Follow-up on Dirksen's Proficiency-Sophistication Grid

I was interested in Dirksen’s Proficiency-Sophistication grid on Page 69 which uses Bloom’s taxonomy and Gery’s Proficiency scale: Familiarization, Comprehension, Conscious effort, Conscious action, Proficiency, Unconscious competence. While the levels seem somewhat obvious, the “Conscious effort” and “Conscious action” levels were somewhat confusing. I suppose it means, in the former, that there is a concentrated mental effort while performing the task, which is then supplanted by the conscious effort to perform the task without the same degree of mental effort. I was so interested that I ordered Gery’s Electronic Performance Support Systems (Used, it’s from 1991 without a digital version) hoping for an explanation of the levels of the scale. There was none. The scale appears on only one graphic:

So, I guess my guess at the meaning above will have to suffice. I think it’s important to have some idea of proficiency when describing learning objectives because the level of proficiency will dictate how much practice is required. In a lot of courses, one leaves without much confidence that one can do the job due to the lack of practice.

The National Institute of Health was the only other organization that used the terminology, “Proficiency Scale,” in their levels:

1. Fundamental Awareness (basic knowledge)
2. Novice (limited experience


Intermediate (practical application)
Advanced (applied theory)
Expert (recognized authority)

However, Gery’s graphic did provide a useful term to search with “Competency Curves,” which led to finding some other “proficiency scales” by other names. One is the Dreyfus Model of Skill Acquisition, which has five levels of increasing proficiency: Novice, Advanced beginner, Competent, Proficient, and Expert. There are also the four stages of competence: Unconscious incompetence, Conscious incompetence, Conscious competence, and Unconscious competence.

Competence is also impacted by the Dunning-Kruger effect, where unskilled individual overrate their ability.

Dirksen's Design for How People Learn - Part 3

I find myself wanting more nuance from Dirksen as the book goes on. I find myself saying, “Yes but…” more often, especially in Chapter 6. There is nothing factually untrue with what Dirksen, but I think her choice of emphasis risks playing into some common misconceptions (and, thus in itself, not causing enough “friction” to overcome those misconceptions). One common misconception is that students should never be told anything directly. In fact, that misconception is so pernicious that is was also addressed by Wiggins and McTeague in Understanding by Design in 2005 and by Hattie and Yates in Visible Learning and the Science of How We Learn in 2014. Dirksen comes close to reinforcing this misconception in her discussion of friction and stickiness. Sometimes, if students have sufficient background knowledge, that form of direct instruction is the most efficient use of students’ working memory. It is more of a call, based on the teacher’s pedagogical-content knowledge (PCK), as to when telling is most appropriate. Otherwise, cognitive load theorists would argue that processing is being wasted on extraneous cognitive load instead of germane cognitive load. Likewise, if one is not familiar with Clark and Meyer’s Engagement Matrix (also known with slight modification as “The Activity Grid”). Cognitive engagement does not require behavioural activity; students can be engaged psychologically without behavioural activity (“Principled Presentations”) or along with behavioural activity (“Principled Engagement”). This misconception can lead to some of the worst kinds of so-called “constructivist” lesson planning, where some behavioural activity with limited psychological engagement does not activate or stimulate learning.

Another common misconception is that all student struggle to learn is positive and equal. While some struggle or “friction” is good, there is an optimal level of arousal that must be achieved for learning to occur. As I noted last week, a stressed, upset, or frustrated student’s amygdala will likely not root information for higher-order processing. That principle has been known for over 100 years; it’s known as the Yerkes-Dodson Law, which found a curvilinear relationship between arousal and performance. At both the low end of arousal and the high end of arousal, performance degrades. Students need to stay within the range of good “friction.” Too much friction and learning will not be as efficient.

Finally, since I also brought up the concept of “attention span” last week parenthetically (“The brain must stop paying attention to new information (attention span) to replenish the executive function.”), I don’t think it was correct for Derksen to be so dismissive about attention spans, that the concept is “silly.” I don’t think it’s a particularly good comparison between a learning situation and watching a movie since as she has already previously explained why we remember stories better than information. Attention span is how long we can devote to thinking hard about something. And, yes, there will be variations in attention span from person to person, day to day, and topic to topic. And the general guideline is that one can concentrate and think hard for roughly the equivalent of one’s age in minutes. I think that’s a good guideline to use. If you say that the concept of attention span is “silly,” you run the risk of people misinterpreting that idea and thinking that hours-long lectures are productive. In fact, most of her suggestions for keeping learners’ attention are strategies for replenishing executive function and reinforcing schema building and consolidation.

Dirksen's Design for How People Learn - Part 2

Chapters 4 and 5 of Julie Dirksen’s Design for How People Learn covered how human memory works and principles for gaining and retaining attention. Dirksen covers the basics of human memory, including the different levels of memory—sensory, short-term (or working), and long-term—and types of memory—declarative, episodic, conditioned, procedural, and flashbulb. The one facet about human memory that Dirksen failed to mention that is particularly important is how long-term memory can act as an initial filter. That facet is one of the more important distinctions between the brain and the oft-used metaphor of a computer. The computer may store information for later use on the hard drive, like the brain encodes memories in long-term memory, but the information stored on the hard drive does not act like a filter for future information. Unfortunately, that is how the brain works. When confronted with new information that does not conform to our existing thinking, we either suffer cognitive dissonance (which can lead to learning as we try to make sense of the new information) or ignore the new information. The three videos on the Minds of Our Own site demonstrate this principle about why teaching often fails. One of the videos, for instance, on photosynthesis and trees, asks how trees build their mass. I have to admit that I was fooled by that one too, thinking that trees build their mass by pulling resources out of the soil. I knew that trees and plants absorb carbon dioxide but could not articulate that the carbon dioxide is converted into a tree’s mass. If you watch that video, “Lessons From Thin Air,” you will see a student do the exact same thing, completing a lesson without learning that gases have weight.

It is not just counter-intuitive information that can get missed. Because sensory memory must first be processed through the reticular activating system (RAS) and amygdala—the reptilian, lower, quick-response brain—new information that does not agree with an existing mental model may also cause an irrational reaction. Once again, the new information is not processed because it is perceived as a threat and can cause a stressed, even angry, response, no matter the veracity of the information. That also forms part of Dirksen’s metaphorical elephant.

Judy Willis, who is the source for the previous point on the RAS and amygdala, was a neurologist, who actually went back to school to became a teacher, because she was receiving so many referrals to check for behavioural problems in what turned out to be normal, healthy children. It turned out the schools were the problem, and that involves one more point that I wanted to make in parallel with Dirksen’s metaphor on the elephant and the rider that I don’t think Dirksen articulated well. While the human survival instinct is often labelled as the “fight or flight” response, Willis actually expands that to the fight/flight/freeze mechanism. That makes sense if we think of the “deer caught in headlights” phenomenon. The amygdala’s version of “freezing” in an educational context is “zoning out.” Thus, if a lesson is boring, the amygdala blocks the information from the prefrontal cortex (Dirksen’s rider) and “zones out.” This is one of two sources of “zoning out,” though. As Daniel Willingham says in the first chapter of Why Don’t Students Like School, “Humans don’t think very often because our brains are designed not for thought but for the avoidance of thought.” Thinking is hard work. The executive function of the prefrontal cortex will erode over time and lead as well to “zoning out.” The brain must stop paying attention to new information (attention span) to replenish the executive function. These “brain breaks” allow for the consolidation of the new information.  So, if the first case of “zoning out,” Dirksen’s metaphorical elephant has not been engaged. However, I think it’s also important to understand that no matter how much the elephant is engaged, the rider will still need to take breaks, the second case of “zoning out.”

Dirksen's Design for How People Learn - Part 1

I had to blog in a course about the readings week by week. I thought I would throw them up here to fill this blog more:

The first three chapters of Julie Dirksen’s Design for How People Learn was a fairly basic introduction. The book does not promote any fallacies, which is better than a number of publications. I was somewhat worried when she raised the topic of learning styles, but she accurately indicated that catering to learning styles has little credible research to support positive educational effects. As Dirksen indicates, though, thinking about learning styles and multiple intelligences can be beneficial during lesson planning. When she indicated that learning styles are popular, that reminded me of the time when I was simultaneously reading Maryellen Weimer defending learning styles while Hattie and Yates were devoting an entire chapter in Visible Learning and the Science of How We Learn to the following conclusion: “that there is not any recognized evidence suggesting that knowing or diagnosing learning styles will help you to teach your students any better than not know their learning styles” (176). Far from throwing babies out with bath water, as Weimer claims “higher education” has a tendency to do, I think there is a remarkably conservative nature to educational thinking, as Weimer demonstrates. In defense of learning styles, Weimer states that there is “one unarguable fact: People do not all learn in the same way.” Hattie and Yates call that fact “a simple and blatant truism” (176). So, other than as a general mental model that acknowledges different preferences in learning that is helpful during a design or lesson planning stage, learning styles do not appear to be a productive area of research.

The basicness of the introduction is not unwelcome. It provides a good opportunity to unpack my existing schemata around education, making the unconscious, conscious again. In many ways, my proficiency is at the level of “Unconscious competence,” and it is a useful exercise to pull that back down to the levels of “Conscious effort” and “Conscious action” to examine my existing schemata for accuracy and currency. In fact, placing sophistication and proficiency on an XY axis was the one thing that was new to me in the chapters. That appears to be a rather productive graphic to use when thinking through learning objectives.

The section in Chapter 2 of Design for How People Learn on student motivation, of course, made me mentally compare that section with John Keller’s Motivational Design for Learning and Performance: The ARCS Model Approach. Keller is one of the leading experts in learning and motivation, having been studying the topic for over 40 years. Keller’s ARCS model— dividing motivational components into Attention, Relevance, Confidence, and Satisfaction—has been around for a few decades. The part where a learning designer was disclaiming responsibility for learner motivation marks the same point that represents something that Keller added to his ARCS model in his 2010 publication. Keller expanded his ARCS model into the ARCS-V model. Keller added the concept of “Volition” to his previous model, to represent that aspect of learner motivation that the learning designer is referring to. As Dirksen says, one cannot force a learner to be motivated. For Keller, that volition is what the learner must bring to the learning situation and cannot be given. However, Keller would argue, and Dirksen would agree, that once that initial volition to learn is there, there are multiple ways that an instructor can increase motivation as well as demotivate learners.  

Thoughts on Fermi's Paradox

“Let’s say we have an ant hill in the middle of the forest. And right next to the ant hill, they’re building a ten-lane super-highway. And the question is “Would the ants be able to understand what a ten-lane super-highway is? Would the ants be able to understand the technology and the intentions of the beings building the highway next to them?”

The Fermi Paradox 

I woke up this morning and decided to reply to that post after several months...not sure why, other than I woke up with a certain phrasing around the issue, so this is not about eLearning...but I also hardly blog anyway...also responding to posts on Artificial Superintelligence Part 1 and Part 2 vaguely. 

Why that analogy? Imagine, instead, that your consciousness is an ant riding on the car riding on a ten-lane super-highway, instead of next to it. An integrated unit, much like your body. How would it seem to you? How would that over-consciousness communicate with you? Well, how do you communicate with an individual cell in your body? You don’t, consciously. Thus, you could well be involved, like our ants, in a system of consciousness that you can only glimpse in the slightest of anomalies or sensed at the odd moment in the mind. Do the individual cells of your body understand their role in the greater whole? If all of reality is what can be comprehended on the level of individual egos, are you not perhaps taking the same philosophical view as the ant or the individual cell in your body? What if the ego is a level of consciousness that is aware of its own existence but uncommunicative directly with other levels of consciousness? We are now deducing that there may be other parallel universes, so we are sensing an anomaly in our universe. Thus, we would have, on one hand, the “God Delusion,” and on the other, the “Ego Delusion.” Perhaps the best we can conceive at this level of consciousness is a space in between the two that inspires “goodness” regardless of one’s cosmology. The conception would mean that all the world religions are “anomalous experiences” for the ego that would indicate that there are higher levels of consciousness. That conception would also mean that individual anomalous experiences are also significant and not just tricks of the mind. That conception would also mean that the individual ego must not project its own self-concept onto its known universe. It could be likely that there is an actual planetary consciousness affecting individual egos and a universal consciousness affecting individual egos more distantly and then levels of consciousness beyond that equally self-aware and unable to communicate directly between levels. So, congratulations, you may already be immortal, just not how your ego wants to be. You can expand (or reject) the significance of this thought in any number of ways, obviously. Certainly, it cuts against the snobbery of modernity while perhaps acting as a fortification against the worst aspects of humanity that are only abetted by technology and perhaps an obvious bridge between so-called creationists and so-called evolutionists. It would also make our allegiance to any foundational belief or recognizable identity seem petty (“Oh, you think red ants are so superior to black ants? You’re an ant!”). Perhaps this would also explain Fermi’s paradox with an almost dismissive wave of the hand: why are you looking to the heavens and not studying the anomalies here for indications of greater levels of consciousness?

So, from this perspective, I’m actually not worried about Artificial Superintelligence (ASI) being achieved. We may be able to re-create the functioning of an ego via artificial intelligence, and that re-creation may teach us something about the limits of our own self-understanding. It may even create something greater than the ego’s self-conception, but for it to somehow start re-making the universe would mean essentially from our level of consciousness that we create a “God Machine.” I don’t think that is any more likely to happen in the near future than a horseman flying through the sky indicating a different apocalypse. 

Lots of people already think from this perspective. I just wanted to add my own musings in case my wording made sense to someone.