It’s all very well to know how the brain works, or how society’s needs and expectations shape our ideas about learning, but eventually we have to decide how to translate those ideas into actual teaching. One of the best guides to doing this that I have found is the book How Learning Works: Seven Research-Based Principles for Smart Teaching (Ambrose et al, Jossey-Bass, 2010). Their advice is based in equal parts on theory, research, and experience, and while some of their recommendations may seem banal when summarized, the full-length explanations in the book itself are not. Over the course of many years, they claim that they have found these principles to be:
- domain-independent: they apply equally well across all subject areas;
- experience-independent: they apply to all educational levels and learning situations; and
- cross-cultural: although it is always important to remember that culture influences how the principles should be applied in particular situations.
Chapter 2 of the book says that, “How students organize knowledge influences how they learn and how they apply what they know” (which no sensible person would argue against). They elaborate by saying that:
Students naturally make connections between different bits of knowledge. When those connections form structures that are accurately and meaningfully organized, students can retrieve and apply their knowledge faster and more accurately. When knowledge is connected in inaccurate or random ways, on the other hand, they either won’t retrieve it or will apply it inappropriately.
Their recommendations are:
- Create a concept map to analyze your own knowledge organization.
- Analyze tasks to identify the most appropriate knowledge organization.
- Provide students with the organizational structure of the course.
- Explicitly share the organization of each lecture, lab, or discussion.
- Use contrasting and boundary cases to highlight organizing features.
- Explicitly highlight deep features.
- Make connections among concepts explicit.
- Encourage students to work with multiple organizing structures.
- Ask students to draw a concept map to expose their knowledge organizations.
- Use a sorting task to expose students’ knowledge organizations.
- Monitor students’ work for problems in their knowledge organizations.
We’re going to focus this week on the four highlighted recommendations (#1 and #5-7). Your tasks are:
- Read Chapters 1 and 2 of How Learning Works.
- Draw a concept map for the topic you’ve been assigned below. (This should be pretty simple, since each topic is only about 10 minutes of teaching time.) Use whatever tools/format you want—Visio, freehand on a tablet, crayon on a napkin—as long as you get a legible image out at the end.
- Make up one mainstream example of how to use the concept in your topic, and one contrasting or boundary example. Each example should be a couple of shell commands or a very short snippet of Python, plus a paragraph of explanation.
- Write another paragraph explaining what deeper concepts your examples illustrate, and what prerequisite knowledge they depend on. (The prerequisite knowledge will often be stuff that’s explained outside your assigned topic.)
- Share what you’ve done by writing a blog post and categorizing it as “Week 1” (just like this one). The last line of your post should report how long it took you to do everything—we’ll use these figures to scale future tasks.
One extra task is to read and comment on everyone else’s blog posts. What did you learn that you hadn’t known or realized before? What do you disagree with? What did they miss? How much do you think their post would help you if you were learning this stuff for the first time?
|Carlos Anderson||Sets & Dictionaries: Dictionaries|
|Dhavide Aruliah||The Shell: Files and Directories|
|Azalee Bostroem||Sets & Dictionaries: Nanotech Example|
|Erik Bray||The Shell: Creating and Deleting|
|Matt Davis||The Shell: Pipes and Filters|
|Justin Ely||The Shell: Permissions|
|Mike Hansen||The Shell: Finding Things|
|Katy Huff||Python: Control Flow|
|Justin Kitzes||Python: Lists|
|Emily Jane McTavish||Python: Input and Output|
|Ian Mitchell||Python: Strings|
|Randy Olson||Python: Functions|
|Sarah Supp||Python: Libraries|
|Hans-Martin von Gaudecker||Matrix Programming: Basic Operations|
|Ben Waugh||Matrix Programming: Indexing|
|Ethan White||Matrix Programming: Linear Algebra|
|Lynne Williams||Matrix Programming: Recommendations|