Prompted Self-explanation

Brief statement of principle

Many empirical studies have shown that there is a large amount of variance when it comes to individually produced self-explanations. Some students have a natural tenancy to self-explain, while other students do little more than repeat the content of the example or expository text. The quality of the self-explanations themselves can be highly variable (Renkl, 1997). One instructional intervention that has been shown to be effective is to prompt students to self-explain (Chi et al., 1994). Prompting can take many forms, including verbal prompts from human experimenters (Chi et al., 1994), prompts automatically generated by computer tutors (McNamara, 2004; Hausmann & Chi, 2002; Koedinger & Aleven, 2002), or embedded in the learning materials themselves (Hausmann & VanLehn, 2007).

Description of principle

Examples

Here are the prompts from Chi et al. (1994):

• What new information does each step provide for you?
• How does it relate to what you've already seen?
• Does it give you a new insight into your understanding of how to solve the problems?
• Does it raise a question in your mind?

An example of prompting for self-explanining

Now that all the given information has been entered, we need to apply our knowledge of physics to solve the problem. One way to start is to ask ourselves, “What quantity is the problem seeking?” In this case, the answer is the magnitude of the force on the particle due to the electric field. We know that there is an electric field. If there is an electric field, and there is a charged particle located in that region, then we can infer that there is an electric force on the particle. The direction of the electric force is in the opposite direction as the electric field because the charge on the particle is negative. We use the Force tool from the vector tool bar to draw the electric force. This brings up a dialog box. The force is on the particle and it is due to some unspecified source. We do know, however, that the type of force is electric, so we choose “electric” from the pull-down menu. For the orientation, we need to add 180 degrees to 22 degrees to get a force that is in a direction that is opposite of the direction of the electric field. Therefore we put 202 degrees. Finally, we use “Fe” to designate this as an electric force. [ PROMPT ] Now that the direction of the electric force has been indicated, we can work on finding the magnitude. We must choose a principle that relates the magnitude of the electric force to the strength of the electric field, and the charge on the particle. The definition of an electric field is only equation that relates these three variables. We write this equation, in the equation window. [ PROMPT ]

Note. PROMPT = "Please begin your self-explanation."

Experimental support

In vivo experiment support

• Does it matter who generates the explanations? (Hausmann & VanLehn, 2006)
• The effects of interaction on robust learning (Hausmann & VanLehn, 2007)
• Deep-level questions during example studying (Craig, VanLehn, & Chi, 2006)

Theoretical rationale

(These entries should link to one or more learning processes.)

References

Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explain with a computer-based Cognitive Tutor. Cognitive Science, 26, 147-179. [1]

Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. [2]

Hausmann, R. G. M., & Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14. [3]

Hausmann, R. G. M., & VanLehn, K. (2007). Explaining self-explaining: A contrast between content and generation. In R. Luckin, K. R. Koedinger & J. Greer (Eds.), Artificial intelligence in education: Building technology rich learning contexts that work (Vol. 158, pp. 417-424). Amsterdam: IOS Press. [4]

McNamara, D. S., Levinstein, I. B., & Boonthum, C. (2004). iSTART: Interactive strategy training for active reading and thinking. Behavioral Research Methods, Instruments, and Computers, 36, 222-233. [5]

Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29. [6]