Learning by Solved Example Problems: Instructional Explanations Reduce Self-Explanation Activity

Abstract

Learning by Solved Example Problems: Instructional Explanations Reduce Self-Explanation Activity Silke Schworm (schworm@psychologie.uni-freiburg.de) Department of Psychology, Educational Psychology, Engelbergerstr.41 79085 Freiburg, Germany Alexander Renkl (renkl@psychologie.uni-freiburg.de) Department of Psychology, Educational Psychology, Engelbergerstr.41 79085 Freiburg, Germany Abstract Learning from worked-out examples is of major importance for initial skill acquisition in well-structured domains. In addition, research has provided knowledge in regards to structuring worked-out examples and how to effectively combine self-explanation activity and instructional explanations. The goal of the present project was to develop a computer-based learning environment in which teachers can learn how to use worked-out examples. Examples of favorably and unfavorably designed worked-out examples were the primary source of information for the teachers. The examples (of worked-out examples) were not in themselves worked-out examples if one views them from a design perspective as the (design) solution steps were not given. We have labeled this type of examples solved example problems. We investigated to what extent learning from such solved example problems could be fostered by self-explanation prompts and by providing instructional explanations. The results of our 2x2 design (80 student teachers) showed that prompting self- explanations in particular had favorable effects. Hence, self-explanations fostered learning not only from worked-out examples but also from solved example problems. Supplementary instructional explanations only partially enhanced learning and at times they were even detrimental. Introduction This study applies the results of cognitive science research (i.e., worked-out example and self-explanation research) to the design of a computer-based learning environment. An empirical study about this learning environment, in turn, contributes to the research on example-based learning and self-explanations. Learning from worked-out examples is of major importance for the acquisition of cognitive skills in well-structured domains such as mathematics or physics (for an overview see Atkinson, Derry, Renkl, & Wortham, 2000). However, worked-out examples do not guarantee effective learning. One moderating factor is the learner's self-explanation activity. Only when a learner actively self-explains the rationale of the worked-out solutions to her/himself will s/he gain an understanding of the solution procedures. Another factor is the provision of instructional explanations. In the study presented below, teacher students learned in an example-based computer learning environment how to effectively structure and combine worked-out examples. It was intended to foster their learning by the employment of self-explanation prompts and by supplementary instructional explanations. Learning by Worked-Out Examples Worked-out examples consist of a problem, solution steps and the complete solution to the problem. Usually they can be found in mathematics and physics schoolbooks. In most cases, a principle or law is introduced in the beginning followed by a worked-out example. The worked-out example shows how the principle can be applied to problem solving. Then, problems to be solved by the students are given. Learning by worked-out examples is not meant to refer to the short learning phase between the introduction of a principle and problem-solving. It means, instead, that the example phase is prolonged. Several studies have shown that such example-based learning is more effective for skill acquisition than the standard procedure of studying just one example and then solving problems (for an overview see Sweller, van Merrienboer, & Paas, 1998). Of course, the use of worked-out examples do not guarantee effective learning. Learning outcomes are influenced mainly by (1) the learner's self-explanation activity and the provided instructional explanations and (2) how the learning materials (examples and problems) are structured (cf. Atkinson et al., 2000). These two aspects are discussed in the following sections. Self-Explanations and Instructional Explanations The extent to which learners benefit from the study of worked-out examples depends on how well they explain the rationale of the presented solutions to themselves ( self-explanation effect , Chi et al., 1989; Renkl, 1997; Renkl, Stark, Gruber, & Mandl, 1998). It is especially useful to make the meaning of specific operations explicit by reifying the relationship between (sub-)