iTech Seminar. David W. Brooks January 24, 2005. Today’s Goals. Describe ICML. Indicate what is known. Relate what is know (tenuously) to neural structures. Focus on what is NOT known. Speculate. Punch Line.
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
David W. Brooks
January 24, 2005
Indicate what is known.
Relate what is know (tenuously) to neural structures.
Focus on what is NOT known.
From an instructional design perspective, we don’t know as much about motivation as we know about working memory.
WM issues are becoming formulaic; motivation is still quite hit-or-miss.
Perhaps the biggest notion of the ICML is that we humans are not cast in stone or otherwise trapped by some genetic or early-life-event factors. Most folks can work out of a learning “issue”; effort matters.
The best predictor of what one can learn is their current knowledge. This generally accounts for no less than one third of the variance, and often accounts for >80% of variance.
A recent paper by Shapiro suggests that prior knowledge often is underestimated as a source of learning success.
Shapiro, A. (2004). How including prior knowledge as a subject variable may change outcomes of learning research. American Educational Research Journal, 41(1), 159-189.
An enormous amount of hand-waving goes on related to intelligence. First, it is clear that intelligence does exist and does matter. Next, it also is clear that differences in intelligence do not impact on performance much among professionals suggesting that all can achieve appropriate levels of knowledge.
A very recent paper suggests an essentially complete overlap between g and working memory. That is, measuring g gives one essentially all one needs to know about working memory. While certainly not a wonderful paper, this report does make sense.
Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. C. (2004). Working memory is (almost) perfectly predicted by g. Intelligence, 32, 277-296.
One can summarize nearly ALL of design work along these lines.
Content folks work in the knowledge box and figure out the sequence of steps needed to achieve understanding.
Occasionally they add something about strategy instruction.
The designer pretty much works on lowering cognitive load to make material accessible. This is important when the material is difficult (possibly just new).
Conflicts often arise between SMEs and IBEs. These often come from issues where like the IBE takes out something that the SME thinks of as very interesting or important (even if a bit off topic).
While this certainly is not meant to trivialize what is not known about knowledge structures and working memory, we know still less about --
Working with Zoo animals, children seemed to give special ‘respect’ not afforded to animals brought to classrooms by teachers.
Trainin et al. showed this to be related to the uniqueness of the animal rather than some ‘aura’ connected with the Zoo.
Trainin, G., Wilson, K., Wickless, M., & Brooks, D. (2005). Extraordinary Animals and Expository Writing: Zoo in the Classroom. J. Sci. Educ. Technology, accepted.
Advanced electrical engineering students found computer simulations of signals to be somehow ‘fake’ and those of hardware simulators to be ‘real.’
Reality versus Simulation, Srilekha Srinivasan, Lance C. Pérez, Robert D. Palmer, David W. Brooks, David Fowler (under revision)
Enormous effort is expended in biology education to deal with the teaching of evolution.
The point is that sometimes motivational issues become all-consuming.
I would assert that much of the difference in approach between TEAC and EDPS is in how one values ‘feel good.’ Indeed, feel good is often a primary goal in TEAC.
I think of qualitative research as feel-good research.
Knowledge changes probably are based in changes in the cortex. These involve -- one way or another -- lowering energy transmission through the cortex. The better nuanced and articulated a neural net, the more nuanced and articulated can be a neural output.
Ericsson’s work can be reduced to explanation in terms of articulating complex neural networks.
Performance understood in terms of automation -- which involves reducing the amount of energy required to support a performance.
Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The Role of Deliberate Practice in the Acquisition of Expert Performance. Psychological Rev., 100(3), 363-406.
The working memory is probably connected to the hippocampus.
Involvement of the cortex is probably a confusion based upon use of the (frontal) cortex to store sequencing information.
Working memory is probably determined by some combination of genetic and/or in utero factors.
“The tiny hypothalamus serves as the Health Maintenance Organization of the body, regulating its homeostasis, or stable state of equilibrium. The hypothalamus also generates behaviors involved in eating, drinking, general arousal, rage, aggression, embarrassment, escape from danger, pleasure and copulation. …”
McClure, S. M., Laibson, D. I., Lowenstein, G., & Cohen, J. D. (2004). Separate Neural Systems Value Immediate and Delayed Monetary Rewards. Science, 306, 503-507.
Immediate -- limbic system
Delayed -- cortex
I’ve found two approaches in the literature from which to try to tackle this issue.
Bandura introduces a concept, goal challenge.
While it might be possible to make a fit here, there is not a large enough base of work for that to seem efficient.
Norman suggests that we perceive designs on three levels:
While Norman’s discussions are interesting and bring up many issues, I think they beg a major point.
Learning is cyclic; today’s struggle is tomorrow's automaticity.
I think that the most efficient way to deal with this is that, when effects such as these (Zoo, EE) are encountered, address them by working explicitly to include knowledge about them as part of the desired learning outcomes. That is, establish cortex-based learning goals.
In other words, take this ‘off the table.’
In the case of the Zoo, perhaps teachers should indicate that scientists often are engaged with novel and interesting cases, but that ordinary cases require that same kind of study and observation.
In the case of the EE, it probably is important to stress that being able to simulate is a virtue because of both ease and cost, and to develop some activities that make this point.
In the case of teaching content with deeper emotional connections, the problem may be MUCH larger.
The notion of cognitive load sometimes is illustrated as follows.
I make three suggestions of how to think about these issues.
Add a factor called emotional load that takes up mental resources.
This would compete for working memory.
Perhaps some mental resources are “reserved” to deal with safety and security.
Perhaps the amount of resources thrown at a problem can be changed by emotional factors.
It probably is possible to test among these possibilities.
For example, learning is known to be most effective when the learner experiences a moderate amount of stress. No stress or too much stress are not as effective.
Of course, that’s deliberate practice.
Lower cognitive load. Adjust to available working memory. The less WM you need, the more effective your are likely to be.
Maybe the BEST way to address this is to say that motivational components compete for working memory slots.
Are we ready to begin making explicit consideration of emotional factors in instructional design?
Should we design and undertake experiments akin to those undertaken by Sweller (and Mayer)?