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
  1. Students do pre-class reading
  2. Instructor poses question
  3. Students vote publicly
  4. Students discuss reasoning with each other
  5. Students re-vote
  6. 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