- 1 Brief statement of principle
- 2 Description of principle
- 3 Experimental support
- 4 Theoretical rationale
- 5 Conditions of application
- 6 Caveats, limitations, open issues, or dissenting views
- 7 Variations (descendants)
- 8 Generalizations (ascendants)
- 9 References
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
- Self-explaining is defined as a "content-relevant articulation uttered by the student after reading a line of text" (Chi, 2000; p. 165) or after studying a step in a worked-out example. A self-explanation may contain a meta-cognitive statement and/or a self-explanation inference.
- A meta-cognitive statement is an assessment, made by the student, of his or her own current understanding of the line of text or example step.
- A self-explanation inference is "an identified pieced of knowledge generated...that states something beyond what the sentence explicitly said" (Chi, 2000; p. 165).
- Prompting is defined as an external cue that is intended to elicit the activity of self-explaining. Prompts are typically generated by a person, tutoring system, or a verbal reminder embedded in the learning material.
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?
These prompts were then included as text, just below a worked-out example. The example was presented as a video taken of the Andes interface, with a voice-over narration describing the user-interface actions (see Table below). In this example, the student is learning how to solve the following problem:
A charged particle is in a region where there is an electric field E of magnitude
14.3 V/m at an angle of 22 degrees above the positive x-axis. If the charge on the particleis -7.9 C, find the magnitude of the force on the particle P due to the electric field E.
Now that all the given information has been entered, we need to apply
One way to start is to ask ourselves, “What quantity is the problem seeking?”
We know that there is an electric field. If there is an electric field,
We use the Force tool from the vector tool bar to draw the electric force.
Now that the direction of the electric force has been indicated, we can work on
Note. PROMPT = "Please begin your self-explanation."
Laboratory experiment support
Prompting for self-explaining has been shown to be effective in both increasing the amount, as well as learning gains (Chi et al., 1994). Prompting for self-explaining is typically paired with a training session, which instructs students on how to produce explanations. Laboratory research has shown that both the training and prompting techniques can be effective in producing performance gains (Bielaczyc, Pirolli, & Brown, 1995). Training does not necessarily have to be done by a human tutor. Instead, training students to self-explain can be automatized with a computerized training system (McNamara, 2004).
In vivo experiment support
Several in vivo experiments have leveraged laboratory work for inclusion of self-explaining in the classroom. Some in vivo experiments include:
- 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)
Prompting for self-explaining should increase the probability that a student engages in self-explaining, which includes an increase in the amount and accuracy of meta-cognitive monitoring statements and self-explanation inferences. Prompting for self-explaining is an attempt to increase the likelihood of traversing deep learning events.
Conditions of application
When should a prompt for self-explanation be delivered? In many of the studies described on this page, prompts for self-explanation were offered after each step of a worked-out solution. The timing of the prompt may depend on the domain. For example, in Hausmann and VanLehn (2007), the domain was physics, which requires the acquisition of procedure knowledge. The prompt to self-explain was issued after each solution step. For a more conceptual domain, such as the circulatory system, the experimenter in Chi et al. (1994) prompted the students to self-explain after reading each page of a text on the circulatory system. Roughly one line (or idea) was contained on each page of the text. After several pages, the participants became accustomed to the procedure, and turning the page became an implicit prompt for the students to begin self-explaining (Chi, personal communication).
Caveats, limitations, open issues, or dissenting views
Examples typically precede problem solving. For example, in Sweller and Cooper (1985; Experiment 2), they asked students to study 2 examples in preparation to solve 8 problems. Similarly, Chi et al. (1989) asked students to read through 4 chapters of a physics text, which contained several examples. After studying each chapter, the students were asked to solve problems related to the content that they just studied. Finally, Trafton and Reiser (1993) manipulated the presentation of examples and problems by using either a blocked design, where students studied 6 examples, then solved 6 problems. Alternatively, an alternating conditions presented one example first, then solved one problem. They continued this sequence until all problems and 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.
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. 
Bielaczyc, K., Pirolli, P., & Brown, A. L. (1995). Training in self-explanation and self-regulation strategies: Investigating the effects of knowledge acquisition activities on problem solving. Cognition and Instruction, 13(2), 221-252. 
Chi, M. T. H., DeLeeuw, N., Chiu, M.-H., & LaVancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477. 
Hausmann, R. G. M., & Chi, M. T. H. (2002). Can a computer interface support self-explaining? Cognitive Technology, 7(1), 4-14. 
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. 
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. 
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21(1), 1-29. 
Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59-89. 
Trafton, J. G., & Reiser, B. J. (1993). The contributions of studying examples and solving problems to skill acquisition. In Proceedings of the Fifteenth Annual Conference of the Cognitive Science Society (pp. 1017-1022). Hillsdale, NJ: Erlbaum. 
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.