Julius Meier, Peter Hesse, Stephan Abele, Alexander Renkl, Inga Glogger-Frey
{"title":"更好的自我解释是向后还是向前?在学习诊断策略的基于视频的建模示例中提示自我解释","authors":"Julius Meier, Peter Hesse, Stephan Abele, Alexander Renkl, Inga Glogger-Frey","doi":"10.1007/s11251-023-09651-7","DOIUrl":null,"url":null,"abstract":"<p>Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented (<i>retrospective</i> prompts). However, it might also help to self-explain upcoming steps (<i>anticipatory</i> prompts). The effects of the prompt type may differ for learners with various expertise levels, with anticipatory prompts being better for learners with more expertise. In an experiment, we employed extensive modelling examples and different types of self-explanations prompts to teach 78 automotive apprentices a complex and job-relevant problem-solving strategy, namely the diagnosis of car malfunctions. We tested the effects of these modelling examples and self-explanation prompts on problem-solving strategy knowledge and skill, self-efficacy, and cognitive load while learning. In two conditions, the apprentices learned with modelling examples and received either retrospective or anticipatory prompts. The third condition was a control condition receiving no modelling examples, but the respective open problems. In comparison with the control condition, modelling examples did not promote learning. However, we observed differential effects of the self-explanation prompts depending on the learner’s prior knowledge level. Apprentices with higher prior knowledge learned more when learning with anticipatory prompts. Apprentices with less prior knowledge experienced a greater increase in self-efficacy and a higher germane cognitive load when learning with retrospective prompts. These findings suggest using different self-explanation prompts for learners possessing varying levels of expertise.</p>","PeriodicalId":47990,"journal":{"name":"Instructional Science","volume":"12 1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Better self-explaining backwards or forwards? Prompting self-explanation in video-based modelling examples for learning a diagnostic strategy\",\"authors\":\"Julius Meier, Peter Hesse, Stephan Abele, Alexander Renkl, Inga Glogger-Frey\",\"doi\":\"10.1007/s11251-023-09651-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented (<i>retrospective</i> prompts). However, it might also help to self-explain upcoming steps (<i>anticipatory</i> prompts). The effects of the prompt type may differ for learners with various expertise levels, with anticipatory prompts being better for learners with more expertise. In an experiment, we employed extensive modelling examples and different types of self-explanations prompts to teach 78 automotive apprentices a complex and job-relevant problem-solving strategy, namely the diagnosis of car malfunctions. We tested the effects of these modelling examples and self-explanation prompts on problem-solving strategy knowledge and skill, self-efficacy, and cognitive load while learning. In two conditions, the apprentices learned with modelling examples and received either retrospective or anticipatory prompts. The third condition was a control condition receiving no modelling examples, but the respective open problems. In comparison with the control condition, modelling examples did not promote learning. However, we observed differential effects of the self-explanation prompts depending on the learner’s prior knowledge level. Apprentices with higher prior knowledge learned more when learning with anticipatory prompts. Apprentices with less prior knowledge experienced a greater increase in self-efficacy and a higher germane cognitive load when learning with retrospective prompts. These findings suggest using different self-explanation prompts for learners possessing varying levels of expertise.</p>\",\"PeriodicalId\":47990,\"journal\":{\"name\":\"Instructional Science\",\"volume\":\"12 1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2023-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Instructional Science\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://doi.org/10.1007/s11251-023-09651-7\",\"RegionNum\":3,\"RegionCategory\":\"教育学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"EDUCATION & EDUCATIONAL RESEARCH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Instructional Science","FirstCategoryId":"95","ListUrlMain":"https://doi.org/10.1007/s11251-023-09651-7","RegionNum":3,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"EDUCATION & EDUCATIONAL RESEARCH","Score":null,"Total":0}
Better self-explaining backwards or forwards? Prompting self-explanation in video-based modelling examples for learning a diagnostic strategy
Self-explanation prompts in example-based learning are usually directed backwards: Learners are required to self-explain problem-solving steps just presented (retrospective prompts). However, it might also help to self-explain upcoming steps (anticipatory prompts). The effects of the prompt type may differ for learners with various expertise levels, with anticipatory prompts being better for learners with more expertise. In an experiment, we employed extensive modelling examples and different types of self-explanations prompts to teach 78 automotive apprentices a complex and job-relevant problem-solving strategy, namely the diagnosis of car malfunctions. We tested the effects of these modelling examples and self-explanation prompts on problem-solving strategy knowledge and skill, self-efficacy, and cognitive load while learning. In two conditions, the apprentices learned with modelling examples and received either retrospective or anticipatory prompts. The third condition was a control condition receiving no modelling examples, but the respective open problems. In comparison with the control condition, modelling examples did not promote learning. However, we observed differential effects of the self-explanation prompts depending on the learner’s prior knowledge level. Apprentices with higher prior knowledge learned more when learning with anticipatory prompts. Apprentices with less prior knowledge experienced a greater increase in self-efficacy and a higher germane cognitive load when learning with retrospective prompts. These findings suggest using different self-explanation prompts for learners possessing varying levels of expertise.
期刊介绍：
Instructional Science, An International Journal of the Learning Sciences, promotes a deeper understanding of the nature, theory, and practice of learning and of environments in which learning occurs. The journal’s conception of learning, as well as of instruction, is broad, recognizing that there are many ways to stimulate and support learning. The journal encourages submission of research papers, covering a variety of perspectives from the learning sciences and learning, by people of all ages, in all areas of the curriculum, in technologically rich or lean environments, and in informal and formal learning contexts. Emphasizing reports of original empirical research, the journal provides space for full and detailed reporting of major studies. Regardless of the topic, papers published in the journal all make an explicit contribution to the science of learning and instruction by drawing out the implications for the design and implementation of learning environments. We particularly encourage the submission of papers that highlight the interaction between learning processes and learning environments, focus on meaningful learning, and recognize the role of context. Papers are characterized by methodological variety that ranges, for example, from experimental studies in laboratory settings, to qualitative studies, to design-based research in authentic learning settings. The Editors will occasionally invite experts to write a review article on an important topic in the field. When review articles are considered for publication, they must deal with central issues in the domain of learning and learning environments. The journal accepts replication studies. Such a study should replicate an important and seminal finding in the field, from a study which was originally conducted by a different research group. Most years, Instructional Science publishes a guest-edited thematic special issue on a topic central to the journal''s scope. Proposals for special issues can be sent to the Editor-in-Chief. Proposals will be discussed in Spring and Fall of each year, and the proposers will be notified afterwards. To be considered for the Spring and Fall discussion, proposals should be sent to the Editor-in-Chief by March 1 and October 1, respectively. Please note that articles that are submitted for a special issue will follow the same review process as regular articles.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
