Mark Abdelshiheed, Robert Moulder, John Wesley Hostetter, Tiffany Barnes, Min Chi
{"title":"范例、点拨还是实践?评估事实性学习者和程序性学习者的元认知知识迁移","authors":"Mark Abdelshiheed, Robert Moulder, John Wesley Hostetter, Tiffany Barnes, Min Chi","doi":"10.1007/s11257-024-09404-2","DOIUrl":null,"url":null,"abstract":"<p>Factual knowledge and procedural knowledge are knowing ‘That’ and ‘How,’ respectively, whereas conditional knowledge is the metacognitive knowledge of ‘When’ and ‘Why.’ As prior work has found that students with conditional knowledge spontaneously transferred such knowledge across intelligent tutoring systems, this work assesses the impact of metacognitive interventions on the knowledge transfer of factual and procedural students. Specifically, we used a between-subject, pre-/posttest design with factual and procedural students, each randomly assigned to either the <i>example</i>, <i>nudge</i>, <i>practice</i>, or <i>control</i> condition. The interventions taught <i>how</i> and <i>when</i> to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Meanwhile, conditional students received no interventions. Six weeks later, we trained all students on a probability tutor that only supports BC without interventions. Our results suggest that nudges for factual students and practice for their procedural peers are the key factors for catching up with conditional students on both tutors and for facilitating knowledge transfer from the logic to probability tutor. We discuss two potential complementary theories for our findings: a choice-based theory (from interventions to knowledge) and a metacognitive load-based theory (from knowledge to interventions). The choice-based theory maps the amount of choice in the interventions to knowledge types, while the metacognitive load-based theory associates knowledge types with the metacognitive load each intervention offers. Implications for practice are discussed.</p>","PeriodicalId":49388,"journal":{"name":"User Modeling and User-Adapted Interaction","volume":"55 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Example, nudge, or practice? 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The interventions taught <i>how</i> and <i>when</i> to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Meanwhile, conditional students received no interventions. Six weeks later, we trained all students on a probability tutor that only supports BC without interventions. Our results suggest that nudges for factual students and practice for their procedural peers are the key factors for catching up with conditional students on both tutors and for facilitating knowledge transfer from the logic to probability tutor. We discuss two potential complementary theories for our findings: a choice-based theory (from interventions to knowledge) and a metacognitive load-based theory (from knowledge to interventions). The choice-based theory maps the amount of choice in the interventions to knowledge types, while the metacognitive load-based theory associates knowledge types with the metacognitive load each intervention offers. 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Example, nudge, or practice? Assessing metacognitive knowledge transfer of factual and procedural learners
Factual knowledge and procedural knowledge are knowing ‘That’ and ‘How,’ respectively, whereas conditional knowledge is the metacognitive knowledge of ‘When’ and ‘Why.’ As prior work has found that students with conditional knowledge spontaneously transferred such knowledge across intelligent tutoring systems, this work assesses the impact of metacognitive interventions on the knowledge transfer of factual and procedural students. Specifically, we used a between-subject, pre-/posttest design with factual and procedural students, each randomly assigned to either the example, nudge, practice, or control condition. The interventions taught how and when to use a backward-chaining (BC) strategy on a logic tutor that supports a default forward-chaining strategy. Meanwhile, conditional students received no interventions. Six weeks later, we trained all students on a probability tutor that only supports BC without interventions. Our results suggest that nudges for factual students and practice for their procedural peers are the key factors for catching up with conditional students on both tutors and for facilitating knowledge transfer from the logic to probability tutor. We discuss two potential complementary theories for our findings: a choice-based theory (from interventions to knowledge) and a metacognitive load-based theory (from knowledge to interventions). The choice-based theory maps the amount of choice in the interventions to knowledge types, while the metacognitive load-based theory associates knowledge types with the metacognitive load each intervention offers. Implications for practice are discussed.
期刊介绍:
User Modeling and User-Adapted Interaction provides an interdisciplinary forum for the dissemination of novel and significant original research results about interactive computer systems that can adapt themselves to their users, and on the design, use, and evaluation of user models for adaptation. The journal publishes high-quality original papers from, e.g., the following areas: acquisition and formal representation of user models; conceptual models and user stereotypes for personalization; student modeling and adaptive learning; models of groups of users; user model driven personalised information discovery and retrieval; recommender systems; adaptive user interfaces and agents; adaptation for accessibility and inclusion; generic user modeling systems and tools; interoperability of user models; personalization in areas such as; affective computing; ubiquitous and mobile computing; language based interactions; multi-modal interactions; virtual and augmented reality; social media and the Web; human-robot interaction; behaviour change interventions; personalized applications in specific domains; privacy, accountability, and security of information for personalization; responsible adaptation: fairness, accountability, explainability, transparency and control; methods for the design and evaluation of user models and adaptive systems