Article(id=1322929690515735207, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.14.027, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752768000000, receivedDateStr=2025-07-18, revisedDate=1776700800000, revisedDateStr=2026-04-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1793340617064, onlineDateStr=2026-10-30, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793340617064, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793340617064, creator=13701087609, updateTime=1793340617064, updator=13701087609, issue=Issue{id=1322929547464815391, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='14', pageStart='2385', pageEnd='2576', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793340582958, creator='13701087609', updateTime=1793343111003, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940150912790807, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940150912790808, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322929547464815391, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2572, endPage=2576, ext={EN=ArticleExt(id=1322929690754810536, articleId=1322929690515735207, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Application progress of eye-tracking technology in nursing education, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=

The application forms,application effects,and existing problems of eye-tracking technology in nursing education were reviewed,so as to provide a reference for the research and application of this technology in nursing education in China.

, authors=Yundong GAO, Wenhua WANG, Jiji YAO, Hong GUO, authorsList=Yundong GAO, Wenhua WANG, Jiji YAO, Hong GUO, authorCompany=null, correspAuthors=Hong GUO, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322929691060994729, articleId=1322929690515735207, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=眼动追踪技术在护理教育中的应用进展, columnId=1220689485482873504, journalTitle=护理研究, columnName=科研综述, runingTitle=null, highlight=null, articleAbstract=

对眼动追踪技术在护理教育中的应用形式、应用效果、存在的问题等进行综述,以期为该技术在我国护理教育中的研究和应用提供参考。

, authors=高运东, 王文华, 姚积极, 郭虹*, *, authorsList=高运东, 王文华, 姚积极, 郭虹, authorCompany=null, correspAuthors=郭虹, authorNote=

高运东,护师,硕士研究生

, correspAuthorsNote=
郭虹,E-mail:
GUO Hong, E-mail:
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高运东,护师,硕士研究生

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眼动追踪技术在护理教育中的应用进展
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高运东 , 王文华 , 姚积极 , 郭虹 *, *
护理研究 | 科研综述 2026,40(14): 2572-2576
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护理研究 |科研综述 2026 , 40 (14) : 2572 -2576
眼动追踪技术在护理教育中的应用进展
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高运东, 王文华, 姚积极, 郭虹*, *
作者信息
  • 1丽水学院医学院,浙江 323000
通讯作者:
郭虹,E-mail:
GUO Hong, E-mail:
作者简介:

高运东,护师,硕士研究生

Application progress of eye-tracking technology in nursing education
Yundong GAO, Wenhua WANG, Jiji YAO, Hong GUO
Affiliations
  • 1School of Medicine, Lishui University, Zhejiang 323000 China
出版时间: 2026-07-25 doi: 10.12102/j.issn.1009-6493.2026.14.027
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对眼动追踪技术在护理教育中的应用形式、应用效果、存在的问题等进行综述,以期为该技术在我国护理教育中的研究和应用提供参考。

眼动追踪技术  /  护理教育  /  视觉注意力  /  情境意识  /  综述

The application forms,application effects,and existing problems of eye-tracking technology in nursing education were reviewed,so as to provide a reference for the research and application of this technology in nursing education in China.

eye-tracking technology  /  nursing education  /  visual attention  /  situational awareness  /  review
高运东, 王文华, 姚积极, 郭虹. 眼动追踪技术在护理教育中的应用进展. 护理研究, 2026 , 40 (14) : 2572 -2576 . DOI: 10.12102/j.issn.1009-6493.2026.14.027
Yundong GAO, Wenhua WANG, Jiji YAO, Hong GUO. Application progress of eye-tracking technology in nursing education[J]. Chinese Nursing Research, 2026 , 40 (14) : 2572 -2576 . DOI: 10.12102/j.issn.1009-6493.2026.14.027
护理教育的核心目标是高效培养学生关键临床能力[1]。然而,涉及高风险场景的技能训练在真实环境中面临安全和伦理挑战,临床模拟训练成为首选方法[2]。传统评估方式在客观性、实时性和洞察学员深层认知过程(如注意分配、信息处理策略、认知负荷)方面存在局限,难以精确评估能力差异与训练效果[3]。眼动追踪技术作为一种客观、连续、无干扰的技术,可以通过记录视觉行为及生理参数为明确受训者在模拟环境中的认知处理过程提供新视角[4-5]。该技术能揭示专家与新手的信息处理差异,提供精准的个体化反馈,评估模拟设计的有效性并优化训练场景,还能通过客观监测认知负荷,为解决护理教育中评估与反馈的瓶颈问题、提升教学效能提供强大支持[6-8]。现对眼动追踪技术在护理教育中的应用形式、应用效果等进行综述,以期为该技术在我国护理教育中的研究和应用提供参考。
眼动追踪技术是一种通过记录和分析眼球运动模式,包括注视、扫视及瞳孔变化,以量化视觉注意分布与认知加工过程的实证研究手段。该技术主要依托静态眼动仪和头戴式系统实现数据采集,其中,静态眼动仪固定于特定显示界面(如计算机屏幕),适用于受控实验室环境;头戴式系统(如集成于智能眼镜的追踪装置)则支持在自然情境或移动操作中的生态化数据收集[9]。眼动追踪技术分析的核心单元为研究者定义的感兴趣区域,用以表征特定视觉元素的认知加工负荷[10]。原始凝视点数据可聚合为时间维度、空间维度、计数维度3类核心指标,其中,时间维度指标包括总注视持续时间、首次注视时间、凝视持续时间、平均注视持续时间和扫视持续时间等;空间维度指标包括注视点空间分布、扫视幅度及描述注视序列动态的扫描路径等;计数维度指标包括注视次数及其回看次数、扫视频率等[11]。这些指标具有明确的认知解释价值,如注视点空间分布反映视觉注意资源分配;扫描路径揭示元认知策略(如信息搜索效率);平均注视持续时间延长通常指示认知负荷增加;注意眨眼现象(即任务诱发的眨眼抑制)以及眨眼频率变化也被视为推断注意力状态的潜在指标[11]。得益于硬件便携性、算法进步及跨学科理论发展,眼动追踪技术在护理教育领域的应用持续深化。
眼动追踪技术通过可视化护理人员在护理操作中的眼动轨迹与注视持续时间解析其视觉行为模式与注意力分配。Maekawa等[12]利用EMR-dFactory眼动追踪软件分析护士的静脉注射操作情况,结果发现,经验丰富的护士在穿刺点的注视持续时间长于学生,表明其专注力高效集中于血管定位,学生注视则较为分散,在非关键区域(如物品、止血带)停留过久,体现了观察策略的不成熟。Sugimoto等[13]采用ViewTracker 3眼动追踪设备监测多层级护理人员(护理专家、新入职护士、学生)的静脉注射眼动轨迹,结果发现,护理专家视觉高度集中于核心区域(病人面部、注射部位),而学生则会频繁注视无关环境要素(输液袋、操作室)。Sanchez等[14]通过ViewTracker 3采集护理专家和学生的静脉注射眼动数据,其结合了状态转移图与热力图,结果显示,护理专家更注重装置确认与滴速监控等精细化环节,学生则对此类关键步骤存在视觉忽视。Cai等[15]运用Tobii Glasses可穿戴眼动追踪设备记录护理专家和学生的腹部注射任务眼动状况,结果发现,护理专家具备动态眼动调控能力,在准备阶段会以大扫视幅度定位关键点,注射阶段则减小扫视幅度以维持精度;学生则呈现静态扫视模式,过度关注手指局部而忽视系统化视觉管理。Dubovi[16]将眼动追踪应用于虚拟现实(VR)药物辨识训练,结果发现,学生面对相似包装时延长注视时间与降低眨眼频率的生理指标与其高认知负荷状态相关。Sugimoto等[17]通过ViewTracker 3眼动追踪分析失禁病人护理垫护理操作情况,结果发现,教师与高阶学生通过高频次区域注视切换维持全局监控,而低阶学生固着于单一物品(新垫子),常忽略病人体征(如臀部状态),导致操作效率低下。
眼动追踪技术可以通过提供超越主观自我报告的客观视觉注意指标揭示护士在模拟复苏场景中的认知偏差与技能不足,从而为优化其临床决策与操作效能提供针对性指导[18]。Wagner等[19]应用Tobii Pro Glass 2.0眼动追踪设备监测医学生、护士及儿科重症监护人员在多样化复苏情景下的视觉注意模式,通过实时记录受试者的注视点,量化反馈设备对复苏者视觉注意分配的调控作用,促进受试者将注意力集中于设备本身,进而提升胸外按压与通气操作质量。Caloca-Amber等[18]利用Pupil Core头戴式眼动追踪设备记录不同年级护生面对心脏呼吸骤停临床模拟情境时的视觉注意特征,通过量化受试者在6个关键区域(面部、胸部、锁骨下区域、心电图显示屏等)的注视次数、回视次数以及注视持续时间发现,低年级护生往往会长时间注视模拟人胸部区域,而高年级护生会高频注视监护设备与除颤控制界面,呈现注视定位精准性(低频率、短时长)特征。Gröpel等[20]采用Tobii Pro Glass 2.0眼动眼镜记录医学生、住院医师、护士及顾问医师在儿科生命支持模拟场景中进行胸外按压和通气操作时的视觉注意行为,结果发现,胸外按压质量(如按压深度、速率)与受试者在婴儿胸部区域的注视时长呈正相关,与分散至非任务相关(注意力分散)区域的注视时长呈负相关;长时间注视婴儿胸部还与较低的通气泄漏率相关。
眼动追踪技术已广泛应用于模拟培训,用以探究不同经验层级护理人员的临床决策过程与视觉注意力模式。Shinnick[21]利用眼动追踪眼镜对比新入职护士与护理专家处理急性呼吸困难的心力衰竭病人模拟案例时的视觉注意分布情况,通过量化护士在关键区域的注视时间和注视频次发现,新入职护士在“经皮脉搏血氧饱和度值”和“医生指令”区域的总注视时间更长,注视频次更高,表明其需要更多时间搜寻信息且决策过程较慢,护理专家则能快速识别和理解关键信息,表现为更短的注视时间和更少的注视频次,提示其丰富的临床经验促使其能更迅速地做出判断。Buehler等[22]通过观测经验不足与经验丰富的重症监护室(ICU) 护士在Intellivent适应性支持通气(I-ASV)操作模拟时的注视模式,分析两组护士在5个关注区域的注视时间、注视频次及注视持续时间发现,经验丰富的护士在操作中对I-ASV特有参数有相对更长的注视时间和更多的注视频次,这种依赖传统参数的视觉行为模式表明,其在向闭环通气模式过渡过程中,视觉注意习惯尚未完全改变。Anton等[23]使用Tobii Glasses 2眼动追踪系统评估护士在脑卒中模拟案例中识别问题、区分信息及制定决策时的视觉注意模式,并结合临床判断量表和出声思考法记录其思维过程以评估决策质量,结果发现,随着模拟情境转变,护士的视觉焦点呈现明显变化,从初始评估阶段逐渐聚焦到病人的头部和口部区域,此趋势在脑卒中事件发生后的评估阶段尤为显著,且研究结果显示,在生命体征监视器和病人口部等区域的凝视时间与决策质量呈负相关。​
眼动追踪技术通过精确记录操作者在执行护理任务时的注视区域、持续时间和频率,揭示其在技能掌握过程中的关键差异[20]。该技术能够客观识别学习者是否有效关注任务核心环节或忽视重要细节,为教育者提供量化评估依据,从而精准定位技能短板并提供针对性反馈,强化临床实践中的技能学习效能[15]。Maekawa等[12]使用EMR-dFactory眼动分析软件分析经验丰富的护士与护生在进行静脉注射操作时的眼动差异,通过识别技能掌握的关键点和不足之处为技能教学进度提供量化方法。Sanchez等[14]采用ViewTracker 3眼动追踪设备采集并解析不同临床经验水平操作者在静脉输液模拟情境中的眼动轨迹数据,结果发现,高年资护士的视觉注意力常聚集于病人面部表情识别区、目标血管定位区及输液监控界面(如滴速调节装置)等关键区域,而护理学员则普遍表现出对上述关键区域的视觉关注不足。Sugimoto等[17]对老年失禁护理操作流程进行研究,采用眼动追踪技术量化记录护理教育者与学员对各预定兴趣区域的注视分布特征与时序动态,结合眼动扫描路径网络图分析,结果发现,低经验学员在操作准备期耗时较长,且多注视新垫子,缺乏对其他关键区域(如病人臀部)的关注,表现出较低的情境意识。
眼动追踪技术可以通过捕捉决策过程中的视觉焦点分布,对个体认知负荷和关注偏好进行量化分析。该技术可揭示学习者在高压环境中是否存在认知偏差(如过度关注非关键信号)或注意力分配不当问题,为教育者诊断决策缺陷提供客观证据,并通过数据驱动的反馈机制引导其优化决策路径[24]。Al-Moteri等[4]应用眼动追踪技术发现,新入职护士在低血容量性休克模拟场景中存在认知偏差,表现为对非关键信号的过度关注或未能聚焦核心问题,与决策评分下降相关。Anton等[23]研究发现,护士在急性脑卒中护理模拟中的决策质量与对关键诊断信息出现区域的视觉聚焦高度相关,同时该研究结合眼动数据与出声思维法清晰揭示了视觉焦点分布与临床判断质量间的内在联系。Shinnick[21]利用眼动追踪技术对比发现,新入职护士和护理专家处理急性呼吸困难案例时的视觉焦点分布模式存在差异,新入职护士对特定信息源会投入过长的注视时间、有过高的注视频次,表明其决策过程有迟滞,对指令存在依赖性,并常伴随较低判断评分,而护理专家则能高效定位和理解核心信息,注视时间短且频次低,可减少对提供者指令的依赖,体现了经验驱动的快速决策优势。
情境意识指个体在动态临床环境中对关键任务信息的感知、理解及响应能力[25]。视觉注意对于关键信息的感知至关重要,是保持有效态势感知的先决条件[26]。眼动追踪技术通过记录操作者对环境中核心要素的视觉捕获效率,客观评估其情境意识水平。该技术可识别学生在任务执行中是否及时响应环境变化、是否有效聚焦核心信息源,从而为实施针对性干预提供实证支持,提升学生在复杂情境下的临床应对能力[21]。Anbro等[27]通过VR头盔集成的眼动追踪技术评估医学生在紧急医疗模拟情境中的表现,结果发现,学生对关键讲者(如医生或护士)的凝视时长与其口头回应的准确性呈正相关,表明视觉注意聚焦对情境理解与临床沟通准确性具有一定作用。Sugimoto等[28]在新生儿护理模拟研究中,通过比较护理专家与新入职护士的眼动轨迹发现,经验丰富的护士凝视行为更具目的性与高效性,他们更倾向于关注新生儿面部和脉搏血氧仪等关键区域,能够快速整合信息并做出决策;而新入职护士的注视更为分散且缺乏有效整合,如在胸腹部区域频繁进行短暂注视,容易忽视关键线索,反映出其在情境意识方面的不足。
眼动追踪技术可以可视化地呈现操作过程中的注视分布与扫视路径,为学习者提供其认知行为的客观记录。该技术支持学生深度回溯任务执行时的视觉情况,识别关键区域的注意缺失或次要信息的过度消耗,从而促使其主动关联操作行为与临床结果,并针对性调整学习策略与操作模式[8]。Browning等[29]通过Tobii眼动追踪眼镜记录护理与急救医学学生在高保真临床模拟中的视觉焦点,结合模拟后视频与眼动数据回放进行反思性总结,结果发现,学生主要注视区域为病人头部(34%)与躯干(24%),回顾总结后的再次模拟演练,其临床表现评分表明眼动反馈对提升决策能力及自我认知具有有效性。Caloca-Amber等[18]利用Pupil Core眼动设备分析不同学年护生在心肺复苏模拟中对关键区域的视觉注意力,结果发现,低年级护生过度集中于胸部等操作区域,高年级护生则侧重监护设备且具有更佳的复苏得分,表明眼动追踪技术能无干扰解析视觉策略与临床判断力的内在关联,为优化复杂情境教学提供实证工具。
眼动追踪技术为护理教育提供了理解视觉注意力和行为模式的强大工具,但仍存在一定问题。1)眼动技术擅长捕捉注视点、眼跳等视觉注意外在表现,但难以直接测量内在思维、决策、记忆负荷或情绪状态,对非视觉行为(如听觉或触觉决策、手部操作、环境中的非视觉线索)以及思考情况缺乏观测能力[16]。在动态高压或复杂环境下,数据易丢失且存在捕捉盲区。其提供的信息维度有限,难以独立构建对认知和行为的全面理解。未来研究可整合行为测量、量表问卷及生理指标数据,构建更完整的多维模型。2)当前分析高度依赖人工定义关注区域,过程烦琐、耗时且易引入主观偏差[13]。眼动数据本质是相关性,难以推断特定注视行为与操作质量等的因果链。数据解读主观性强,不易精确区分识别、回忆或决策等不同认知任务下的具体活动[17]。未来可研发基于人工智能与机器学习的自动化工具,用于关注区域的自动识别、眼动模式分类、异常检测及深度模式挖掘。3)眼动行为受个体差异(如瞳孔反应特性、药物影响、心理压力、个性特征、操作熟练度)以及光照条件和干扰物等环境影响因素[18],这种复杂性使数据解读困难,眼动指标可能无法充分、准确反映操作技能或认知负荷。未来可优化校准和捕捉算法以提高数据质量、降低丢失率、提高效率,可通过在更贴近真实临床场景的模拟环境中测试或进行更多现场研究以增强结论适用性。
眼动追踪技术通过客观记录护理人员的视觉注意行为为理解其认知机制和优化护理教育提供了新视角。当前,我国护理教育中存在技能反馈精细化不足、决策思维可视化困难及情境意识评估不佳等问题,该技术可精准识别学员在操作、决策中的视觉注意偏差与认知盲区,其可提供技能训练的精准可视化反馈,揭示决策认知误区,量化评估情境意识,并赋能自我反思学习。但目前我国的相关研究尚处起步阶段,实证探索有限。未来建议优先开发针对性培训模块评估其可行性与教育影响,待通过不同教育环境的大规模纵向研究验证有效性后,再考虑纳入护理教育课程体系。

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2026年第40卷第14期
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doi: 10.12102/j.issn.1009-6493.2026.14.027
  • 接收时间:2025-07-18
  • 首发时间:2026-10-30
  • 出版时间:2026-07-25
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  • 收稿日期:2025-07-18
  • 修回日期:2026-04-21
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    1丽水学院医学院,浙江 323000

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郭虹,E-mail:
GUO Hong, E-mail:
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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