Many times, I find myself on autopilot; just going through the motions without really thinking about what I am doing.
Automaticity, while there currently is not consensus about the exact meaning of the term, is this idea of processing information with little to no attention (Moors & De Houwer, 2006) – it is this idea of autopilot. We have probably all experienced this in our daily lives: we drive home after work instead of driving to the grocery store, we read something – a news article, a book – and have no idea what we read at the end.
Another place where we can see this automaticity is in the classroom. Students (myself included) can end up just going through the motions, following a script, without thinking about what we are really doing. Have you ever read something, gotten to the end of a paragraph or section, and realized that you have no idea what you read? Because I have. And students can do that too. Just going through the motions.
Another place where I have seen this “just going through the motions” is in students’ problem-solving. Novice engineering students, when solving problems, may just follow a series of steps because that was what was presented to them. Instead, we want students to be problem-solvers, not just recipe followers.
Let me give you an example. For solving statics problems, the statics for dummies cheat sheet (here) lists just a few necessary steps:
- Set up a free body diagram for the whole system
- Write equilibrium equations for the support reactions
- Write equilibrium equations for the internal forces
- Solve for the unknowns
Seems simple enough, right? But when students follow these steps, do they really understand the different forces that are at play or are they just going through the motions? Do they understand how to represent the free-body diagram and represent relevant forces? It may be hard to tell.
In addition to automatically following a set of steps to solve a textbook problem which has a defined answer, students can act automatically when solving ill-defined problems too. When solving these ill-defined problems, students often move through the problem formulation and idea generation phases quickly and move on to picking the best idea. With these ill-defined problems, it can be challenging to get students to really focus on the complexity of problems and the variety of possible solutions.
However, we want students to be able to solve a wide variety of problems and to be able to transfer the information that they learned in one context to another context. [For a more information about transfer, look here: Where do I go from here?]
To help students avoid falling into this trap of automatically going through the steps, here are a few strategies that can help.
- Have students summarize what they read. Having students write a summary, even a really short summary, can help students avoid just going through the motions when reading a textbook or article.
- Have students explain how they solved a problem. Teachers can ask students to both solve a textbook problem numerically and write an explanation for how they solved the problem. This can help the teacher identify if students are just following a series of steps exactly as they were presented, of if students are identifying the various nuances in the problem.
- Have students solve a variety of problems that don’t necessarily look the same (but use the same principles)
- For ill-structured problems: Have students identify the problem components, constraints, and criteria
- For ill-structured problems: Have students generate multiple possible solutions
- Give students cases or problems that differ in some meaningful way and have students compare the cases.
For more information, check out these resources:
- Logan, G. D. (1988). Toward an instance theory of automatization. Psychological review, 95(4), 492.
- Moors, A., & De Houwer, J. (2006). Automaticity: a theoretical and conceptual analysis. Psychological bulletin, 132(2), 297.
- Schneider, W., & Shiffrin, R. M. (1977). Controlled and automatic human information processing: I. Detection, search, and attention. Psychological review, 84(1), 1.
- Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological review, 84(2), 127.