You Got Some Explaining To Do!: Self-explaining

Here's a fun question for you: How detailed is your mental model of a toilet? If I had to guess, I would say you generally know how a toilet works, but haven't written a thesis on the topic. To test yourself, read the following passage and try to assemble a mental model of the modern flushable toilet.

The toilet has several working components. The tank (or cistern) holds clean water, and it is located above the toilet bowl, which holds the flushable content. Connected to the tank is a handle that, when pressed, releases the water from the tank. Gravity drains the water into the bowl through a small cistern tube. The water then exits the bowl at the bottom through the S-trap. 

Inside the tank are several additional components that control the flow of water into and out of the tank. At the bottom of the tank is a flapper valve, which is connected to the handle by a chain. When the handle is pressed, the chain raises the flapper valve to let the water out. As the water level decreases, the float ball, which is connected to the float rod, drops and opens the inlet valve. When the inlet valve opens, clean water rushes in to fill the tank. When the water reaches the top of the tank, the float ball closes the inlet valve. 

There's a lot of information crammed into those paragraphs [1]; however, it is also the case that a lot of information is missing. For example, how does the float ball and rod open and close the inlet valve? To make sense of the description as currently written, you still have to do some work. First, you have to spatially assemble all of the components in your mind's eye as you learn about them. Second, you need to supply any information that may be missing. Finally, you might have to resolve contradictions that arise between the information you are learning and the mental model you are building as you engage in the first two activities [2].

What do good students do when learning something new? 

When students read a text about an unfamiliar topic, their first priority is to make sense of each and every word. Once they decode the words, their next task is to assemble them into larger chunks of meaningful information. For example, you probably know what the words handle, chain, and tube mean. But you need to connect all three concepts together to form a working model of the flush mechanism in a toilet. The mental process of hooking these concepts together is what we will call self-explaining, or the generation of inferences that are required to construct an accurate mental model.


Evidence for self-explaining was first observed by asking students to read a passage about a branch of physics called statics [3]. In statics problems, balanced forces are acting on a body, which means there is no acceleration (see Fig. 1 for an example). Statics problems can become quite complex, as you can probably imagine. Researchers noticed that students who best understood statics were the ones who explained to themselves how to solve these types of problems. In other words, the best students naturally engaged in self-explaining. They also generated several self-explanation inferences to fill in the missing pieces of information that are not stated explicitly in the text. These missing pieces are necessary to fully understand the problem's solution.

Fig 1. An example statics problem.

Can you motivate students to self-explain? 

It's really interesting to read transcripts of students as they attempt to make sense of a complex topic. They might not understand it at first. But as they explain it to themselves, they are then able to see how to correct their current understanding with the model presented in the text. Good students naturally self-explain, but can we get all students to engage in self-explaining? The answer, as it turns out, is a qualified "yes." 

In one of the first studies to demonstrate that self-explaining can be prompted, researchers asked eighth grade students to read a passage about the human circulatory system [4]. Before students read the passage, the researchers asked them several questions about their understanding of the circulatory system. Because they were so young, it was no surprise that students generally had an impoverished understanding of the circulatory system. Most knew that the heart beats, but they didn't know why or what purpose the heart served. Some of the better students knew that the heart circulates blood, but they didn't understand pulmonary circulation. That is, they didn't realize that the heart has a special circuit of blood that goes to the lungs for oxygenation (and back).

After the pretest phase of the experiment, the students were asked to read a text passage that described, in detail, all of the steps of circulation. The student was verbally prompted by the experimenter to self-explain after reading each sentence of the text. After a while, merely turning the page was a prompt to self-explain. In other words, the researchers trained the students to self-explain, and then they reminded them over and over to self-explain. It was a pretty heavy-handed approach, but it worked. It paid off because the students who were prompted to self-explain demonstrated a more robust mental model of the circulatory system on the post-test than students in control condition who were only prompted to think about or reread the sentences.

Why was prompting students to self-explain a qualified success? Like most educational interventions, there were individual differences in the amount and effectiveness of the prompting. Some students generated several self-explanation inferences, whereas others generated only a few. Since the original study, researchers have designed new ways to help students become better self-explainers. For example, one study gave students an incorrect solution and asked students to debug the fictitious student's reasoning process [5]. This helped take the pressure off the student so that they could focus their attention on reconciling the differences between the correct and incorrect solution paths.

The STEM Connection

The connection between self-explanation and STEM education is extremely straightforward. Students should be trained on the definition of what self-explaining is. Then they should be prompted, periodically, to engage in self-explaining. To help them get started, here is a list of prompts that were used in the original study [4]: 

1. What new information does each line provide for you?
2. How does it relate to what you’ve already read?
3. Does it give you a new insight into your understanding of how ______ works?
4. Does it raise a question in your mind?

Also, students should be shown evidence of the utility of self-explaining. Students will probably be more likely to self-explain if they understand what it will eventually buy them. Moreover, students should be given an opportunity to develop the skill [6]. In fact, there is an intelligent tutoring system that was developed to do precisely that. The iSTART system gives students the opportunity to self-explain while reading complex textual passages [7]. Because self-explaining can be mentally taxing, the iSTART system was even outfitted with a bunch of mini-games to help build fluency in self-explaining [8].

Self-explaining is a powerful learning mechanism. It can be time-consuming and mentally exhausting, but the end result is definitely worth the effort. Like other concepts covered in this blog, self-explaining is a skill, which means it can become automatized over time. Thus, we need to get students started with self-explaining as early as possible because when they get to college, they will thank us for giving them the tools to make sense of tough concepts like organic chemistry (and flushable toilets). 


Share and Enjoy!

Dr. Bob

For More Information

[1] Here is the resource I consulted to write this description. To further enhance reading comprehension, it's typically to include a diagram. I intentionally omitted the diagram because I wanted you to attempt to assemble a mental model without a visual aid. 

[2] Chi, M. T. H. (2000). Self-explaining expository texts: The dual processes of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in Instructional Psychology, Hillsdale, NJ: Lawrence Erlbaum Associates. 161-238.

[3] Chi, M. T. H., Bassok, M., Lewis, M., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13: 145-182.

[4] Chi, M. T. H., de Leeuw, N., Chiu, M. H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18: 439-477.

[5] Booth, J. L., Lange, K. E., Koedinger, K. R., & Newton, K. J. (2013). Using example problems to improve student learning in algebra: Differentiating between correct and incorrect examples. Learning and Instruction, 25, 24-34.

[6] Hausmann, R.G.M., & Chi, M.T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14.

[7] McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking. Behavior Research Methods, Instruments, & Computers, 36(2), 222-233.

[8] Jackson, G. T., & McNamara, D. S. (2013). Motivation and performance in a game-based intelligent tutoring system. Journal of Educational Psychology, 105(4), 1036.