Example, nudge, or practice? Assessing metacognitive knowledge transfer of factual and procedural learners

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, CYBERNETICS User Modeling and User-Adapted Interaction Pub Date : 2024-07-01 DOI:10.1007/s11257-024-09404-2
Mark Abdelshiheed, Robert Moulder, John Wesley Hostetter, Tiffany Barnes, Min Chi
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Abstract

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.

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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
事实性知识和程序性知识分别是指 "那 "和 "如何 "的知识,而条件性知识是指 "何时 "和 "为什么 "的元认知知识。之前的研究发现,拥有条件性知识的学生会自发地在智能辅导系统中转移这些知识,因此本研究评估了元认知干预对事实性和程序性学生知识转移的影响。具体来说,我们采用了主体间、前/后测试设计,将事实型和程序型学生随机分配到范例、提示、练习或控制条件中。干预措施教授了如何以及何时在支持默认前向连锁策略的逻辑导师上使用后向连锁(BC)策略。同时,有条件的学生不接受任何干预。六周后,我们对所有学生进行了概率导师培训,该导师只支持 BC,不进行干预。我们的研究结果表明,对事实型学生的鼓励和对程序型学生的练习,是他们在两个导师上赶上条件型学生的关键因素,也是促进从逻辑导师到概率导师的知识转移的关键因素。我们讨论了我们的研究结果的两个潜在互补理论:基于选择的理论(从干预到知识)和基于元认知负荷的理论(从知识到干预)。基于选择的理论将干预措施中的选择数量与知识类型联系起来,而基于元认知负荷的理论则将知识类型与每种干预措施提供的元认知负荷联系起来。讨论了对实践的影响。
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来源期刊
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction 工程技术-计算机:控制论
CiteScore
8.90
自引率
8.30%
发文量
35
审稿时长
>12 weeks
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
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