- Learners can describe the steps in reverse instructional design and explain why it generally produces better lessons than the usual “forward” lesson development process.
- Learners can define “teaching to the test” and explain why reverse instructional design is not the same thing.
- Learners can construct and critique five-part learner personas.
- Learners can construct good learning objectives and critique learning objectives with reference to Bloom’s Taxonomy and/or Fink’s Taxonomy.
Most people design lessons as follows:
Someone tells you that you have to teach something you haven’t thought about in ten years.
You start writing slides to explain what you know about the subject.
After two or three weeks, you make up an assignment based more or less on what you’ve taught so far.
You repeat step 3 several times.
You stay awake into the wee hours of the morning to create a final exam.
There’s a better way, but to explain it, we first need to explain how test-driven development (TDD) is used in software development. Programmers who are using TDD don’t write software and then (possibly) write tests. Instead, they write the tests first, then write just enough new software to make those tests pass, and then clean up a bit.
TDD works because writing tests forces programmers to specify exactly what they’re trying to accomplish and what “done” looks like. It’s easy to be vague when using a human language like English or Korean; it’s much harder to be vague in Python or R.
TDD also reduces the risk of endless polishing, and also the risk of confirmation bias: someone who hasn’t written a program is much more likely to be objective when testing it than its original author, and someone who hasn’t written a program yet is more likely to test it objectively than someone who has just put in several hours of hard work and really, really wants to be done.
A similar “backward” method works very well for lesson design. This method is something called reverse instructional design and was developed independent in [Wiggins2005], [Biggs2011], and [Fink2013] (a summary of which is freely available online [Fink2003].) In brief, lessons should be designed as follows:
Brainstorm to get a rough idea of what you want to cover, how you’re going to do it, what problems or misconceptions you expect to encounter, what’s not going to be included, and so on. You may also want to draw some concept maps at this stage.
Create or recycle learner personas (discussed in the next section) to figure out who you are trying to teach and what will appeal to them.
Create assessments that will give the learners a chance to practice the things they’re trying to learn and tell you and them whether they’re making progress and where they need to focus their work.
Put the assessments in order based on their complexity and dependencies to construct a course outline.
Write just enough to get learners from one formative assessment to the next. An actual classroom lesson will typically then consist of three or four such episodes, each building toward a short check that learners are keeping up.
This method helps to keep teaching focused on its objectives. It also ensures that learners don’t face anything on the final exam that the course hasn’t prepared them for.
Building Lessons by Subtracting Complexity
One way to build a programming lesson is to write the program you want learners to finish with, then remove the most complex part that you want them to write and make it the last exercise. You can then remove the next most complex part you want them to write and make it the penultimate exercise, and so on. Anything that’s left–i.e., anything you don’t want them to write as an exercise–becomes the starter file(s) that you give them. This typically includes things like importing libraries or helper functions to access data.
How and Why to Fake It
One of the most influential papers in the history of software engineering was Parnas and Clements’ “A Rational Design Process: How and Why to Fake It”. In it, the authors pointed out that in real life we move back and forth between gathering requirements, interface design, programming, and testing, but when we write up our work it’s important to describe it as if we did these steps one after another so that other people can retrace our steps. The same is true of lesson design: while we may change our mind about what we want to teach based on something that occurs to us while we’re writing an MCQ, we want the notes we leave behind to present things in the order described above.
Teaching to the Test
Reverse instructional design is not the same thing as “teaching to the test”. When using RID, teachers set goals to aid in lesson design, and may never actually give the final exam that they wrote. In many school systems, on the other hand, an external authority defines assessment criteria for all learners, regardless of their individual situations, and the outcomes of those summative assessments directly affect the teachers’ pay and promotion. Green’s Building a Better Teacher [Green2014] argues that this focus on measurement is appealing to those with the power to set the tests, but is unlikely to improve outcomes unless it is coupled with support for teachers to make improvements based on test outcomes. This is often missing, because as [Scott1999] pointed out, large organizations usually value uniformity over productivity.
A key step in the process above is figuring out who your audience is. One way to do this is to write two or three learner personas. This technique is borrowed from user interface design, where short profiles of typical users are created to help designers think about their audience’s needs, and to give them a shorthand for talking about specific cases.
Learner personas have five parts: the person’s general background, what they already know, what they think they want to do, how the course will help them, and any special needs they might have. A learner persona for a weekend workshop aimed at new college students might be:
Jorge has just moved from Costa Rica to Canada to study agricultural engineering. He has joined the college soccer team, and is looking forward to learning how to play ice hockey.
Other than using Excel, Word, and the Internet, Jorge’s most significant previous experience with computers is helping his sister build a WordPress site for the family business back home in Costa Rica.
Jorge needs to measure properties of soil from nearby farms using a handheld device that sends logs in a text format to his computer. Right now, Jorge has to open each file in Excel, crop the first and last points, and calculate an average.
This workshop will show Jorge how to write a little Python program to read the data, select the right values from each file, and calculate the required statistics.
Jorge can read English proficiently, but still struggles sometimes to keep up with spoken conversation (especially if it involves a lot of new jargon).
A single learner persona is sometimes enough, but two or three that cover the whole range of potential learners is better. One of the ways they help is by serving as a shorthand for design issues: when speaking with each other, lesson authors can say, “Would Jorge understand why we’re doing this?” or, “What installation problems would Jorge face?”
Our Learners Revisited
The personas of Samira and Moshe in the introduction have the five points listed above, rearranged to flow more readably.
Deciding What to Teach
There are two ways to decide what to teach: pick material and then find an audience, or decide on an audience and then figure out what they want to learn. Either way, Guzdial’s “Five Principles for Programming Languages for Learners” offers essential guidance:
- Connect to what learners know.
- Keep cognitive load low.
- Be honest (i.e., use authentic tasks).
- Be generative and productive.
- Test your ideas rather than trusting your instincts.
Summative and formative assessments help instructors figure out what they’re going to teach, but in order to communicate that to learners and other instructors, a course description should also have learning objectives (sometimes also called a learning goal). A learning objective is a single sentence describing what a learner will be able to do once they have sat through the lesson in order to demonstrate what they have learned.
Learning objectives are meant to ensure that everyone has the same understanding of what a lesson is supposed to accomplish. For example, a statement like “understand Git” could mean any of the following, each of this would be backed by a very different lesson:
Learners can describe three scenarios in which version control systems like Git are better than file-sharing tools like Dropbox, and two in which they are worse.
Learners can commit a changed file to a Git repository using a desktop GUI tool.
Learners can explain what a detached HEAD is and recover from it using command-line operations.
Objectives vs. Outcomes
A learning objective is what a lesson strives to achieve. A learning outcome is what it actually achieves, i.e., what learners actually take away. The role of summative assessment is therefore to compare outcomes with objectives.
More specifically, a good learning objective has a measurable or verifiable verb that states what the learner will do, and specifies the criteria for acceptable performance. Writing these kinds of learning objectives may initially seem restrictive or limiting, but will make both you, your fellow instructors, and your learners happier in the long run. You will end up with clear guidelines for both your teaching and assessment, and your learners will appreciate the clear expectations.
One way to understand what makes for a good learning objective is to see how a poor one can be improved:
“Learner will be given opportunities to learn good programming practices.” Describes the lesson’s content, not the attributes *of successful students.
“Learner will have a better appreciation for good programming practices.” Doesn’t start with an active verb or define the *level of learning, and the subject of learning has no context and *is not specific.
“Learner will understand how to program in R.” Starts with an active verb, but doesn’t define the level of learning, and the *subject of learning is still too vague for assessment.
“Learner will write one-page read-filter-summarize-print data analysis scripts for tabular data using R and R Studio.” Starts with an active verb, defines the level of learning, and provides context to ensure that outcomes can be assessed.
Bloom’s taxonomy can be used to organize learning objectives. First published in 1956, it attempts to define levels of understanding in a way that is hierarchical, measurable, stable, and cross-cultural. The list below defines its levels and shows some of the verbs typically used in learning objectives written for each level.
Knowledge: recalling learned information (name, define, recall).
Comprehension: explaining the meaning of information (restate, locate, explain, recognize).
Application: applying what one knows to novel, concrete situations (apply, demonstrate, use).
Analysis: breaking down a whole into its component parts and explaining how each part contributes to the whole (differentiate, criticize, compare).
Synthesis: assembling components to form a new and integrated whole (design, construct, organize).
Evaluation: using evidence to make judgments about the relative merits of ideas and materials (choose, rate, select).
A set of learning objectives based on this taxonomy for an introductory course on HTML and CSS might be:
By the end of this course, students will:
Understand the difference between markup and presentation, the nested nature of HTML, what CSS properties are, and how CSS selectors work.
Know how to write and style a web page using common tags and CSS properties.
Be able to compare and contrast authoring with HTML and CSS to authoring with desktop publishing tools.
Understand how the visually impaired interact and people in low-bandwidth environments interact with web pages and take their needs into account when designing new pages.
Good courses take a lot of effort to build, but building them is only the first challenge. Once they have been written, someone needs to maintain them, and doing that is a lot easier if the lessons have been built in a maintainable way.
But what exactly does “maintainable” mean? The short answer is that a course is maintainable if it’s cheaper to update it than to replace it. This equation depends on many factors, only some of which are under our control:
How well documented the course’s design is. If the person doing maintenance doesn’t know (or doesn’t remember) what the course is supposed to accomplish or why topics are introduced in a particular order, it will take her more time to update it. One of the reasons to use the template described earlier is to capture decisions about why each course is the way it is.
How the course’s content is structured. Version control is the secret sauce that allows software development to scale, but today’s version control systems (still) can’t handle widely-used file formats like Word and PowerPoint. Lessons should therefore either be written in plain-text formats like HTML, Markdown, or LaTeX, or stored online in systems like Google Docs that allow many people to edit the same files. (The next section discusses this in more detail.)
How easy it is for collaborators to collaborate technically. Lesson authors usually share material by passing it from hand to hand (or equivalently, by emailing files to each other or putting them in a shared drive. Collaborative writing tools like Google Docs and wikis are a big improvement, as they allow many people to update the same document and comment on other people’s updates. The version control systems used by programmers, such as GitHub, are another big advance, since they let any number of people work independently and then merge their changes back together in a controlled, reviewable way. Unfortunately, version control systems have a long, steep learning curve, which makes shared online authoring systems like Google Docs and wikis the best technical choice for most groups.
The True Cost of Video
Making a small change to this webpage only takes a few minutes, but in our experience, making any kind of change to a video takes an hour or more. In addition, most people are much less comfortable recording themselves than contributing written material.
The fourth factor, and the most important one in practice, is how willing people are to collaborate. The tools needed to build a “Wikipedia for lessons” or a “GitHub for lessons” have been around for almost twenty years, but neither model has caught on. When asked why not, teachers raise many objects, none of which hold up to close inspection:
The most important thing about a lesson isn’t having it, but *writing it, because that gives you a chance to figure out what you think about the topic.* This objection rhymes with my personal experience, but the same is true of software, and somehow we get up-and-coming programmers to use and improve libraries rather than building their own stuff from scratch.
It’s just more trouble than it’s worth, because it’s always easier in the short term to write something from scratch than to learn your way around someone else’s material. And yet most teachers use textbooks, and most actors perform other people’s plays, and…
It doesn’t pay off for most teachers because they only teach any particular lesson once a year (or once a quarter). Infrequent teaching ought to push people toward re-use, not away from it.
Working at scale results in a more neutral point of view (the average of the contributors’ personal views), but in many fields, lessons are valuable precisely because they’re one person’s opinion. This is true for literature, but for basic algebra? And if the difference is one of teaching method rather than content, then yeah, there should be half a dozen different shared lessons on polynomials, each approaching the topic in a different way.
There’s no onboarding process to teach people the mechanics of distributed ad hoc large-scale collaboration. This is undoubtedly a contributing factor, but (a) teachers get more training in how to develop lessons than most programmers get in how to take part in an open source project and (b) lack of a formal onboarding process hasn’t slowed down Wikipedia.
Collaboration on lesson development gets squeezed out by more important things (where “important” means “to the principal or chair”). Again, this should push people toward collaboration (possibly under official radar), since every minute they don’t spend writing a lesson is a minute they can use to satisfy the principal or chair.
The Firewall of Doom at many schools prevents people from working on shared materials. Probably true for some people, but this is not true for all and most teachers in industrialized countries have access to a computer at home these days.
The stakes are too high for teachers who are going to be evaluated on their teaching. This may be true for some teachers, but isn’t a universal.
No measurable outcome will show improvement, so there’s no incentive to do it. The same is true of open source software, but while only a small minority of programmers contribute, that’s still enough people for it to thrive.
It’s a generational thing: as digital natives, tomorrow’s teachers will just naturally do it. Millenials don’t actually act that differently from their elders, and “not yet” arguments are as unfalsifiable as the claims by members of millenarian movements that the apocalypse is definitely coming–yup, any day now.
You can’t run regression tests on a lesson, so there’s no easy way to *tell if my changes have broken something that you wrote. But Wikipedia…
One interesting observation is that while teachers don’t collaborate at scale, they do remix by finding other people’s materials online or in textbooks and reworking them. That suggests that the root problem may be a flawed analogy: rather than lesson development being like writing Wikipedia articles or open source software, perhaps it’s more like postmodern music.
If this is true, then lessons may be the wrong granularity for sharing, and collaboration might be more likely to take hold if the thing being collaborated on was smaller. This fits well with Caulfield’s theory of choral explanations. He argues that sites like Stack Overflow succeed because they provide a chorus of answers for every question, each of which is most suitable for a slightly different questioner. If Caulfield is right, the future of learning–particularly online learning–may lie in guided tours of community-curated Q&A repositories rather than in things we would recognize as “lessons” today.
When designing a lesson, you must always remember that you are not your learners. You may be older (or younger, if you’re teaching seniors) or wealthier (and therefore able to afford to download videos without foregoing a meal to pay for the bandwidth), but you are almost certainly more knowledgeable about technology. Don’t assume that you know what they need or will understand: ask them, and actually pay attention to their answer. After all, it’s only fair that learning should go both ways.
Working in pairs or small groups, create a five-point persona that describes one of your typical learners.
Write one more learning objectives for something you currently teach or plan to teach using Bloom’s Taxonomy. Working with a partner, critique and improve the objectives.
Write one more learning objectives for something you currently teach or plan to teach using Fink’s Taxonomy. Working with a partner, critique and improve the objectives.
Betsy Leondar-Wright coined the phrase inessential weirdness to describe things groups do that aren’t really necessary, but which alienate people who aren’t already members of that group. Sumana HariHareswara later used this notion as the basis for a talk on inessential weirdnesses in open source software. Take a few minutes to read both articles, and then make a list of inessential weirdnesses you think your learners might encounter when you first teach them. How many of these can you avoid with a little effort?