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 instructions to self-explain, taken from Chi et al. (1994):

"We would like you to read each sentence out loud and then explain what it means to you. That is, what
new information does each line provide for you, how does it relate to what you've already read, does it give
you a new insight into your understanding of how the circulatory system works, or does it raise a question
in your mind. Tell us whatever is going through your mind–even if it seems unimportant."

These prompts were reworded to be used in Hausmann & VanLehn (2007):

• 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.)

Caveats, limitations, open issues, or dissenting views

Examples typically precede problem solving. For example, in Sweller and Cooper (1985), they asked students to study X examples in preparation to solve X problems. Similarly, Chi et al. (1989) asked students to read through X chapters of a physics text, which contained several examples. Finally, Trafton and Reiser (1993) manipulated the presentation of examples and problems by using either a blocked design, where students studied X examples, then solved X problems. Alternatively, an alternating conditions presented one example first, then solved one problem. They continued this sequence until X problems and X examples were completed.

The order of solving and studying examples from Hausmann and VanLehn (2007) differed from traditional research on example-studying. In their experiment, students attempted to solve a problem first, and then studied an isomorphic example. The students alternated between solving problems and studying examples until all four problems were solved and all three examples were studied. Problems were presented first to capitalize on the strengths of impasse-driven learning (VanLehn , 1988). The problems created conditions where an impasse might be reached while solving a problem, and the example would demonstrate a smooth, expert solution to the same problem.

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]

VanLehn, K. (1988). Toward a theory of impasse-driven learning. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 19-41). New York: Springer.