Effects of specificity of self-explanation prompts for worked examples on computer-supported learning of collaborative diagnostic reasoning

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-)

BackgroundExample-based learning using worked examples can foster clinical reasoning. Worked examples are instructional tools that learners can use to study the steps needed to solve a problem. Studying worked examples paired with completion examples promotes acquisition of problem-solving skills more than studying worked examples alone. Completion examples are worked examples in which some of the solution steps remain unsolved for learners to complete. Providing learners engaged in example-based learning with self-explanation prompts has been shown to foster increased meaningful learning compared to providing no self-explanation prompts. Concept mapping and concept map study are other instructional activities known to promote meaningful learning. This study compares the effects of self-explaining, completing a concept map and studying a concept map on conceptual knowledge and problem-solving skills among novice learners engaged in example-based learning.MethodsNinety-one physiotherapy students were randomized into three conditions. They performed a pre-test and a post-test to evaluate their gains in conceptual knowledge and problem-solving skills (transfer performance) in intervention selection. They studied three pairs of worked/completion examples in a digital learning environment. Worked examples consisted of a written reasoning process for selecting an optimal physiotherapy intervention for a patient. The completion examples were partially worked out, with the last few problem-solving steps left blank for students to complete. The students then had to engage in additional self-explanation, concept map completion or model concept map study in order to synthesize and deepen their knowledge of the key concepts and problem-solving steps.ResultsPre-test performance did not differ among conditions. Post-test conceptual knowledge was higher (P < .001) in the concept map study condition (68.8 ± 21.8%) compared to the concept map completion (52.8 ± 17.0%) and self-explanation (52.2 ± 21.7%) conditions. Post-test problem-solving performance was higher (P < .05) in the self-explanation (63.2 ± 16.0%) condition compared to the concept map study (53.3 ± 16.4%) and concept map completion (51.0 ± 13.6%) conditions. Students in the self-explanation condition also invested less mental effort in the post-test.ConclusionsStudying model concept maps led to greater conceptual knowledge, whereas self-explanation led to higher transfer performance. Self-explanation and concept map study can be combined with worked example and completion example strategies to foster intervention selection.Electronic supplementary materialThe online version of this article (doi:10.1186/s12909-015-0308-3) contains supplementary material, which is available to authorized users.

This study examined the effects of worked examples with different explanation types and novices’ motivation on cognitive load, and how this subsequently influenced their programming problem-solving performance. Given the study’s emphasis on both instructional approaches and learner motivation, the Cognitive Theory of Multimedia Learning served as the theoretical framework, as it integrates instructional design with motivational perspectives. The participants consisted of 75 university students who were non-computer majors and enrolled in their first programming course. A 2 × 2 between-subjects ANOVA design was employed, with two factors: explanation type (worked examples with instructional explanations vs. worked examples with guided questions to prompt self-explanations) and learner motivation level (high-motivated vs. less-motivated). The dependent variables included cognitive load components experienced by learners during learning and learning outcomes measured by retention and transfer performance. The results showed that (a) the worked example effect could reduce extraneous load and manage intrinsic load, thereby enhancing retention performance; (b) the combined effects of worked examples and guided self-explanations could benefit transfer learning, regardless of learners’ motivation; and (c) the role of motivation was evident, as high-motivated learners exhibited better retention and transfer performance by exerting more cognitive effort, regardless of instructional approach. The findings suggest that combining worked examples with guided questions to prompt self-explanations through in-code commenting is an effective and constructive activity, enabling novices to focus on actual learning without expending excessive cognitive effort.

Conditions and effects of example elaboration

Worked examples support initial skill acquisition. They often show skill application on content knowledge from another, "exemplifying" domain (e.g., argumentation skills have to be applied to some contents). Although learners' focus should remain on the skill, learners need to understand the content knowledge to benefit from worked examples. Previous studies relied on exemplifying domains that are familiar and contain simple topics, to keep learners' focus on skill acquisition. We examined whether using a relevant exemplifying domain would allow learners to acquire both skills and content knowledge simultaneously, or whether relevant content distracts from the main learning goal of skill acquisition. In a training study with 142 psychology students, we used example-based learning materials with an exemplifying domain that was either relevant or irrelevant for participants' course outcomes. We assessed cognitive load, declarative knowledge about skills and course-related content knowledge, and argumentation quality. Incorporating relevant content knowledge in worked examples did not reduce learning outcomes compared to a condition using an irrelevant exemplifying domain. Contrary to previous research, the results suggest that worked examples with a relevant exemplifying domain could possibly be an efficient teaching method for fostering skills and content knowledge simultaneously.

Cognitive load theory states that one way to optimise learning is to decrease extraneous cognitive load, defined as information not relevant to task completion. Worked examples, which show the learner the logic behind the solving of a problem, can decrease extraneous load. However, there is little research to guide the optimal formatting of worked examples. In a crossover design, first-year medical students were randomised to worked examples of bradycardias with salient features first and tachycardias with discriminatory features second (n=33) or worked examples of bradycardias with discriminatory features first and tachycardias with salient features second (n=32). After each learning phase, participants completed a testing phase. Diagnostic accuracy and reported cognitive load were compared between the two worked example formats, as well as with data for a group of historical controls, consisting of medical students interpreting electrocardiogram rhythms without worked examples. Each module concluded with a questionnaire in which the learner was asked to rate his or her perceptions of the difficulty of the core content, the clarity with which the information was presented, and perceived learning. Worked examples highlighting salient and discriminatory features were associated with similar levels of diagnostic accuracy (56% and 60%, respectively; P=.32). Both worked example conditions were associated with higher diagnostic accuracy than was found in historical controls (P<.0001). There was no difference in the extraneous load experienced between worked examples highlighting salient features and those highlighting discriminatory features (12.5±6.1 and 11.9±6.1, respectively; P=.52). Participants reported greater intrinsic load in the worked examples highlighting salient rather than discriminatory features (17.1±4.9 and 15.5±4.6, respectively; P=.01). Discriminatory feature-based worked examples were associated with less intrinsic cognitive load, but this did not translate into any meaningful difference in diagnostic performance. Instruction with worked examples improved diagnostic performance regardless of whether salient or discriminatory features were highlighted.

The purpose of this study is to derive appropriate implications for teaching and learning mathematics using the worked examples. We analyzed how the cooperative learning using the worked examples, fully worked examples(FWE) and partially worked examples(PWE), affects students' mathematics achievement and cognitive load. 258 first year middle school students participated and solved 4 problems applying for the linear equations by small groups. The results of the study are as follows. First, there was no significant difference in mathematics achievement between the students who learned using the worked examples and the students who learned using the problems not including their solutions. Second, there were the differences in intrinsic, extraneous, and germane cognitive load between the students who learned using the worked examples and the students who learned using the problems, but there was not a difference in transactional cognitive load. Students using the FWE perceived the intrinsic and extraneous cognitive load as low, and germane cognitive load as high. Third, in the groups with a high level of achievement, students using the PWE perceived germane cognitive load more than students using the problems. In the middle level, the extraneous cognitive load of the students using the FWE was low, and in the low level, the intrinsic cognitive load of the students who using the FWE was low.

ABSTRACTPrevious research has indicated the disconnect between example-based research focusing on worked examples (WEs) and that focusing on modeling examples. The purpose of this study was to examine and compare the effect of four different types of examples from the two separate lines of research, including standard WEs, erroneous WEs, expert (masterly) modeling examples, and peer (coping) modeling examples, on student performance (knowledge retention, near transfer, and far transfer), cognitive load, and self-efficacy. One hundred and sixteen students participated in the study by undergoing computer-based instruction in one of the four versions differing in how examples were provided. The results showed that, overall, expert modeling examples were most effective in promoting knowledge retention, near transfer, and far transfer, while peer modeling examples were shown to be superior in fostering self-efficacy among the four different types of examples.

Research in a variety of domains has shown that viewing worked examples (WEs) can be a more efficient way to learn than solving equivalent problems. We designed a Peer Code Helper system to display WEs, along with scaffolded self-explanation prompts, in a block-based, novice programming environment called \snap. We evaluated our system during a high school summer camp with 22 students. Participants completed three programming problems with access to WEs on either the first or second problem. We found that WEs did not significantly impact students' learning, but may have impacted students' intrinsic cognitive load, suggesting that our WEs with scaffolded prompts may be an inherently different learning task. Our results show that WEs saved students time on initial tasks compared to writing code, but some of the time saved was lost in subsequent programming tasks. Overall, students with WEs completed more tasks within a fixed time period, but not significantly more. WEs may improve students' learning efficiency when programming, but these effects are nuanced and merit further study.

Starting from problems of knowledge application in the domain of empirical re-search methods and findings from a field study on example-based learning in this domain, a complex worked-out example was employed under experimental conditions. The completeness of the information provided in the example (no gaps vs. gaps) and the provision of questions of understanding (no questions vs. questions) was varied, that is a 2×2-factorial design was used. For all conditions, a distinct learning progress could be achieved by employing the complex worked-out example. However, learning outcomes were neither fostered by the gaps nor by the questions of understanding. The instructional means also had no influence on cognitive load and on motivational aspects. However, various characteristics of the students proved to be important influencing factors

This research aims to analyze worked examples in mathematics learning and mathematical problem solving abilities with worked examples from the 2019-2024 period. This research uses Systematic Literature Review (SLR) sourced from Google Scholar. 21 articles were collected, 15 articles related to worked examples in mathematics learning and 6 articles related to mathematical problem solving abilities using worked examples. The results obtained by using worked examples in mathematics learning can minimize students' cognitive load, help teachers teach new material concepts and deepen students' understanding of the concepts they have learned, then they can improve mathematical abilities, then worked examples are used to develop learning modules and LKPD or worksheets. Using worked examples improves mathematical problem solving abilities. Apart from that, the ability to solve problems using worked examples can be combined with other learning such as combining it with tracing gestures, differentiation and integrated worked examples

The contributions to this special issue document some recent advances of cognitive load theory, and are based on contributions to the Third International Cognitive Load Theory Conference (2009), Heerlen, The Netherlands. The contributions focus on developments in example-based learning, amongst others on the effects of integrating worked examples in cognitive tutoring systems; specify the effects of transience on cognitive load and why segmentation may help counteract these effects in terms of the role of time in working memory load; and discuss the possibilities offered by electroencephalography (EEG) to provide a continuous and objective measure of cognitive load. This article provides a short introduction to the contributions in this issue.

One promising technique for helping students in fulfilling complex problem solving tasks is through learning with worked example. Although worked example approach is the most prominent technique discussed in the literature of cognitive load theory, there is still very little scientific understanding of managing the students’ sub-cognitive load, namely intrinsic, extraneous and germane load on different complexity of worked example approach especially in engineering domain. Thus, this study was conducted to investigate the effects worked examples presentation on sub-cognitive loads among electrical engineering students. In this research, the worked examples were presented in three different sequences: (i) all examples were of same level of difficulty (ii) from difficult to easy example; (iii) from easy to difficult example. Data were collected from 82 students (Condition (i) =27; Condition (ii) = 34; condition (iii) = 21) of Diploma in Electrical Engineering Program at three selected polytechnics. The inventory tool of sub cognitive load were given to students after each teaching and learning session ends. The findings suggested that worked examples presented in easy to difficult format could be one of the approaches that is more efficient to manage students’ cognitive load and effective to be applied in engineering lessons.

Out of the three major constituents of cognitive load theory, intrinsic is the most crucial as it relates to difficulty of learning material. Difficulty of learning material is determined by two factors: the learner's prior knowledge and interacting elements present in the task. The proposed study investigated the role of two formats of worked examples (full and worked) in mathematical problem solving taking into account the learner's prior knowledge and difficulty of the presented material. One hundred and sixty participants were recruited for the study. The participants solved algebraic systems of equations by either using full or completion worked examples approach. Participants were identified as low-prior-knowledge learners or high-prior-knowledge learners based on their performance on the prior knowledge test, using a median split method in which the top one-third and lower one-third participants were retained, with the middle one-third excluded from final analyses. Results indicated that both low- and high-prior-knowledge learners found completion worked examples to be beneficial in solving easy problems and full worked examples in solving difficult problems. This finding is contradictory to the expertise reversal effect. Significant positive correlation was found between intrinsic and germane cognitive load while a significant negative correlation was found between extraneous and germane cognitive load. Both of these significant correlations are aligned with proposals from previous research. Results of the motivation questionnaire indicated that interest was significantly positively correlated with germane load implying that interest in the instructional domain is an important determinant in effecting germane load.

The carbon cycle is the way in which carbon atoms, in various compounds, circulate through nature. The S-Note application has a handwriting mode feature that can be used in drawing object. Worked example are study approaches that provide step by step solutions for solving problems in a study. Worked example can reduce intrinsic cognitive load and germane cognitive load. The purpose of this study is to obtain information about worked example using the S-Note application in carbon cycle study. This research method is descriptive. The S-Note application is used in making carbon cycle images. The carbon cycle drawing consists of nine steps. The results of carbon cycle images are images that have JPEG format. The results show that worked example using the S-Note application can help study carbon cycles and show low categorization for intrinsic cognitive load, and very low categorization for germane cognitive load. Conclusion worked example using the S-Note application can reduce intrinsic cognitive load and germane cognitive load in carbon cycle study.