Use the science of learning to design lessons, objectives, materials and assessments that actually change what people can do.

Instructional Design & How People Learn

## Why it matters

Most people teach the way they were taught: explain the topic clearly, show some slides, then move on. Learners nod along, the feedback forms say "great session", and a week later most of it is gone. Cognitive psychology has studied why this happens for more than a century, and it has found a few techniques that reliably make learning last. They are cheap, they work for beginners and experts, and almost every training course underuses them.

Throughout this course we follow one example: **a 90-minute workshop that teaches new junior developers to use Git** (commit, branch, merge and recover from mistakes).

## Learning is changing long-term memory

A useful working definition: learning is a lasting change in long-term memory that lets someone do something they could not do before. Two consequences follow.

- **Performance during the session is a poor guide.** Learners who just watched you run `git merge` can copy it immediately. That does not mean they can do it next Tuesday on their own. Robert and Elizabeth Bjork call this the difference between *performance* and *learning*.
- **Feeling fluent is not the same as knowing.** Re-reading notes or watching a demo again feels easy, so learners (and trainers) think it is working. Psychologists call this an *illusion of fluency*.

## Retrieval practice: make people pull information out

The **testing effect** is one of the best-replicated findings in learning science. In a well-known study, Henry Roediger and Jeffrey Karpicke (2006) had students read a passage and then either re-read it or take a recall test with no feedback. After five minutes the re-readers did slightly better. After one week the tested group remembered far more. Pulling an answer out of memory strengthens the memory itself; it is not just a measurement.

In practice, retrieval practice means low-stakes questions that make learners *produce* an answer:

- Start the session with three questions on last week's topic.
- After a demo, ask "write the command that creates a branch called `fix-login`" before showing it again.
- End with a one-minute "brain dump": write everything you remember about merging.

It works best when the questions are effortful but answerable, and when learners get the correct answer afterwards.

## Spacing: spread practice out

Hermann Ebbinghaus measured his own forgetting in the 1880s and found that memories fade quickly at first and then more slowly: the *forgetting curve*. He also found that repetitions spread over days beat the same number crammed together. A large meta-analysis by Nicholas Cepeda and colleagues (2006) confirmed this **spacing effect** across hundreds of studies.

For the Git workshop, that means not teaching everything in one block and never mentioning it again. Instead:

| When | What |
|---|---|
| Day 1 | Workshop: commit, branch, merge |
| Day 3 | Five-minute quiz in the team chat |
| Day 10 | Short task: fix a merge conflict in a practice repo |
| Day 30 | Review question in the onboarding check-in |

Each return trip is a retrieval opportunity, so spacing and retrieval combine.

## Interleaving and desirable difficulties

**Interleaving** means mixing different kinds of problems instead of practising one kind in a block. Doug Rohrer and Kelli Taylor (2007) found that students who practised mixed maths problems did worse during practice but much better on a later test, because they had to work out *which* method fits each problem. For Git, mix "undo the last commit", "switch branch" and "resolve a conflict" tasks rather than doing ten of each in a row.

Robert Bjork groups these techniques under **desirable difficulties**: conditions that slow learning down in the moment but improve long-term retention. The word *desirable* matters. A task so hard that learners cannot succeed at all is just frustrating.

## What the evidence ranks highly

John Dunlosky and colleagues (2013) reviewed ten common study techniques. They rated **practice testing** and **distributed (spaced) practice** as high utility, and highlighting, re-reading and summarizing as low utility. This is a good shortlist to remember when you design anything.

## Common mistakes

- **Treating quizzes only as grading.** Ungraded, frequent quizzes are a teaching tool.
- **Ending with a recap you deliver.** Ask learners to produce the recap instead.
- **Judging success by the mood in the room.** Smiling, nodding learners are not proof of learning. Check what they can do later.
- **Massing everything into one day** because the calendar is easier. Add at least one spaced follow-up.

## Quick recap

- Learning is a lasting change in long-term memory, not good performance in the session.
- Retrieval practice: make learners recall, then give the answer.
- Spacing: return to material over days and weeks.
- Interleaving and other desirable difficulties feel harder but last longer.

## Try it

Take a lesson you have taught or attended. On the Do tab, answer the quiz, then write down one retrieval question you could add at the start and one spaced follow-up you could schedule.


Use retrieval practice, spacing and interleaving so learning lasts beyond the session.

Learning science essentials

Study Strategies: Retrieval Practice

Study Strategies: Spaced Practice

## Why it matters

New learners often get lost not because the topic is too hard, but because the lesson asks them to juggle too much at once: a new tool, new vocabulary, a busy slide and a fast talker. Cognitive load theory explains why, and it gives you concrete ways to simplify a lesson without dumbing it down.

## Working memory is small

Information you are actively thinking about sits in **working memory**, which can hold only a handful of new items at a time. George Miller (1956) famously suggested "seven, plus or minus two"; later work by Nelson Cowan (2001) suggests about four chunks for new material. **Long-term memory**, by contrast, is huge. Experts are not people with bigger working memories; they have organized knowledge in long-term memory (schemas) that lets them treat many details as one chunk.

When you type `git commit -m "Fix login"`, you see one action. A beginner sees a program name, a sub-command, a flag, quotation marks and a message, each of which needs attention.

## Three kinds of load

John Sweller developed **cognitive load theory** in the late 1980s. It separates:

- **Intrinsic load**: the difficulty that comes from the material itself, such as how many elements interact. Merging branches has more interacting parts than making a commit.
- **Extraneous load**: effort spent on things that do not help learning, such as decoding a cluttered slide, hunting for the right window, or reading text while also listening to different words.
- **Germane load** (in older versions of the theory): effort spent building schemas. Recent versions fold this into intrinsic load, but the idea remains: you want learners' effort going into the material, not into the packaging.

The design rule is simple: **cut extraneous load, and manage intrinsic load by sequencing**.

## The worked example effect

For novices, studying a fully worked example usually beats solving the same problem unaided. Sweller and Graham Cooper (1985) showed this with algebra, and it has been replicated many times. A beginner solving a problem from scratch spends working memory searching for a method; a worked example shows the method, so the effort goes into understanding it.

A worked example for Git might look like this:

```bash
# Goal: start new work without touching main
git switch main          # 1. start from the up-to-date main branch
git pull                 # 2. get the latest changes
git switch -c fix-login  # 3. create and switch to a new branch
# ...edit files...
git add login.js         # 4. stage the change
git commit -m "Fix login redirect"  # 5. record it
```

Each step has a comment saying *why*, not just what. Richard Catrambone's work on **subgoal labels** found that naming the purpose of each group of steps helps learners transfer the method to new problems.

### Fading

Do not stop at examples. Alexander Renkl and colleagues found that **faded worked examples**, where later examples leave out more steps for the learner to fill in, ease the move to independent problem solving. Example 1 is complete; example 2 leaves out step 3; example 3 leaves out steps 3 to 5; then learners solve a full problem.

## The expertise reversal effect

What helps novices can hurt experts. Slava Kalyuga and colleagues (2003) found that detailed guidance which benefits beginners becomes redundant for more experienced learners and can even make their learning worse. Experienced developers in your Git workshop will be bored and slowed by step-by-step examples of `git commit`; give them problems sooner. This is one reason a quick pre-assessment is worth the time.

## Simplifying a lesson: a before and after

**Before:** one slide with eight bullet points about branching, a screenshot of a terminal in small font, a live demo in a different window while the trainer explains merge strategies, and then "now try it on your own project".

**After:**

1. One idea per slide: "A branch is a movable label on a commit", with a single simple diagram.
2. Demo in a large font, in the same window learners will use, narrating only what is on screen.
3. A worked example handout with subgoal labels.
4. Two faded examples in a practice repository with no other files to distract.
5. Only then: try it on your own project.

Same content, far less load.

## Other load reducers

- **Split attention:** put labels on the diagram instead of in a legend learners must match up.
- **Redundancy:** do not read out the exact text on a slide while learners read it; say it *or* show it.
- **Pre-training:** teach key terms (commit, branch, HEAD) before the process that uses them.
- **Segmenting:** break a long demo into short chunks with a pause and a question between each.

## Common mistakes

- Making slides "engaging" with decorative images, animations and background music that add extraneous load.
- Giving novices open-ended discovery tasks before they have any schema to work with.
- Keeping detailed scaffolding for learners who no longer need it.

## Quick recap

- Working memory is tiny for new information; long-term memory is not.
- Cut extraneous load; sequence intrinsic load.
- Novices learn well from worked examples, then faded examples, then problems.
- Guidance should shrink as expertise grows (expertise reversal).

## Try it

Answer the quiz on the Do tab, then pick one slide or handout you have made and list two things you could remove or move to reduce extraneous load.


Use cognitive load theory and worked examples to simplify a lesson without dumbing it down.

Cognitive load

Teaching Strategies: Cognitive Load Theory

Teaching Strategies: Worked Examples

## Why it matters

Technical trainers mostly teach adults: new hires, customers, career changers and experienced engineers learning a new tool. Computer science teachers face a different mix of teenagers and adult learners. Both groups share the same basic memory system, but they bring different prior knowledge, motivation and constraints. Adapting a lesson to the people in the room matters more than any single technique.

## Andragogy: Knowles's assumptions

Malcolm Knowles popularized the term **andragogy** in the 1970s to describe how adults learn, in contrast with pedagogy for children. His assumptions are that adults:

1. **Need to know why** they are learning something before they invest effort.
2. **Have a self-concept** as responsible for their own decisions and dislike being treated like children.
3. **Bring experience**, which is both a resource and a source of habits that can get in the way.
4. **Are ready to learn** what helps them with real tasks and roles.
5. **Are problem-centered** rather than subject-centered.
6. **Are motivated more by internal factors** (competence, satisfaction) than external ones.

These are useful design prompts. A fair caution, though: andragogy is a set of assumptions rather than a tested theory, and research has not shown that adults learn in a fundamentally different way from children. Much of it is simply good teaching for anyone. Treat it as a checklist, not a law.

## Motivation: autonomy, competence, relatedness

**Self-determination theory**, developed by Edward Deci and Richard Ryan, has stronger research support. It says people are more motivated when three needs are met:

- **Autonomy**: some choice and control (which practice task, which project).
- **Competence**: early, visible success at an appropriate challenge.
- **Relatedness**: feeling connected to the trainer and to other learners.

For the Git workshop: explain in the first two minutes how Git will be used on their team (why); let them choose between two practice scenarios (autonomy); make sure everyone completes a first commit within ten minutes (competence); and pair people up for the merge-conflict exercise (relatedness).

## Novices versus experienced learners

The biggest difference in any adult group is usually **prior knowledge**, not age. The expertise reversal effect from the last lesson applies directly:

| Learner | What they need |
|---|---|
| Career changer who has never used a terminal | Pre-training on terminal basics, worked examples, short steps, lots of reassurance |
| Developer who has used Git through an editor | Mapping from buttons they know to commands, then problem practice |
| Senior engineer from another version control system | A quick comparison, then advanced tasks (rebasing, recovering lost commits) |

A five-question pre-assessment before the session lets you group people or offer a "fast track" handout.

## Experience as a resource and an obstacle

Adults have **misconceptions** built from experience. Someone who used shared network folders may think of Git as "a place to save files" and be confused when a commit does not appear on the server. Ask learners to predict what will happen before you show it, and listen to the wrong predictions: they tell you which mental model to fix.

## A myth to drop: learning styles

Many trainers were taught to diagnose learners as "visual", "auditory" or "kinesthetic" and to match teaching to their style. In 2008 Harold Pashler, Mark McDaniel, Doug Rohrer and Robert Bjork reviewed the evidence and found no adequate support for this "meshing" idea: studies with the right design did not show that matching instruction to a preferred style improves learning. Later studies have found the same.

People do have preferences, and using several modes (a diagram *and* an explanation) helps everyone, but that is because of how the material is best represented, not because of learner types. Design for the content and for prior knowledge, not for learning-style labels.

## Practical constraints of adult learners

Adults in workplace training often:

- are interrupted by work messages, so short segments and clear "where we are" signposts help;
- have limited time, so practice should use realistic tasks they will face next week;
- fear looking incompetent in front of colleagues, so make early mistakes safe (practice repositories that can be thrown away, anonymous polls).

## Adapting a lesson: example

**Original:** a 90-minute lecture on Git internals, then commands, then a quiz.

**Adapted for adult new hires:** start with a two-minute story of a real merge conflict on the team and what it cost; quick pre-assessment; worked example in a throwaway repo; choice of two practice scenarios; pairs resolve a conflict; finish with a checklist they can pin next to their monitor and a spaced follow-up task on day 3.

## Common mistakes

- Skipping the "why" because it seems obvious to you.
- Teaching to an imagined average learner instead of checking prior knowledge.
- Labelling learners by learning style.
- Treating experienced learners' questions as interruptions instead of resources.

## Quick recap

- Knowles's assumptions are useful prompts, not a proven separate theory.
- Support autonomy, competence and relatedness.
- Prior knowledge matters more than age; adjust guidance to it.
- Learning styles matching is not supported by evidence.

## Try it

Answer the quiz on the Do tab. Then think of a group you might teach and write one sentence each on why they need the topic, what they already know, and one choice you can give them.


Adapt a lesson for adults by building on prior knowledge, motivation and real tasks, and drop the learning-styles myth.

Adult and novice learners

The 6 Principles of Andragogy (Explained in 3 Minutes)

The Biggest Myth In Education

What cognitive science says about memory and motivation, and how to use it: retrieval, spacing, cognitive load and adult learners.