The Lost Blueprint of Learning

The 40-Year-Old Blueprint That Solves the Biggest Mistake in Lesson Design

As educators, we make thousands of decisions a day. But our most critical choice often happens before the morning bell even rings:

How exactly am I going to teach this specific objective?

When a lesson fails to land, our instinct is usually to search for something new. A new activity. A new framework. A new idea that promises to engage students in ways our current approach did not.

Lately, I’ve been wondering if we are actually wasting our time trying to invent new solutions when we would be much better off dusting off the old ones. Perhaps we are suffering from collective amnesia.

Decades ago, some of the most careful thinkers in instructional design laid out remarkably precise blueprints for effective teaching. Brilliant minds like Robert Gagne, Madeline Hunter, Thomas Gilbert, Robert Mager and of course Sigried Engelmann wrote about effective teaching decades ago, yet their work is rarely discussed today.

As Trina Spencer (2021) pointed out, what makes these highly structured instructional models so powerful is that they do not just teach rote facts; they are specifically designed to establish generative repertoires efficiently. They build the exact foundations students need to tackle complex, novel problems.

In this article, I’m going to unpack one of those great lost treasures. It comes from a 1983 book called, Analyzing Instructional Content: A Guide to Instruction and Evaluation by Phillip W. Tiemann and Susan M. Markle, and it might just change the way you look at every lesson you teach.

Last year, Dr. Russ Fox nonchalantly popped an original copy of the book in front of me and I’ve been obsessed with it ever since. I’ve only been able to get access to an electronic copy of the book on Internet Archives (heads up, it’s not a simple read).

The Problem

For years, I went into lessons hoping that what I had planned would land. My lesson preparation would be based on the content that I was teaching, but lacked purpose behind how I was going to teach it. If a lesson failed, I would blame it on a lack of engagement, without having an understanding of the fact that it may have had more to do with my poorly engineered lesson structure.

In education, not many people think about how we might need to teach different types of learning differently. We just think if it's something new we need to use explicit instruction (well, that’s what I’ve always thought). We treat a spelling rule, a historical fact and a complex maths problem like they require the exact same instructional moves. They don’t.

Tiemann and Markle explain how learning can be broken down into distinct categories and each category demands a completely different teaching vehicle.

The Emotional Underpinning

If you look at Tiemann and Markle’s original taxonomy (below), they don’t just divide learning into cognitive and physical tasks. They argue that there is a foundational layer sitting beneath every single lesson we teach: Emotional Learning.

Every time a student is in your classroom, emotional learning is taking place. They are not just learning how to solve a fraction or write a sentence, but they are learning whether to feel curious, confident, frustrated or fearful about the subject. Tiemann and Markle point out that this isn’t just a byproduct of learning, rather it is an active, learned response. A student who happily volunteers in music class might shake with nerves when called upon in history.

Why does this matter for instructional design? Because when we mismatch our teaching tactics to the type of learning required—when we ask a student to “discover” a complex strategy without first building their factual fluency, or drill them on a concept they should be analysing—we breed frustration (this is why moments of genuine insight must be engineered rather than hoped for). We inadvertently cause students to learn avoidance behaviours. But when we align the instructional vehicle perfectly with the cognitive architecture of the task, the friction disappears.

Types of Learning

Tiemann and Markle mapped out the different types of learning in the diagram below. It is incredibly comprehensive, but for the sake of making this as practical as possible for the classroom, I have distilled it down to what I feel are its core components (with apologies to Tiemann and Markle, hopefully they aren’t turning in their graves).

Tiemann, P. W., & Markle, S. M. (1983). Analyzing instructional content. Champaign, IL: Stipes.

The Types of Learning that I will unpack in this article are:

1. Psychomotor

2. Simple Cognitive (Facts and Procedures)

3. Complex Cognitive (Concepts, Principles/Rules and Strategies)

*And no, we are not talking about “Learning Styles” (Visual, Auditory, Kinesthetic). Those are a myth. We are talking about the Types of Learning.

1. Psychomotor (The Physical Execution)

“The attainment of complex cognitive learning can only be demonstrated through psychomotor responses which students must learn first.” Tiemann & Markle, 1983

This is all about the physical body, including the vocal tract. It’s not about deep understanding; it’s about knowing how to physically move your muscles, limbs or mouth to produce a desired result.

Photo by Towfiqu barbhuiya on Unsplash

Why it is Necessary

Cognitive knowledge is useless if the physical execution fails. More importantly, a lack of psychomotor fluency can completely mask what a student actually knows (Binder, Haughton and Van Eyk, 1990). For example, we might think a student is struggling with their multiplication facts during a timed test, when in reality, the barrier is their lack of handwriting fluency. They simply cannot physically write the numbers fast enough. Or we might want them to partition a large number, but because they haven’t mastered the physical skill of aligning their numbers in straight columns, the entire maths problem falls apart. Psychomotor learning bridges the gap between knowing what to do and physically executing it effortlessly.

Examples

• Holding a pencil correctly with a tripod grip.

• Physically placing the tongue behind the top teeth to correctly articulate the ‘th’ sound.

• Performing a standard layup in basketball.

The Mistake

Talking too much or teaching “words about a concept” instead of the movement itself.

How to Teach it in Five Simple Steps

1. Model the complete chain at full speed: Show them the final product first so they know exactly what the fluent pperformance looks and/or sounds like.

2. Model the chain slowly, step-by-step: Break the movement down into its smallest teachable parts.

3. Highlight the critical physical cues: Direct their attention to specific body parts.

4. Maximise practice opportunities: Eliminate passive waiting or “standing in line”. Set up the room or the routine so that every student is getting high-rate physical or vocal practice simultaneously.

5. Provide feedback: Provide immediate physical or verbal correction. Do not let them practice mistakes; guide their movement until the execution becomes automatic.

2. Facts (The Factual Foundation)

“Memorising often is looked upon as a low and therefore unworthy kind of learning. But when people become experts - with all the facts at their fingertips, we take a different view of their ability." Tiemann & Markle, 1983

Some people might not like hearing this, but this is rote memorisation. The student needs to reproduce an exact fact fast. There is no room for creativity here. It is simply stimulus and response: when you see “b”, your verbal response needs to be “bee”.

Why it is Necessary

Facts are the raw materials of thought. Fluency in factual recall is necessary because it frees up working memory (Poncy et al., 2007). If a student is using all their cognitive capacity to remember what 6 x 7 is they have no brain power left to solve the actual multi-step word problem. When knowledge is not fluent, thinking becomes effortful in ways that are often invisible to teachers.

Examples

• Recalling that 6 x 7 = 42.

• Naming the capital cities of Australia.

• Identifying the chemical symbol for Potassium (K).

• Recalling the exact year World War II began.

The Mistake

Asking leading questions to correct a rote fact or using discovery methods for simple associations.

How to Teach it in Five Simple Steps

1. Ensure accuracy first: Do not test before you teach. Heavily prompt the correct answer on day one.

2. Complete timed practice: Use 1-minute timings to build high-rate, effortless responding.

3. Do it everyday: Consistency is required to move from accuracy to automaticity.

4. Track progress: Have students track their own correct and error rates.

5. Respond to data: Compete against yourself, not others, and adjust based on the numbers.

3. Procedures (The Rigid Sequence)

"Algorithms are sequential sets of rules or steps that generate correct solutions or products for all problems of a given type." Tiemann & Markle, 1983

If facts are a single stimulus and response, procedures are a chain of them. This is the realm of the algorithm. We aren’t asking students to invent a new way to solve a problem; we are asking them to follow a predetermined, step-by-step procedure flawlessly. Doing step three before step two means the whole thing falls apart.

Why it is Necessary

Procedures automate multi-step processes so students don’t have to reinvent the wheel every time they encounter a standard problem (Gagné, 1984). It builds reliable pathways for executing standard tasks accurately every single time.

Examples

• The standard algorithm for long division (Divide, Multiply, Subtract, Bring down, Repeat).

• Following a rigid proofreading checklist (Check for a capital letter, check for finger spaces, check for a full stop)

• Lighting a Bunsen burner

• Completing the steps to label the parts of a plant from a diagram

The Mistake

Showing the entire 15-step process once and expecting the student to replicate it flawlessly from memory.

How to Teach it in Five Simple Steps

1. Break it into discrete links: Do a task analysis. Write down every single micro-step of the procedure so you know exactly what the chain looks like.

2. Choose your direction: Decide if you are using forward chaining (teaching step one first) or backward chaining (doing everything for the student except the final step, so they get the immediate win).

3. Isolate the target step: Model and heavily prompt just the single step you are currently teaching. Do not overwhelm them with the whole sequence at once.

4. Demand mastery of the link: A student must be able to execute the current step flawlessly before you introduce the next one.

5. Link the chain: Once the single step is secure, attach it to the next step. Practice them together until the entire procedure is a single, unbroken routine.

4. Concepts (The Mental Filing System)

“A definition may be a good place to start, but it is a poor place to stop (when analysing a concept).” Tiemann & Markle, 1983

Now we are moving away from simple recall and just asking students to reproduce exactly what we showed them. A concept requires a student to classify completely new examples that they have never seen before.

Why it is Necessary

Concepts build the mental filing system. They allow students to organise a chaotic world into neat categories. Without concept mastery students are just memorising isolated instances rather than understanding underlying properties. As Twyman (2021) highlights, the goal of this careful sequencing is 'faultless communication'—designing and arranging the examples so perfectly that the learner is left with only one possible logical interpretation of the concept.

Examples

• Recognising mammals among unfamiliar animals

• Deciding whether an unfamiliar shape is a polygon

• Identifying whether a newly presented number is prime or composite.

• Spotting metaphors in a previously unseen poem.

The Mistake

Only showing standard, typical examples. If you only show Golden Retrievers, students will think a "dog" must be fluffy and golden.

How to Teach it in Five Simple Steps

These steps also lean heavily on the work of Engelmann and Carnine (1991) as they are the ones who perfected the architecture of “rational sets” and “faultless communication”.

1. Identify “Must Have” Features: Clearly define the critical attributes of the concept.

2. Identify “Can Have” Features: Identify the irrelevant noise that changes from example to example.

3. Juxtapose “Close In” Non-examples: Show a non-example that almost fits the rule to sharpen discrimination.

4. Juxtapose “Maximally Different” Examples: Show wildly varying examples that still fit the “Must Have” features.

5. Unpredictable Testing: Test with novel examples not used during teaching.

5. Principles (The Predictive Rule)

"More complex than concept learning is learning to apply principles. A principle is a statement that sets forth a relationship between two or more concepts... a student demonstrates mastery of a principle only when applying that principle in a new situation.” Tiemann & Markle, 1983

If concepts allow us to name the world, principles allow us to predict it. This is the realm of cause and effect. We aren’t just asking students to categorise isolated things anymore, but we are asking them to apply a reliable rule to see how those things interact in entirely new situations. It is the fundamental understanding that if a specific condition is met, then a specific, predictable action must follow.

Why it is Necessary

Principles move students from just categorising things to predicting how things interact. They provide the “cause and effect” rules of the universe allowing students to navigate new situations reliably.

Examples

• Applying the silent e (CVCE) spelling rule

• Applying the rule for subject–verb agreement in unfamiliar sentences

• Using the distributive property to solve new multiplication problems

• Applying the rule of buoyancy to predict if an object will float.

The Mistake

Stating the rule, providing two perfect examples and moving on.

How to Teach it in Five Simple Steps

1. Check the component concepts: A student cannot apply a rule to a concept they don't understand. Verify they can classify the concepts before you ask them to predict how they interact.

2. State the “If-Then” Rule Explicitly: Tell them the exact cause and effect relationship.

3. Demonstrate Across Varied Examples: Show the rule working in multiple contexts.

4. Throw the Curveball: Introduce situations where the “If” condition is NOT met.

5. Test with the Unknown: Assess application in entirely new contexts.

6. Strategies (The Uncharted Territory)

“Strategies are also ways of dealing with new situations - situations not faced before by the learner... That person is not behaving in a trial-and-error fashion, but has learned a rational strategy to be applied.” Tiemann & Markle, 1983

If principles give us reliable rules for predictable situations, strategies are what we use when those rules aren’t enough. This is the realm of ambiguity. We are no longer dealing with neat textbook examples that have a single correct answer. We are asking students to attack messy, completely novel problems where they have to monitor their own thinking, choose an attack path and pivot when their first attempt fails.

Why it is Necessary

The real world rarely hands us perfectly structured procedures. We need to equip students to handle ambiguity. Strategy instruction prepares students to monitor their own thinking, change course when a plan fails and tackle problems that don’t have a textbook answer (Gagne 1984).

However, we cannot expect students to successfully navigate this ambiguity if they are still struggling with the basics. Johnson and Layng (1992) demonstrated that true fluency in foundational tool skills is what actually allows completely novel problem solving strategies to emerge. If a student is using all their working memory just to recall a basic maths fact or decode a simple word, they have no cognitive space left to evaluate their own thinking or adapt to a curveball. Strategy instruction is vital but it relies entirely on the fluent foundations built in the earlier stages.

Powell, S. R., Benz, S. A., Mason, E. N., & Lembke, E. S. (2022). How to structure and intensify mathematics intervention. Beyond Behavior, 31(1), 5-15.

Examples

• Solving an unstructured multi-step maths word problem (UPSCheck: Understand, Plan, Solve, Check).

• Planning how to structure a narrative (CSPACE: Characters, Setting, Purpose, Action, Conclusion, Emotion)

• Summarising a dense informational text where the main idea isn’t explicitly stated (RAP: Read a paragraph, Ask yourself what it’s about, Put it in your own words).

The Mistake

Throwing students into the deep end with a complex task and saying "give it a go" or “just keep at it,” hoping a brilliant problem-solving strategy will magically emerge from the struggle.

How to Teach it in Five Simple Steps

1. Verify fluency in tool and component skills: Students cannot employ a strategy if they lack the foundational facts.

2. Introduce Ill Structured Problems: Give them messy problems without obvious solutions.

3. Equip with Problem-Solving Repertoires: Provide a reliable attack strategy to navigate the unknown.

4. Shift to Heuristic Questioning: Move from giving answers to asking questions that keep minds moving.

5. Demand Justification: Require students to explain their reasoning and strategy choice.

Links to the Instructional Hierarchy

When teaching any type of learning and it is new for students, they are right back in Acquisition. However, once acquired we can think about the different types like this:

• Psychomotor skills, Facts and Procedures are worth building fluency in

• Concepts and Principles enable us to generalise our knowledge to new contexts

• Strategies give us a way to adapt and appy what we know

Aligning teaching with learning

Perhaps the most powerful implication of Tiemann and Markle’s work is also the simplest.

Not all learning is the same. Therefore, not all teaching should look the same.

When instructional tactics are chosen without regard for the type of learning required, lessons become inefficient and sometimes counterproductive. Students may appear disengaged or incapable when the real issue is a mismatch between task demands and instructional design.

Recognising different types of learning allows teachers to act with greater precision and confidence. It shifts lesson planning from hopeful experimentation towards deliberate engineering.

Key takeaways

• Learning can be categorised into distinct forms, each requiring different instructional approaches

• Emotional responses to learning are shaped by repeated experiences of instructional success or failure

• Factual and procedural fluency are essential foundations for conceptual and strategic thinking

• Effective teaching involves aligning methods with the cognitive architecture of the task

If we want teaching to feel less uncertain and more predictable, we do not necessarily need more innovations. We already have the blueprint! We do not need to constantly reinvent our teaching methods. The research and structures for highly effective instruction have existed for decades. A comprehensive half-century meta-analysis by Stockard et al. (2018) proved conclusively that these highly structured, direct instructional models produce vastly superior outcomes across almost every measure.

We may simply need to rediscover the blueprints that have been there all along.

References

Binder, C. V., Haughton, E. and Van Eyk, D. (1990) ‘Increasing Endurance by Building Fluency: Precision Teaching Attention Span’, Teaching Exceptional Children, 22(3), pp. 24–27.

Engelmann, S. and Carnine, D., 1991. Theory of instruction: Principles and applications. Eugene, OR: ADI Press.

Gagné, R. M. (1984) ‘Learning Outcomes and Their Effects: Useful Categories of Human Performance’, American Psychologist, 39(4), pp. 377–385

Johnson, K.R. and Layng, T.V.J., 1992. ‘Breaking the structuralist barrier: Literacy and numeracy with fluency’, American Psychologist, 47(11), pp. 1475-1490.

Poncy, B. C., Skinner, C. H., & Jaspers, K. E. (2007). Evaluating and comparing interventions designed to enhance math fact accuracy and fluency: Cover, copy, and compare versus taped problems. Journal of Behavioral Education, 16(1), 27-37.

Spencer, T.D., 2021. ‘Ten instructional design efforts to help behavior analysts take up the torch of direct instruction’, Behavior Analysis in Practice, 14(3), pp. 816-830.

Stockard, J., Wood, T.W., Coughlin, C. and Rasplica Khoury, C., 2018. ‘The effectiveness of direct instruction curricula: A meta-analysis of a half century of research’, Review of Educational Research, 88(4), pp. 479-507.

Tiemann, P. W. and Markle, S. M. (1983) Analyzing Instructional Content: A Guide to Instruction and Evaluation. 3rd edn. Champaign, Illinois: Stipes Publishing Company.

Twyman, J.S., 2021. ‘Faultless communication: The heart and soul of DI’, Perspectives on Behavior Science, 44(2), pp. 1-13.