Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems

Abstract

The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings.

Following the Wrong Footsteps: Fixation Effects of Pictorial Examples in a Design Problem-Solving Task.

“…One of the factors that seems to be particularly beneficial has been defined as the self-explanation effect (Chi, Bassok, Lewis, Reimann, & Glaser, 1989; see also Reimann & Neubert, 2000;VanLehn, Jones, & Chi, 1992;Wright, 1981), which refers to the idiosyncratic ways individuals explain elements of the examples to themselves during problem solving. For example, VanLehn (1998) observed no difference in the number of self-explanations among successful and unsuccessful solvers.…”

“…In addition, the tasks used in most previous studies (Chi, Bassok, et al, 1989;LeFevre & Dixon, 1986) all involved welldefined problems, that is, problems with one correct solution that the participant was expected to attain. Regarding the findings of LeFevre and Dixon (1986), in particular, it is important to examine whether there is a similar preference of participants for examples compared with instructions in ill-defined problems, that is, problems with no one specific correct solution.…”

Why some surprises are more surprising than others: Surprise as a metacognitive sense of explanatory difficulty

“…When students encounter a surprising piece of information their attention is aroused, provoking more intensive processing of the tobe-learned material (i.e., there is a call to explain, to correct and better understand the material). Self-explaining and self-explanation training is also known to improve text comprehension and learning (e.g., Chi, Bassok, Lewis, Reimann & Glaser, 1989;Chi, De Leeuw, Chiu & Lavancher, 1994;Durkin, 2011;Roy & Chi, 2005), particularly for lowknowledge readers (McNamara, 2001;McNamara & Scott, 1999). Self-explanation appears to have a greater impact if there are reliable patterns and consistencies in the material uncovered by the explanations (Williams, Lombrozo & Rehder, 2010); although, conversely, it can be detrimental to learning in some cases if it leads to overgeneralisation (Williams, Lombrozo & Rehder, 2013).…”

In pursuit of knowledge: Comparing self-explanations, concepts, and procedures as pedagogical tools

“…In their seminal study on the self-explanation effect, Chi, Bassok, Lewis, Reimann, & Glaser (1989) found that, when studying example exercises in a physics text, the best learners spontaneously explained the material to themselves, providing justifications for each action in a solution sequence. Subsequent studies have shown that prompting for such self-explanations can lead to improved learning outcomes in numerous domains including arithmetic (Calin-Jageman & Ratner, 2005;Rittle-Johnson, 2006;Siegler, 2002), geometry (Aleven & Koedinger, 2002;Wong, Lawson & Keeves, 2003), interest calculations (Renkl, Stark, Gruber & Mandel, 1998), LISP programming (Bielaczyc, Pirolli, & Brown, 1995), argumentation (Schworm & Renkl, 2007), Piagetian number conservation (Siegler, 1995), probability calculation (Große & Renkl, 2003), biology text comprehension (Chi, DeLeeuw, Chiu, & LaVancher, 1994), and balancing beam problems (Pine & Messer, 2000).…”

“…Importantly, these differences are associated with divergent learning outcomes (Chi et al, 1989;Chi et al, 1994;Pirolli & Recker, 1994;Renkl, 1997). Successful learners tend to give more principle-based explanations, more frequently consider the goals of operators and procedures, and less frequently show illusions of understanding (see Renkl, 2002 for an effective summary).…”