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自我调节学习是成功的在线学习的关键,教师支持作为一个重要的社会性支持因素,对在线学习者的自我调节学习具有重要影响。基于327名大学生的问卷调查数据分析发现,在线学习者感知的教师支持处于中等程度,其自我调节学习水平不高;教师支持对在线学习者的自我调节学习具有显著正向作用;教师的自主支持和认知支持直接影响在线学习者的自我调节学习,而情感支持通过自主支持和认知支持起到间接影响作用。基于此,在在线学习中,需要加强对学习者自我调节学习的诊断和干预,构建以自我调节学习为导向的教师支持体系,优化教师支持实践策略。 相似文献
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自我调节学习已经成为西方教育心理学和学习心理学研究领域中的一个热点。自我调节学习不仅仅表现为学习者学习过程中的具体行为策略,稳定的自我调节系统也成为学习者本人的一种优秀个性品质,构成其人格的核心。不同学者对小学生自我调节学习具有不同的理论观点。小学生自我调节学习指导具有重要性、可能性和可行性。影响小学生自我调节学习的因素包括个体内部因素和环境外部因素。小学生自我调节学习指导的具体策略包括师生责任转变、指导利用时间、演示教授技巧、与课程的整合、提供社会支持等策略。 相似文献
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在线学习环境下开展复杂知识的教学实践往往效果不佳,有学者尝试使用认知弹性理论来改善教学设计、优化学习效果,但基于此理论设计的非线性超文本结构对学习者的自我调节学习又提出了更高的要求。自我调节学习行为对学习成绩的影响不是简单的对称线性关系,而是以整体的组合方式作用于学习过程。文章采用定性比较分析法,从组态视角深度剖析认知弹性设计下影响自我调节学习行为的内在机制,探讨各行为及各行为间组合导致不同学习成绩的原因。研究发现:(1)计划阶段的设置目标行为是导致较好学习成绩的必要不充分条件;(2)计划阶段的制订计划行为是影响学习成绩的无关条件;(3)执行阶段的自我监测和自我调节行为能起到明显的补益作用;(4)反思阶段进行适应性自我反应行为对提高学习成绩更有效。研究结论验证了早期发现,完善了自我调节学习理论,补充并拓展了在线学习的认知弹性设计。 相似文献
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采用混合方法研究大学英语听力学习者的在线自我调节（Online Self-Regulated,简称OSR学习过程。定量数据来自354名完成在线问卷的学习者。定量数据显示,学习者的OSR处于中等水平,任务策略的OSR值最高,学习求助的OSR值最低。通过学习者的日志数据,以及对6名大学英语听力学习者进行的半结构化访谈,进一步获得对这些发现的有趣见解。三类英语听力学习者的在线自我调节学习分别呈稳步、螺旋和缓慢发展趋势。低、中OSR水平的学习者主要将其OSR水平归因于教师的监督或同学的影响,而高OSR水平的学习者主要将其OSR水平归因于内在动机。 相似文献
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在线自我调节学习是增强学习者在线学习投入、提升学习绩效的重要保障。当下在线开放课程在各级各类教育中广泛深度应用,国内外学者主要关注高等教育全日制学习者在线自我调节学习的相关研究,对学历继续教育学习者的关注较为罕见。采用混合研究法,对疫情防控期间,陕西、广东、云南等三省参与在线课程学习的学历继续教育学习者在线自我调节学习的现状、影响因素及优化策略进行了系统研究,结果发现:研究样本在线自我调节学习现状不是很理想,主要受个人因素、课程因素、文化因素和管理因素的影响,最终从优化课程、重构学习空间、加强过程监控等三个方面提出了调节和优化策略。 相似文献
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论自我调节学习的影响因素 总被引：3，自引：0，他引：3
自我调节学习指学习者在认知和行为上积极主动地参与学习活动、控制学习进程、达到学习目标的过程。我们把学生看成是一个积极主动的主体 ,就是将其看成是能够参与并控制学习过程的自我调节主体。我们强调培养学生的自我教育能力 ,使学生学会如何学习 ,都是在强调使学生进行自我调节学习。自我调节学习要求学习者在认知调控方面能够有计划地组织学习活动 ,能自己设置目标、自我监控和自我评价 ;在动机方面要把自己看成是自主的、具有学习能力感 ;在行为方面能够清楚地意识自己所拥有的学习策略的有效性 ,并能够运用适当的策略选择和建构有利… 相似文献
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自我调节学习自20世纪80年代提出至今已成为教育心理学研究的热点.关于自我调节学习的研究主要可分为两个取向:早期取向是将自我调节学习看作个体的能力,该取向多采用访谈法、问卷法加以测评;近年来随着研究的深入和计算机技术的发展,一种新的研究取向则是将自我调节学习视作一种事件,该取向的测评包括超媒体环境下出声思维测量、录像分析及提示性访谈、在线元认知判断、计算机辅助学习中的学习痕迹分析等.这些方法为研究者提供有关自我调节学习的更为真实、可信、细化的数据,带动自我调节学习的研究进入新的境地.本文将重点介绍基于计算机环境下自我调节学习研究的主要范式,以期为相关的研究者提供参照. 相似文献
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近年来,众多学者将学习分析应用于自我调节学习以优化学习过程和学习效果,但当前学习分析的应用多以数据为驱动,尚未涉及学习者内在要素的变化规律,无法得知学习分析是如何影响自我调节学习的。为回答该问题,研究首先基于现有研究和理论归纳学习分析与自我调节学习的内在联系。其次,以学习分析的普遍应用模式为背景,收集学习者自我调节学习后的反思和访谈数据,利用认知网络分析和语义分析挖掘自我调节学习受学习分析影响的要素变化规律和各阶段触发特征。最后,总结变化规律和触发特征得到学习分析对自我调节学习的影响机理。结果表明,学习分析介入后,切实促进了自我调节学习各要素的融合,且在自我调节学习的不同阶段,学习分析触发自我调节学习发生的起点和路径存在差异。研究结论为促进学习分析更深入地支持自我调节学习提供了发展方向和理论依据。 相似文献
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自我调节学习是指学习者主动地调节与控制动机、元认知与行为进行学习的过程,它有利于学习的成功与目标的达成.成人的自我调节学习过程是一个螺旋上升的循环模式,大体经历了以下四个阶段:学习起点评价、确定目标与选择策略、策略执行与意志控制、学习结果的反思. 相似文献
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随着信息技术与教育的进一步融合,混合多种学习环境下的学习已经成为信息化时代发展的潮流,在混合式学习环境中起关键性作用的自我调节学习受到了广泛关注.目前关于混合学习环境下自我调节学习的研究大多针对在线学习的部分,文章为全面探索混合式学习环境中的自我调节学习,利用学习管理系统开展了为期14周的"虚拟线上"和"线下"相结合的课程,分别将学习管理系统中自我调节学习的行为数据和自我报告与学生成绩进行分析.结果表明自我报告无法对成绩进行预测,而学习管理系统的行为数据能有效地预测学生的成绩.为未来混合式课程中自我调节学习的相关研究奠定基础. 相似文献
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采用问卷调查的方法,以武汉地区试点高校的英语网络学习者为研究对象,分析了其网络自主学习的能力现状,探讨了网络自主学习模式的构建,提供了培养网络自主学习能力的途径。 相似文献
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Dongho Kim In-Heok Lee Joo-Ho Park 《British journal of educational technology : journal of the Council for Educational Technology》2019,50(6):3420-3436
Open educational resources (OERs) are increasingly adopted in non-formal education contexts. However, little research has been conducted to investigate the self-directed learning patterns of non-formal learners using OERs. A lack of knowledge about non-formal learners precludes discussions on how to support their self-regulated learning in open learning environments. We explored the self-directed learning patterns of 1892 non-formal online learners who were using OER repositories. In addition, we examined whether differences existed in future intentions to use OERs between learners with different learning patterns. Four latent classes were identified, and learners’ age was found to affect class membership. We also found that non-formal learners’ use of self-directed learning strategies predicted their future intentions to use OERs. This study advanced our understanding of the subpopulations of non-formal learners using OERs. Future research will benefit from the findings related to designing OER environments that can accommodate diverse self-directed learners. 相似文献
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Jaclyn Broadbent Matthew Fuller-Tyszkiewicz 《Educational technology research and development : ETR & D》2018,66(6):1435-1455
This study examines a person-centered approach to self-regulated learning among 606 University students (140 online, and 466 in blended learning mode). Latent profile analysis revealed five distinct profiles of self-regulated learning: minimal regulators, restrained regulators, calm self-reliant capable regulators, anxious capable collaborators, and super regulators. These profiles showed that: (1) differences in academic success are associated with a learner’s capacity for motivational regulation and self-regulated learning strategy implementation, (2) online learners are more likely to belong to profiles that are more adaptive, and less reliant on collaborations with others, (3) for learners at the lower end of the self-regulation spectrum, an increase in both motivational regulation and adoption of self-regulated learning strategies may be academically beneficial, and (4) high motivational regulation and strategy adoption can be all for naught, if the student is also highly anxious with worry and concern regarding performance. 相似文献
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基于探究社区理论,探讨影响在线学习认知形成的各种临场感因素,构建结构关系模型,并对各临场感的结构关系进行相关、多重中介、调节分析。采用调查问卷的方式,对利用Sakai网络虚拟教学平台进行在线学习的614名大二本科生进行调查。分析结果表明:在线学习中教学行为与学习认知具有间接显著正相关,教学通过社交临场和学习者临场间接影响学习认知,自我调节学习对学习认知具有调节作用。研究揭示了在线学习中教学活动通过学习交互和自我效能对学习认知产生影响的内在机制以及自我调节学习的调节作用对学习认知形成带来的个体差异。 相似文献
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试论自主学习的理论依据、内涵与特征 总被引：11，自引：1，他引：11
吴健强 《湖南广播电视大学学报》2004,(1):1-4
自主学习是学习主导自己的学习的主动建构的过程，是建立在教育的终身化、民主化和个性化等教育观上的一种教育策略。同时，自主学习也是一个相对意义上的概念，自主学习的自主不是绝对的。自主学习具有自主性、能动性、超前性、独立性、独特性、创造性等特点，自主性是自主学习的本质属性。 相似文献
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Roger Azevedo Daniel C. Moos Jeffrey A. Greene Fielding I. Winters Jennifer G. Cromley 《Educational technology research and development : ETR & D》2008,56(1):45-72
We examined how self-regulated learning (SRL) and externally-facilitated self-regulated learning (ERL) differentially affected
adolescents’ learning about the circulatory system while using hypermedia. A total of 128 middle-school and high school students
with little prior knowledge of the topic were randomly assigned to either the SRL or ERL condition. Learners in the SRL condition
regulated their own learning, while learners in the ERL condition had access to a human tutor who facilitated their self-regulated
learning. We converged product (pretest-posttest shifts in students’ mental models and declarative knowledge measures) with process (think-aloud protocols) data to examine the effectiveness of self- versus externally-facilitated regulated learning. Findings
revealed that learners in the ERL condition gained statistically significantly more declarative knowledge and that a greater
number of participants in this condition displayed a more advanced mental model on the posttest. Verbal protocol data indicated
that learners in the ERL condition regulated their learning by activating prior knowledge, engaging in several monitoring
activities, deploying several effective strategies, and engaging in adaptive help-seeking. By contrast, learners in the SRL
condition used ineffective strategies and engaged in fewer monitoring activities. Based on these findings, we present design
principles for adaptive hypermedia learning environments, engineered to foster students’ self-regulated learning about complex
and challenging science topics.
Roger Azevedo is an Associate Professor in the Department of Psychology at the University of Memphis. His research interests include the role of self-regulated learning about challenging science topics with open-ended learning environments and using computers as metacognitive tools for enhancing learning. Daniel C. Moos is an Assistant Professor in the Department of Education at Gustavus Adolphus College. His research interests include the role of prior knowledge and motivation, and self-regulated learning with computer-based learning environments. Jeffrey A. Greene is an Assistant Professor in the School of Education at University of North Carolina, Chapel Hill. His research interests include the epistemic and ontologic cognition, quantitative methods, cognition and learning, and self-regulated learning with computer-based learning environments. Fielding I. Winters is a doctoral student in the Department of Human Development at the University of Maryland. Her research interests include students’ learning about science with computer-based learning environments. Jennifer G. Cromley is an Assistant Professor in the Department of Psychological Studies in Education at Temple University. Her research interests include the reading comprehension, adolescent literacy, applied educational statistics and measurement, and self-regulated learning. 相似文献
| Roger AzevedoEmail: |
Roger Azevedo is an Associate Professor in the Department of Psychology at the University of Memphis. His research interests include the role of self-regulated learning about challenging science topics with open-ended learning environments and using computers as metacognitive tools for enhancing learning. Daniel C. Moos is an Assistant Professor in the Department of Education at Gustavus Adolphus College. His research interests include the role of prior knowledge and motivation, and self-regulated learning with computer-based learning environments. Jeffrey A. Greene is an Assistant Professor in the School of Education at University of North Carolina, Chapel Hill. His research interests include the epistemic and ontologic cognition, quantitative methods, cognition and learning, and self-regulated learning with computer-based learning environments. Fielding I. Winters is a doctoral student in the Department of Human Development at the University of Maryland. Her research interests include students’ learning about science with computer-based learning environments. Jennifer G. Cromley is an Assistant Professor in the Department of Psychological Studies in Education at Temple University. Her research interests include the reading comprehension, adolescent literacy, applied educational statistics and measurement, and self-regulated learning. 相似文献
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动态在线讨论：交互式学习环境中的深层学习 总被引：3，自引：0，他引：3
本文阐述了在远程教育中进行面向深层学习的动态在线讨论的一个框架。该框架建立在信息、方法和认知等三个普遍性过程的基础上。在本框架中进行动态讨论的结构包括三种在线讨论的类型,即灵活的同伴、结构化的主题和协作性任务讨论。该框架在一门有关教学多媒体设计的在线研究生课程中使用过两个学期。创建动态讨论的策略是为了促进不同学习者之间的在线交互,并有助于设计能实现有效交互的任务。学生通过设计的任务产生了接受性学习,在此基础上促进了适应性学习,并且激发了他们促进深层学习的认知能力。该框架及动态讨论的策略提供了一个在线学习环境,在这样的环境中学生的学习超越了既定的课程目标。 相似文献