From EDEC-625 notebook, Fall 2015, 19 Oct 2015.
Setting up a prediction activity. This is a classic technique for Science classes, where labs and experimentation are part of the process. But it should work really well for Math and CS too, where the algorithm/formula/steps are complex enough that students have to do some mental gymnastics to go from seeing them on paper to knowing what they will do.
What to do:
- Activate the students’ prior knowledge, as needed.
- Show the setup of something new. Don’t execute it yet.
- Ask for predictions. Probe for understanding of the details of the setup and the underlying concepts at play. Discuss in small groups and/or large group, and record the students’ ideas visually as per usual technique.
- Execute the program/problem/algorithm, and confirm the results.
- Was the outcome understood? Which theory “won”?
Go through this cycle with a well-chosen case, and then do it again with new inputs–maybe once or twice more with inputs that you choose for specific reasons (they generate good illustrations, highlight edge cases or unintuitive results, etc.). Then repeat a few times with the students suggesting the inputs. Repeat until everyone is able to predict together how it will behave.
Allow students to give you crazy inputs. Don’t think too hard about what the result will be, and (unless it’s too crazy) don’t say “no” to a student’s suggestion. The idea is to (a) let go of the usual tight curricular control that we usually exercise, and (b) to help the students poke and prod the concepts you are illustrating.
The overall cycle is: Input > Predict > Execute > Discuss
- The teacher holds back his/her own knowledge of the outcome.
- Teacher draws thinking out of the students, builds ideas before executing, gets them to reflect and understand after the execution.
- Teacher embraces unknown, novel, or surprising results.
- Teacher relinquishes control over inputs and problem design.
From notebook EDEC-625 Fall 2015, 19 Oct 2015.
Have students in groups. Each group creates a programming challenge for the next group to solve. They have to come up with something that’s within the class’s ability, using the blocks and techniques they’ve all learned up to that point. Ex:
- Make the sprite draw _______.
- Make the sprite do _______.
- Animate a conversation about ____ with appropriate costumes.
Each group formulates a challenge and passes it to the group to their right. They receive a challenge coming from their left, and they have to carry it out.
It’s great to solve a challenge… but it’s an even richer task to think up a challenge. You have to see through it, understand how a person might go about it, understand where the pitfalls are (or maybe intentionally place some). That’s good meta-cognition.
Via my friends Andrea and Adam over at inov8 Educational Consulting, here’s a list of interesting 2015 Edu books, published by Australia’s informED ed-tech-pedagogy blog.
I would personally skip the two about higher ed “disruption,” since nobody really knows how much of that (for-profits, MOOCs, finance/tuition reform, etc.) will stick—and, frankly, the blurbs are an embarrassing mix of hype and misunderstanding. From The End of College, for example: will the “traditional meritocracy” really be “upended” in the end? Who would describe the US college system as a “meritocracy” in the first place? It just goes downhill from there…
Some more promising books on this list, by my own reading of the descriptions:
Using Evidence of Student Learning to Improve Higher Education by George D. Kuh and Stanley O. Ikenberry. I cite this one because I’ve seen university teaching & learning be directly influenced by research, training, and classroom design. It’s definitely true that strong teachers and strong classes can be made, and it’s worth universities consciously investing in this.
Making Classrooms Better: 50 Practical Applications of Mind, Brain, and Education Science by Tracey Tokuhama-Espinosa. This one interests me for my own application as a teacher and a geeky parent of school-age kids. It starts with cross-disciplinary scientific background and then touches on a range of practical topics, from classroom climate to metacognitive skills and mindfulness.
What Connected Educators Do Differently by Todd Whitaker and Jeffrey Zoul. In 2014 I read Elizabeth Green’s Building a Better Teacher and came away convinced that way we leave teachers isolated in North America, with a (well or poorly designed) curriculum on a page and no community of practice, simply doesn’t make any sense. I immediately thought of teaching Computational Thinking in North America—an obvious place where we can start to build a better system. I’d be interested to see what Whitaker and Zoul have to say about social media and professional development.
These are, on the surface anyway, practical and grounded in good research and analysis. Not all are published just yet; post a comment if you’ve read any of them (or plan to).