Ten Simple Rules for Creating an Effective Lesson
I’m preparing a couple of talks based on Teaching Tech Together, and this seems like a useful topic to cover. Updated notes are below; I’d be grateful for feedback by email. (I’ve disabled comments on this site until the trolls find someone else to pester.)
Introduction
- There are many kinds of lessons, formal and informal, from seconds long to lifelong
- This guide is about prepared instructional plans and content for half an hour to a day
A Little Bit of Theory
- Psychology of learning
- Load short term memory
- Keep it there long enough to transfer to long term memory
- Create links so that knowledge is retrievable (bigger obstacle than outright retention)
- Goal for novices is to build a mental model
- Following a path through a concept map helps ensure connectedness
- Naming subgoals help
- Cognitive load theory points out that learners are trying to:
- Recall factual knowledge
- Choose a solution strategy
- Formulate a response
- Use tools,
- So teach these all separately and synthesize as a lesson in its own right
- While we’re here, a few myths:
- Mindset and stereotype threat are probably not as important as initially thought (this is how science progresses)
- VAK learning styles
- The “Pyramid of Learning”
- And teaching evaluations are suspect, as are self-assessments
1. Write Learner Personas to Define Your Audience
- A lesson is a user interface for knowledge, so borrow methods from user interface design
- You are not your learners (expert blind spot)
- General background
- What they already know
- What they think they want to know
- What they actually need to know in order to achieve their aims (which may be different from what they think)
- Special considerations (e.g., accessibility, child care)
- How the lesson will help them achieve their aims
- First four parts are usually shared across multiple lessons and instructors
2. Write Summative Assessments to Set Concrete Goals
- “Understand linear regression” is vague; a specific exercise makes aim much clearer
- Also helps communicate value of lesson to learners
- Derive learning objectives from what learners will do to demonstrate knowledge
- Remember, “understand X” isn’t checkable
- Draw a concept map!
- This series should be “7 plus or minus 2 Rules”
3. Write Formative Assessments for Pacing, Design, Preparation, and Reinforcement
- Spend 1-2 minutes checking in with learners via formative assessment every 10-15 minutes
- Pacing: are they still with you?
- Design: they act as milestones building toward the summative assessment
- Preparation: everything in the summative assessment should be exercised by a formative assessment
- Reinforcement: learners remember more if they use material right away (problem isn’t forgetting, but access)
- Doing this almost always tell you that you’re trying to cover too much
- Makes prerequisites explicit
- Enumerating prerequisite knowledge and skills helps you overcome expert blind spot
- And helps stitch lessons together
- Be careful not to intimidate learners who have impostor syndrome
- As with summative assessment, works best when concrete
- Not “understand linear regression”, but “can do the following”
4. Be Inclusive
- Choice of language or examples will tell people whether or not they’re welcome and likely to be taken seriously
- (If you don’t already know this, you’re probably a member of a privileged group)
- Do not use a deficit model (“they” are missing something, “they” need to try harder)
- For example, Lach2018 explored two strategies:
- Community representation: highlights students’ social identities, histories, and community networks using after-school mentors or role models from students’ neighborhoods, or activities that use community narratives and histories as a foundation for a computing project. Major risk is shallowness, e.g., using computers to build slideshows rather than do any real computing.
- Computational integration: incorporates ideas from the learner’s community, e.g., reverse engineering indigenous graphic designs in a visual programming environment. Major risk is cultural appropriation, e.g., using practices without acknowledging origins.
- When in doubt, ask your learners and members of their community what they think you ought to do and give them control over content and direction.
- DiSa2014a demonstrates effectiveness
5. Eliminate Incentives for Cheating
- Cheating is usually not a symptom of moral failing, but a rational response to poorly-designed incentives
- Beck2014 found that cheating is no more likely online than in person
- Motivation and demotivation in adult learners
- Motivators: agency, utility, communality
- Demotivators: unpredictability, unfairness, indifference
- Examples from Lang2013:
- Set the cost of failure very high
- Rely on single assessment mechanisms like multiple-choice tests
- Arbitrary grading criteria
6. Use Concreteness Fading
- PETE (Problem, Explanation, Theory, Example) goes from specific and tangible to more abstract
- What is the authentic problem that the lesson solves next?
- Explain a concrete solution
- Fill in the underlying theory
- Provide a second example so that learners will understand which parts generalize
- Authentic problem may be an end goal, or in later lessons, may arise out of a previous solution
- Build a usable mental model so that learners have somewhere to put knowledge, then correct the model as necessary
- E.g., ball-and-spring model in chemistry, evolution solely by descent, CPU-memory-disk model in computing
7. Design for Peer Instruction
- Scalable approximation of individual tutoring that engages learners
- Students do pre-class reading
- Instructor poses question
- Students vote publicly
- Students discuss reasoning with each other
- Students re-vote
- Instructor reviews
- Crou2001 and Port2016 are just two examples of studies showing its effectiveness
- In practice, often do some instruction in class, but always leading to peer discussion as quickly as possible
- E.g., type and run a few lines of code, then ask “what happens if I add X?” or “what line do I add to achieve Y?”
- Thanks to Mark Guzdial for this example
- The Discussion Book has many other methods (e.g., think-pair-share)
8. Design Around Worked Examples
- Learners learn faster from worked examples than they do from solving problems on their own
- They eventually need to do the latter, but step-by-step explanation of why and how helps more
- Live performances (music, programming, theorem proof) are effectively worked examples
- The “PEE” in “PETE”
9. Show How to Detect, Diagnose, and Correct Common Mistakes
- One aspect of worked examples that’s important enough to deserve its own section
- Novices spend much of their time making mistakes and trying to fix them, because they’re novices
- Including DD&C in the lesson reduces frustration, which in turn accelerates learning
- Also helps solidify their mental model
10. Foster Collaboration with Other Instructors
- DiSa2014b shows that lessons are often not findable
- FAIR Principles for data (findable, accessible, interoperable, reusable) also apply to lessons
- But “reusable” is aiming low
- Instead of one author broadcasting for others to use, foster collaboration in a teaching commons
- Open source software and Wikipedia demonstrate the benefits
- Increases inclusivity and reduces lesson maintenance burden
- And it’s more fun