or doing some other trick, the dogs never learned to do so. The experience of random punishment had rendered these dogs passive, and immune to classical Pavlovian conditioning.
In a landmark series of studies with Claudia M. Mueller during the 1990s, Dweck demonstrated that praising children for their intelligence, rather than for their effort, often leads them to give up when they encounter setbacks. Such children tend to become preoccupied with how their performance compares with that of their peers, rather than with finding new strategies to improve their own work."
In the nineties, the self-esteem gurus were telling parents and teachers to praise children as lavishly and globally as possible," Dweck says. "But from my research going back 20 years, I knew that it was the children who were overly concerned with their intelligence—who were even trafficking in that concept—who were the vulnerable ones." That element was something that Dweck began to explore in the years after her colored-block study; in a 1978 experiment with Carol I. Diener, Dweck found that children who described their own memory or intelligence in fixed ways were much more likely to give up on a difficult pattern-identification task than otherwise-similar children who did not make such statements.

Devlin, Keith (2000). The math gene: How mathematical thinking evolved and why numbers are like gosip. Basic Books.

Author or Authors

The initial version of this document was developed by David Moursund.

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