Self-Explanation Prompts on Problem-Solving Performance in an Interactive Learning Environment

This study examined the effects of self-explanation prompts on problem-solving performance. In total, 47 students were recruited and trained to debug web-program code in an online learning environment. Students in an open self-explanation group were asked to explain the problem cases to themselves, whereas a complete other-explanation group was provided with partial explanations and asked to complete them by choosing correct key-words. The results indicate that students in the open self-explanation condition (a) outperformed in a debugging task, (b) perceived higher confidence for their explanations, and (c) showed a strong positive relationship between the quality of their explanation and their performance. These results demonstrate the benefits of the open self-explanation prompts. Cognitive load of self-explanation and quality of explanation are discussed. Self-explanation refers to a reflective activity explaining to oneself a learning material in order to understand facts from the material or to repair misunderstanding during studying worked-out examples or reading exploratory texts (Chi, Bassok, Lewis, Reimann, & Glaser, 1989). It seems obvious that a student performs better at problem-solving tasks, generates inferences which facilitate conceptual understanding, and repairs flawed mental models as well when being encouraged to use the self-explanation strategy during learning (Chi, 2000; Chi, de Leeuw, Chiu, & La Vancher, 1994). Other studies have corroborated the self-explanation effect from various domains such as mathematics (Siegler, 2002; Wong, Lawson, & Keeves, 2002), programming (Pirolli & Recker, 1994), biology (O'Reilly, Symons, & MacLatchy-Gaudet, 1998) and physics (Mayer, Dow, & Mayer, 2003). The self-explanation effect can be explained with two fundamental reflection mechanisms: inference generation and conceptual revision (Chi, 2000). From the inference generation perspective, we expect to find learners who are able to induce information omitted from a text or explanation provided by an expert. When learners realize a gap between their current mental model and incoming information, they tend to infer new knowledge while explaining to themselves (Chi, et al., 1994). In a conceptual revision perspective, while students study an expository text with an initial flawed mental model, they may recognize a conflict between their mental model and the text. With the recognition of this violation, students intentionally take efforts to resolve the dissonance (Chi, 2000). Considering the internal process of representing an expert model and evaluating it, students are required to use metacognitive Journal of Interactive Online Learning Kwon, Kumalasari, and Howland 97 activities such as comparing their current mental model with the expert model and finding discrepancy between them. In spite of its positive effectiveness, the self-explanation strategy needs to be supported with scaffolding to encourage students to use it in effective ways. At first, the reflection mechanisms require higher cognitive skills, but many students do not possess the skills (Chi, et al., 1989) or are reluctant to engage in learning by using their skills (Renkl, 1997). Not surprisingly, in the spontaneous self-explanation condition, only a few students (33%) engaged in generating explanations (Renkl’s, 1997). According to cognitive load theory, generating selfexplanation requires high cognitive load by requiring that learners monitor their understanding and represent incoming information at the same time (Sweller, 1988; Sweller, Van Merriënboer, & Paas, 1998). In order to engage and reflect on the learning process, learners need to adopt metacognitive strategies, which usually require more efforts and abilities. Cheshire, Ball, and Lewis (2005) examined self-explanation effects and found that self-explanation alone was not sufficient in analogical reasoning. Learners who were asked to generate explanations and were provided with feedback while solving problems outperformed learners who were only asked to explain themselves without feedback. Self-Explanation Effect and Scaffolding With concern for this issue, recently, many researchers have investigated the ways to support self-explanation. For example, Atkinson, Renkl, and Merrill (2003) implemented a fading instruction and self-explanation strategy while students learned from worked-out examples and revealed positive learning outcomes in an authentic classroom practice. The selfexplanation prompts enabled students to reflect on which probability principles they used in solving the problems. Although the results supported self-explanation effects, there were some limitations in generalizing the outcomes because students in the self-explanation group received more information about the principles than the other groups. Berthold and Renkl (2005) examined the effects of scaffolded self-explanation prompts in probability theory. In a computer-based learning environment, they manipulated three treatments: open self-explanation prompts, scaffolded self-explanation prompts, and no prompts. At the beginning, learners in the open self-explanation prompts group received a simple question prompt eliciting self-explanation (e.g., “Why do you calculate the total acceptable outcome by multiplying?”), and learners in the scaffolded self-explanation prompts group received “fill-inthe-blank” explanations (e.g., “There are ____ times ___ branches.”). Next, they practiced an isomorphic example with only open self-explanation prompts. As a result, they found that learners in both the open and scaffolded self-explanation prompts groups outperformed those in a no prompts group. Moreover, scaffolded self-explanation prompts showed a notable effect on conceptual knowledge acquisition compared with open self-explanation prompts. Although it was true that the scaffolded self-explanation prompts conveyed additional information required to construct a mental model, the fading out method might enable learners to integrate the domain concept with their current mental models by reducing cognitive load in early learning phases (Renkl & Atkinson, 2003). Aleven and Koedinger (2002) implemented an intelligent tutoring system and Geometry Cognitive Tutor (GCT) in a high school. The GCT provided feedback on the students’ solutions as well as their explanations. As a result, students in the self-explanation group were encouraged to generate more explanations and outperformed a problem-solving group, especially in more difficult test items. The feedback on self-explanation is important in that it may help them to recognize faults if students did not have a correct mental model, besides providing corrective Journal of Interactive Online Learning Kwon, Kumalasari, and Howland 98 information on the domain. In this sense, the GCT enhanced the self-explanation process very well. Considering the process of self-explanation, the GCT helped students to reflect on their understanding by providing corrective information, a process opposed to Chi’s (2000) reflection mechanisms of self-explanation in which students realize the gaps between their mental model and learning materials through an internal process rather than the realization being evoked by external information. As we consider self-learning, it is instructive to examine the internal reflection process of self-explanation. However, there are only a few studies to date that investigate the internal reflection process of self-explanation and its effects on learning (e.g. Chi, 2000). Moreover, in previous research, self-explanation was examined while studying learning materials; however, there are few studies investigating the self-explanation effect while conducting problem-solving tasks. When studying expository texts or worked-out examples, students might focus on understanding domain knowledge. Solving problems, on the other hand, requires more cognitive efforts such as identifying problems, testing hypotheses, and finding solutions. The problemsolving performance will be promoted by reflection on one’s problem-solving process. This study examined the effects of self-explanation on problem-solving performance. Purposes of Study The purpose of the present experiment is threefold. The first goal is to examine the effect of different self-explanation prompts on conceptual understanding and enhancing problemsolving performance. Previous research has confirmed the effects of self-explanation in comparison to no self-explanation prompt or other learning strategies, but effective ways to elicit self-explanation from students have not yet been confirmed. Berthold and Renkl (2005), for example, tested two types of self-explanation prompts in learning probability theory: open selfexplanation prompts and scaffolded self-explanation prompts. While the scaffolded selfexplanation prompts fostered conceptual understanding better than the open self-explanation prompts, one was not superior to the other for enhancing procedural knowledge. The current study extends Berthold and Renkl’s investigation by applying two different self-explanation prompts into problem-solving processes. Comparing the effects of two prompts will provide meaningful insight into the instructional design field. The second goal is to examine whether students provided with an open self-explanation prompt exerted more cognitive efforts while generating explanations and examining problems. According to a cognitive load theory, reducing extraneous cognitive load and increasing germane cognitive load is critical to support learning. In general, generating explanations requires more time and cognitive resources than reviewing explanations. This study explores whether the efforts contributed to learning. The third goal is to investigate the quality of explanations elicited from different prompts. If students generated incorrect explanations, it might reduce the effect of self-explanation prompts or even hinder learning. So, one can assume that the quality of explanations has a close relationship with learning gains. In this vein, if the types of prompts affect the quality of explanations, this can be significant in designing self-explanation. Considering the reflection mechanism of self-explanation, it is also very useful to investigate how well students evaluate their explanations and how confident they are with them. Theoretically, students who evaluate their understanding correctly will benefit from the self-explanation processes (Chi, et al., 1989). High confidence on good explanations reveals students’ accurate monitoring skills on their understanding, and vice versa. Journal of Interactive Online Learning Kwon, Kumalasari, and Howland