Oct. 22, 2026

Last time, I argued that Bloom’s Taxonomy does NOT necessarily represent progressively advanced levels of learning or understanding.

For one thing, a task in the Remembering/Knowledge level could be extremely sophisticated (e.g., recall the names of all bones in the human body, as well as the origins of the names). Similarly, a task in the Evaluating level could be relatively basic (e.g., judge whether an x-ray depicts a broken bone).

For another, regardless of the complexity of the task at hand, the person’s extent of understanding can fall anywhere along a continuum from shallow to deep…

Like the ocean, shallow areas of understanding can be easily spotted from the surface; a few penetrating questions can usually reveal that someone doesn’t really know what they’re talking about.

In contrast, deeper understanding is harder to gauge. Even when we can recognize that someone’s understanding isn’t shallow, the actual depth remains murky—as does the maximum possible depth in the topic, if there even is such a thing.

Due to the way our brains work, we generally opt for shallow understanding. We tend to want the minimum viable understanding in order to be able to move on or apply the information.

Consequently, a person could demonstrate “understanding” in all six levels of Bloom while still possessing very little depth of understanding in the relevant subject matter.

To show this is true, consider anyone who has an advanced degree, yet understands very little of the subject with any depth. (I’ll give a generic example in a minute.)

If you’re not familiar with academia, this may sound preposterous. But the truth is that many learners adopt a “learn-to-get-by” mentality, not a “learn-to-understand” mentality, and at many institutions that is enough to obtain an advanced degree.

We can speculate about why this is the case, but that’s not the point here.

Consider the fictional example of John Smith, who is a mashup of a variety of real people…

John majored in psychology and statistics during college, then went on to get a masters degree in the social sciences with a quantitative specialization.

(By the way, none of what follows is John’s fault, exactly—except insofar as he failed to claim responsibility for his own learning. The broader problem lies in how humans naturally think and how certain educational systems are designed.)

What John was taught—and in some sense mastered—was a cookbook-style overview of statistics, where flowcharts guided all decision points and AI helped write code to conduct all analyses.

For example, if a homework assignment asked John to compare the means of two independent groups, he looked at his flowchart and found that it instructed him to conduct an “independent samples t-test.” (In preparation for closed-book exams, John even memorized this If–Then rule in a basic pattern-matching way.)

The AI confirmed the use of a t-test and provided R code to produce summary statistics [e.g., mean() ] and test results [e.g., t.test() ]. The results were interpreted according to complicated decision rules about what the numbers do or do not mean.

Depending on the program of study, John might also have learned about statistical assumptions, which are in turn evaluated with their own bevy of procedures and decision rules.

John followed all required steps, submitted the requested information, and was awarded an “A.” Many such instances, across a variety of different problem scenarios and respective procedures, resulted in successful graduation from the program.

Notice that all six levels of Bloom’s Taxonomy have been addressed many times over. For example…

1. Remembering/Knowledge: John accurately recalled the names of the appropriate statistical tests for a variety of data analysis situations, as well as the associated R functions to run the rests.

2. Understanding/Comprehension: He successfully paraphrased various statistical concepts (e.g., a 95% confidence interval was described as, “The interval within which the population parameter would fall in 95% of repeated samples.”)

3. Applying: He correctly ran countless analyses on homework assignments and exams according to flowcharts and decision rules.

4. Analyzing: He parsed and interpreted statistical output (e.g., effect sizes were extracted and interpreted according to Cohen’s guidelines; p-values were extracted and interpreted as “statistically significant” or not according to an α = .05 threshold).

5. Evaluating: He scrutinized published research papers during class sessions to determine (a) whether the authors ran the correct analyses; (b) if the reported results are correct; and (c) whether the conclusions align with the results.

6. Creating/Synthesis: He conducted his own research projects, analyzing the results and perhaps even publishing them in scientific journals!

Despite all this, a few penetrating questions might cause it all to fall apart like a house of cards.

• Why does the flowchart say to run an ANOVA rather than a t-test? Why is one test “right” and the other “wrong”? Why is that rule the rule?
• Where does that p-value come from? Why does it tell us anything about how to think about the results? What’s the difference between p = .0499 and p = .0501?
• If we were feeling particularly cruel, we might ask basic questions about degrees of freedom—a concept John would have learned about in the first week or two of the program, but which is rarely understood by either students or instructors!

Even if a response to any of these questions could be mustered, a follow-up question of, “But what does that mean?” would probably be the death blow.

Why would these basic questions stump John?

Because the only depth that has been established is with respect to the flowcharts and decision rules, not the why and how of statistical mechanics. (And certainly not the underlying phenomena being studied.)

So much of learning is skimming along the surface, weaving a web of tenuously connected concepts, where much of it is accepted without question.

Again, this isn’t because John is stupid or unmotivated (necessarily). It’s because he’s faced with an infinite barrage of information from every direction, and he needs some way to prioritize and get through it all. He’s probably not even consciously aware he’s doing this.

But this is what he’s doing if the answer to the question, “Why?” is, “Because that’s the rule.”

Why do we care? If John can get the correct results, then does it really matter?

It DOES matter, because this flimsy kind of understanding tends to fall apart in real-world analytic situations, which call not for flowcharts but judgment and nuance.

Additionally, superficial understanding is more easily replaced by AI than deep understanding is, so there’s not much job security for John in the current age.

Now, I know it may seem like I’m being really mean to John in this post…

But the truth is that we are ALL John.

We all have certain domains of knowledge in which we don’t really understand what’s going on with much depth, even if we’ve completed high school, college, trainings, etc.

In many domains we don’t need deep understanding.

I’m worse than John when it comes to car engines. I don’t even know where to start. But things have pretty much worked out so far, because that’s not my area.

The question is, What’s the domain (or two or three) where you ARE going to have deep understanding?