Bloom's Taxonomy vs the Model of Hierarchical Complexity

Introduction

Anyone who has spent time around education has seen Bloom's Taxonomy: the pyramid that runs from remembering up to creating. Far fewer people have heard of the Model of Hierarchical Complexity, the MHC, even though it is the more precise instrument for measuring thinking. They are often confused for each other, and they should not be, because they answer genuinely different questions. ¹

Bloom's Taxonomy classifies the type of cognitive task. Is the learner recalling, applying, analyzing, or creating? The Model of Hierarchical Complexity measures how complex a task is, regardless of its type, by examining how its parts are organized. ³

Both are useful. But for the specific job of measuring how someone thinks and tracking whether that capacity is growing, the MHC is the stronger tool, and it is the one Arq.training is built on.

What Bloom's Taxonomy is

Benjamin Bloom and colleagues published the original Taxonomy of Educational Objectives in 1956, sorting cognitive objectives into six levels: knowledge, comprehension, application, analysis, synthesis, and evaluation. ² In 2001, Lorin Anderson and David Krathwohl published a revision that turned the levels into verbs, Remember, Understand, Apply, Analyze, Evaluate, and Create, and added a second dimension for the type of knowledge involved, from factual to metacognitive. ¹

Bloom's enduring value is as a design tool. It helps teachers and trainers make sure they are not asking only for recall. If every question on an assessment lives at Remember and Understand, the taxonomy makes that visible and prompts the writer to push toward Analyze, Evaluate, and Create.

What it is not is a measurement scale. The levels are categories, not calibrated distances. Two Analyze tasks can differ wildly in difficulty, and a well-designed Apply task can demand more sophisticated thinking than a shallow Evaluate task. The taxonomy was never built to assign a precise difficulty value, and it does not.

What the Model of Hierarchical Complexity is

The MHC, developed by Michael Commons and Francis Richards, starts from a different goal: measuring the complexity of a task by its structure. It assigns each task an order of hierarchical complexity, a whole number, based on how lower-order actions are coordinated into higher-order ones. ³

Because the definition is formal rather than descriptive, the MHC produces something Bloom cannot: a calibrated scale. A task at a higher order genuinely requires coordinating more than a task at a lower order, by definition. That is what lets reasoning be scored reliably and lets growth be tracked, since you can observe a person moving from one order to the next. ⁴

The orders most relevant to advanced learning run from concrete and abstract through formal, then up to systematic reasoning, which coordinates multiple relationships into whole systems, and metasystematic reasoning, which compares and integrates whole systems. Most schooling targets the formal order; the harder real-world problems live above it.

The key differences, side by side

• Question answered. Bloom asks what type of thinking a task calls for. The MHC asks how complex the task is.

• Output. Bloom gives a category label. The MHC gives a number on a calibrated scale.

• Hierarchy. Bloom's levels are commonly read as a ladder, but they are not strictly ordered by difficulty. The MHC's orders are strictly ordered by structure. ³

• Scope. Bloom is built for educational objectives. The MHC applies to any task in any domain, for children or adults, because it is content-free.

• Use case. Bloom is excellent for designing questions and curricula. The MHC is built for measuring cognitive level and tracking development.

A useful way to remember it: Bloom tells you what to ask, the MHC tells you how hard the asking really is and what a response reveals about the thinker.

Using them together

These tools are not rivals. A thoughtful program can use Bloom to make sure it is asking for the full range of cognitive work, then use the MHC to measure the complexity of what learners actually produce.

In practice, that is close to how Arq operates. The design side draws on the spirit of Bloom by posing tasks that demand analysis, evaluation, and creation rather than recall. The measurement side draws on the MHC, reading the order of complexity in how a person reasons through the task rather than checking whether they reached a predetermined answer. ⁴

The reason Arq anchors measurement in the MHC is simple. The whole premise of the platform is that thinking can be made observable, measurable, and trainable. Measurement and tracking require a calibrated scale, and that is what the Model of Hierarchical Complexity provides and a category system, by design, does not.

Originally published on Arq.