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Navigating learning disruptions: The role of digital learning platforms in student motivation, feedback and emotion 导航学习中断:数字学习平台在学生动机、反馈和情绪中的作用
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-06-01 Epub Date: 2025-12-12 DOI: 10.1016/j.compedu.2025.105534
Negar Monazam-Tabrizi , Yusuf Kurt , William il-Kuk Kang
Crises such as pandemics disrupt the motivational, feedback, and emotional processes that sustain effective learning. Digital learning platforms (DLPs) are often positioned as tools to maintain educational continuity, yet their associations with effective learning in disrupted contexts remains insufficiently understood. This study examines whether DLP use moderates the relationships between low motivation, insufficient feedback, negative emotions, and students' effective learning during crisis-driven remote education. Using a mixed-methods design, quantitative data from 216 UK university students were analysed using partial least squares structural equation modelling (PLS-SEM), and qualitative insights derived from open-ended questionnaire responses were analysed using thematic analysis. Findings indicate that diminished motivation, inadequate feedback, and negative emotions are negatively associated with learning effectiveness during crises. While DLP use was positively associated with effective learning, it did not significantly moderate the relationships between low motivation, insufficient feedback, negative emotions, and students’ effective learning. Qualitative insights suggest that more favourable learner experiences occur when technological affordances are aligned with instructional design, instructor presence and emotional support. The study points to the value of incorporating motivation-enhancing strategies, personalised feedback and affective support within digital learning environments. It contributes to digital pedagogy by highlighting that DLPs relate more strongly to effective learning when their features are integrated with coherent pedagogical design, meaningful social interaction and emotionally supportive practices. These insights extend beyond crisis contexts and offer guidance for designing resilient, inclusive, and human-centred digital learning systems.
流行病等危机破坏了维持有效学习的动机、反馈和情感过程。数字学习平台(dlp)通常被定位为维持教育连续性的工具,但它们与中断环境中有效学习的关系仍未得到充分理解。本研究旨在探讨在危机驱动的远程教育中,使用DLP是否能调节低动机、反馈不足、负面情绪与学生有效学习之间的关系。采用混合方法设计,使用偏最小二乘结构方程模型(PLS-SEM)分析了来自216名英国大学生的定量数据,并使用主题分析分析了来自开放式问卷回答的定性见解。研究结果表明,在危机期间,动机减弱、反馈不足和消极情绪与学习效率呈负相关。使用DLP与有效学习呈正相关,但对低动机、反馈不足、消极情绪与学生有效学习之间的关系没有显著调节作用。定性分析表明,当技术支持与教学设计、教师在场和情感支持相结合时,会出现更有利的学习者体验。该研究指出了在数字学习环境中结合激励增强策略、个性化反馈和情感支持的价值。它强调了当dlp的特点与连贯的教学设计、有意义的社会互动和情感支持实践相结合时,dlp与有效学习的关系更强,从而有助于数字教学法。这些见解超越了危机背景,为设计有弹性、包容和以人为本的数字学习系统提供了指导。
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引用次数: 0
A comparative study of expert, AI, and no external feedback on mathematics teacher learning outcomes in reflective practice 反思性实践中专家、人工智能和无外部反馈对数学教师学习成果的比较研究
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-06-01 Epub Date: 2026-01-13 DOI: 10.1016/j.compedu.2026.105572
Yimin Ning , Wee Tiong Seah , Jihe Chen , Jinhai Liu , Peifen Tan
Feedback is crucial for teacher professional development, yet most studies focus on a single source (e.g., experts or peers). Longitudinal comparisons across sources and analyses of underlying mechanisms are rare. This study therefore investigates how different feedback sources influence teacher learning outcomes. A total of 522 teachers participated in a 13-week professional learning program, including three weeks of reflective practice. Participants were divided into three groups: NF (no external feedback), EF (expert feedback), and AF (AI feedback). Teaching videos, instructional design artifacts, and reflective journals were analyzed to assess outcomes. Teachers in the EF group achieved significantly higher learning outcomes than those in the AF group, who, in turn, outperformed the NF group. These findings underscore the distinctive value of expert feedback in providing depth of insight and contextual sensitivity, while also indicating that AI feedback, although timely and adaptive, cannot fully replace expert judgment. Cross-Lagged Panel Network (CLPN) analysis identified key behaviors and sequences across groups and revealed detailed temporal patterns within behavioral clusters. Qualitative interviews further demonstrated that feedback effectiveness is shaped by the interaction of three dimensions—Time, Object, and Level (TOL)—which form the basis of a framework with 16 elements. The study highlights the complementary strengths of AI adaptability and expert insight, suggesting that multi-source feedback enhances teacher professional development by integrating process support with higher-order regulation.
反馈对教师的专业发展至关重要,但大多数研究都集中在单一来源(例如,专家或同行)。跨来源的纵向比较和潜在机制的分析是罕见的。因此,本研究探讨了不同的反馈来源对教师学习成果的影响。共有522名教师参加了为期13周的专业学习计划,其中包括三周的反思性实践。参与者被分为三组:NF(无外部反馈)、EF(专家反馈)和AF(人工智能反馈)。通过分析教学视频、教学设计工件和反思日志来评估结果。EF组的教师取得了显著高于AF组的学习成果,而AF组的教师又优于NF组。这些发现强调了专家反馈在提供深度洞察力和上下文敏感性方面的独特价值,同时也表明人工智能反馈虽然及时且具有适应性,但不能完全取代专家判断。交叉滞后面板网络(CLPN)分析确定了跨群体的关键行为和序列,并揭示了行为集群内的详细时间模式。定性访谈进一步表明,反馈的有效性是由三个维度——时间、对象和水平(TOL)——的相互作用形成的,这三个维度构成了一个包含16个要素的框架的基础。该研究强调了人工智能适应性和专家洞察力的互补优势,表明多源反馈通过将过程支持与高阶调节相结合来促进教师的专业发展。
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引用次数: 0
Longitudinal effects of learning analytics support for study planning and monitoring: Role of self-efficacy and data literacy 学习分析对学习计划和监控的纵向影响:自我效能感和数据素养的作用
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-06-01 Epub Date: 2025-12-11 DOI: 10.1016/j.compedu.2025.105532
Anceli Kaveri , Ismail Celik , Egle Gedrimiene , Anni Silvola , Hanni Muukkonen
Learning analytics (LA) can be an effective support for self-regulation of higher education students. Supporting different students on their academic paths requires considering students' self-efficacy beliefs and data-literacy skills as well as their varying uses and interpretations of LA. In this quasi-experimental study, we collected quantitative survey data on higher education students (N = 105) with and without access to a student-facing LA dashboard designed to support students on an academic path level. We collected data on students' perceived support for study planning and monitoring at three timepoints during one academic year. Utilizing latent growth curve modeling, the study investigated how students with different self-efficacy beliefs and data-literacy skills perceive receiving self-regulation support from LA throughout an academic year compared to peers with only regular digital tools. Our results showed that students with access to the LA dashboard reported a larger increase in perceived support compared to peers with access to only regular digital tools. Students with high data-literacy skills perceived receiving more support from LA compared to peers with low data-literacy. Students' self-efficacy beliefs did not significantly impact the extent of perceived support from LA. The results highlight the ethical risk of complex digital solutions, which, over time, put less technology-savvy students at a greater disadvantage and mainly benefit already higher-skilled students. The results strengthen current understanding on how LA supports higher education students on their academic paths but call for further investigation into ways of fostering students' data-literacy skills to maximize the support and overcome equity concerns.
学习分析可以为高等教育学生的自我调节提供有效的支持。支持不同学生的学术道路需要考虑学生的自我效能信念和数据素养技能,以及他们对洛杉矶的不同使用和解释。在这项准实验研究中,我们收集了高等教育学生(N = 105)的定量调查数据,这些学生有和没有使用面向学生的LA仪表板,该仪表板旨在支持学生的学术路径水平。我们在一学年的三个时间点收集了学生对学习计划和监控的感知支持的数据。利用潜在增长曲线模型,该研究调查了与仅使用常规数字工具的同龄人相比,具有不同自我效能信念和数据素养技能的学生如何看待整个学年从洛杉矶获得的自我调节支持。我们的研究结果表明,与仅使用常规数字工具的同龄人相比,使用洛杉矶仪表板的学生报告了更大的感知支持增长。与数据素养低的学生相比,数据素养高的学生认为从洛杉矶获得了更多的支持。学生的自我效能感信念并没有显著影响来自LA的感知支持程度。研究结果凸显了复杂的数字解决方案的伦理风险,随着时间的推移,对技术不太了解的学生处于更大的劣势,而主要受益的是已经具备较高技能的学生。研究结果加强了目前对洛杉矶如何支持高等教育学生走上学术道路的理解,但呼吁进一步研究培养学生数据素养技能的方法,以最大限度地提供支持并克服公平问题。
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引用次数: 0
Tracing scientific reasoning as process: A trait-behavior-performance model with learning analytics in simulated environments 追踪科学推理过程:模拟环境中学习分析的特征-行为-绩效模型
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-06-01 Epub Date: 2026-01-12 DOI: 10.1016/j.compedu.2025.105556
Chia-Mei Lu
Scientific reasoning in simulation-based learning environments (SBLEs) is a time-structured process, not a terminal outcome. We advance and test a trait → mechanism → behavior model explaining how self-regulated learning (SRL) and critical thinking disposition (CTD) become consequential via a translation layer: dispositions first shape a reasoning-aligned configuration mechanism (SIM; planned contrasts, disciplined retesting), which enables process-level behavior (SIB; semantic precision, revision cadence). Eleventh graders (N = 168) completed a closed-loop aquatic simulation under Guided→Open or Open→Open sequences. Consistent Partial Least Squares for Reflective Constructs(PLSc)estimated reflective blocks; SIB was formative (Mode B). Measurement quality and cross-condition invariance were established; Cluster-Robust Variance Estimator, Type 2 (CR2), and PLSpredict supported stability and utility. Process analytics (K-means profiles; three-state HMM) complemented the SEM. Findings: SRL and CTD had moderate positive effects on SIM; SIM had a vast, robust effect on SIB. Mediation showed trait effects reach behavior primarily through SIM. Format moderation was small/uncertain; temporally, lower-SRL learners dwelled longer in low-efficiency states, and editing cadence marked transitions to efficiency; early scaffolds modestly shortened dwell. Design principles: instrument platforms to monitor SIM/SIB as live control points; route support by profiles; and time-minimal, load-aware prompts to the revision window to restore Control of Variables Strategy (CVS) discipline and semantic alignment. The contribution is a validated, mechanism-aware account that yields diagnostic, feedback-ready, and scalable specifications for precision scaffolding and evaluation in SBLEs.
基于模拟的学习环境(SBLEs)中的科学推理是一个时间结构的过程,而不是最终的结果。我们提出并测试了一个特质→机制→行为模型,该模型解释了自我调节学习(SRL)和批判性思维倾向(CTD)如何通过翻译层变得重要:倾向首先塑造了一个与推理一致的配置机制(SIM;有计划的对比,有纪律的重新测试),从而实现了过程级行为(SIB;语义精度,复习节奏)。11年级学生(N = 168)在Guided→Open或Open→Open顺序下完成闭环水上模拟。反射构造估计反射块的一致偏最小二乘法SIB是形成性的(模式B)。建立了测量质量和交叉条件不变性;聚类-鲁棒方差估计器,类型2(CR2)和plpredict支持稳定性和实用性。过程分析(k -均值曲线;三态HMM)补充了扫描电镜。结果:SRL和CTD对SIM有中等正向影响;SIM对SIB有巨大而强大的影响。中介结果表明,特质效应主要是通过SIM达到行为的。格式适度性小/不确定;在时间上,低语速学习者在低效率状态下停留的时间更长,编辑节奏标志着向高效率的过渡;早期支架适度缩短了停留时间。设计原则:仪器平台监控SIM/SIB作为现场控制点;配置文件支持路由;以及时间最小、负载敏感的修订窗口提示,以恢复变量控制策略(CVS)规则和语义对齐。其贡献是一个经过验证的、机制感知的帐户,该帐户为sble中的精确搭建和评估生成了诊断、反馈就绪和可扩展的规范。
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引用次数: 0
The double-edged sword of technology: Investigating technostress and techno-eustress in academic burnout through digital literacy, internet self-efficacy, and cognitive flexibility 技术的双刃剑:通过数字素养、网络自我效能和认知灵活性研究技术压力和技术压力对学业倦怠的影响
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-05-01 Epub Date: 2025-12-19 DOI: 10.1016/j.compedu.2025.105540
Mehmet Avcı
The swift advancement of technology in educational contexts introduces a range of challenges alongside significant benefits. Technostress and techno-eustress have been identified as significant determinants of academic achievement in higher education, influencing students’ well-being, productivity, and levels of burnout. However, there is limited research on how psychological mechanisms play a mediating role in the relationship between technostress, techno-eustress, and academic burnout. To fill this gap, two serial mediation models with three mediators were proposed to explore the associations among technostress, techno-eustress, digital literacy, internet self-efficacy, cognitive flexibility, and academic burnout in a random sample of university students from different education faculties in Türkiye (N = 677). The total effect of technostress on academic burnout was significant. This first model, with an additional three mediators, accounted for 16 % of the explained variance in academic burnout, and then 5 % without mediators. In the second model, the total effect of techno-eustress on academic burnout was nonsignificant (p = 0.602), while the direct effect had a small impact (p = 0.029). However, the total indirect effect of techno-eustress on academic burnout was significant and serially mediated by digital literacy, internet self-efficacy, and cognitive flexibility, accounting for 13 % of the explained variance. Triple serial mediation analyses indicated that digital literacy enhances internet self-efficacy, which in turn improves cognitive flexibility. This sequence ultimately promotes techno-eustress and mitigates technostress, resulting in a reduction of academic burnout. Focusing on these mediators as protective resources against technostress may enhance psychological and behavioral outcomes in higher education students.
技术在教育领域的迅速发展带来了一系列的挑战,同时也带来了巨大的好处。技术压力和技术压力已被确定为高等教育中学业成就的重要决定因素,影响学生的幸福感、生产力和倦怠水平。然而,关于心理机制如何在技术压力、技术-良性压力和学业倦怠之间发挥中介作用的研究却很少。为了填补这一空白,我们提出了两个具有三个中介的序列中介模型来探索技术压力、技术-良好压力、数字素养、互联网自我效能感、认知灵活性和学业倦怠之间的关系,并随机抽取了来自 kiye不同教育学院的大学生(N = 677)。技术压力对学业倦怠的总影响显著。第一个模型,加上三个额外的中介,在学业倦怠的解释方差中占16%,没有中介的解释方差占5%。在第二个模型中,技术压力对学业倦怠的总影响不显著(p = 0.602),而直接影响较小(p = 0.029)。然而,技术压力对学业倦怠的总间接影响显著,并通过数字素养、网络自我效能和认知灵活性依次介导,占解释方差的13%。三序列中介分析表明,数字素养提高网络自我效能感,网络自我效能感进而提高认知灵活性。这个顺序最终促进了技术压力,减轻了技术压力,从而减少了学业倦怠。关注这些中介作为对技术压力的保护资源可能会提高高等教育学生的心理和行为结果。
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引用次数: 0
Mitigating cognitive biases in higher education contexts through a brief video-based intervention: A randomized controlled trial 通过简短的基于视频的干预减轻高等教育背景下的认知偏见:一项随机对照试验
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-05-01 Epub Date: 2025-12-19 DOI: 10.1016/j.compedu.2025.105535
Lemi Baruh , Zeynep Cemalcilar , Andy Stoycheff
Cognitive biases present significant challenges in higher education, yet existing mitigation strategies are often resource-intensive or lack scalability. This study introduces and evaluates a novel, scalable intervention using brief, scenario-based videos to reduce cognitive biases that may influence self-perceptions, relationships, and decision-making in higher education contexts. We conducted a cross-national experiment (N = 1038) with teachers and learners, targeting 21 distinct biases. Participants were randomly assigned to a control or experimental condition. Both groups viewed a video depicting a bias-eliciting scenario; the experimental group was then shown an additional "debiasing" segment containing supplemental, context-altering information. Results showed the debiasing videos significantly reduced biased decisions. This effect was substantially stronger for teachers (Cohen's d = 0.51) than for learners (d = 0.11). For students, the intervention was effective only when participants viewed the videos with subtitles in their native language. Teachers, but not learners, also demonstrated a "transfer" effect, showing reduced bias in subsequent, unrelated tasks, after receiving the debiasing intervention. These findings indicate that brief, narrative-based micro-interventions are a promising and scalable tool for cognitive bias mitigation. However, their effectiveness is moderated by audience characteristics, like role as a teacher or a student, and the language of delivery, underscoring the need for context-sensitive design and implementation.
认知偏差是高等教育面临的重大挑战,但现有的缓解策略往往是资源密集型的,或者缺乏可扩展性。本研究介绍并评估了一种新颖的、可扩展的干预措施,使用简短的、基于场景的视频来减少可能影响高等教育背景下自我感知、关系和决策的认知偏见。我们对教师和学习者进行了一项跨国实验(N = 1038),针对21种不同的偏见。参与者被随机分配到对照或实验状态。两组人都观看了一段视频,视频描绘了一个引发偏见的场景;然后向实验组展示另一个“去偏见”片段,其中包含补充的、改变上下文的信息。结果显示,消除偏见的视频显著减少了有偏见的决定。这种效应在教师(Cohen’s d = 0.51)中明显强于学习者(d = 0.11)。对于学生来说,只有当参与者观看带有母语字幕的视频时,干预措施才有效。教师,而不是学习者,也表现出“转移”效应,在接受去偏见干预后,在随后的不相关任务中表现出减少的偏见。这些发现表明,简短的、基于叙述的微干预是一种有前途的、可扩展的认知偏见缓解工具。然而,它们的有效性受到受众特征的影响,比如作为教师或学生的角色,以及交付的语言,强调了对上下文敏感的设计和实施的需要。
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引用次数: 0
Differential effects of student and parental mobile phone use on academic procrastination trajectories: Machine learning evidence 学生和家长使用手机对学业拖延轨迹的差异影响:机器学习证据
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-05-01 Epub Date: 2025-12-24 DOI: 10.1016/j.compedu.2025.105551
Jiabin Liu , Ru-De Liu , Wei Hong , Jingmin Lin
Academic procrastination is a prevalent and varies considerably among adolescents. However, little is known about the specific patterns of academic procrastination development and how the family's digital environment influences these developmental patterns. We conducted a three-wave longitudinal survey with 1130 Chinese adolescents (47.10 % males, Mage = 13.59 years, SD = 2.14 at T1) to identify distinct procrastination trajectories and examine how student and parental mobile phone use (MPU) predicts trajectories. The latent class growth analysis revealed four distinct trajectories: high-stable (47.40 %), low-increasing (14.10 %), moderate-stable (22.20 %), and low-stable groups (16.30 %). Machine learning analysis demonstrated that students' mobile phone dependency and escape motivation predicted membership in less adaptive trajectories (i.e., high-stable, moderate-stable, and low-increasing groups). For the specific purposes of MPU, using for seeking life info, online courses learning, and playing games predicted membership in the low-stable group; while chatting with net friends predicted membership in the low-increasing group. Notably, parental phubbing also predicted membership in these less adaptive groups, whereas active parental mediation predicted membership in the low-stable group. These findings provide the first empirical evidence for the heterogeneous development of academic procrastination and highlight the important role of the family digital ecosystem in shaping these trajectories. Practically, the study supports the development of targeted and family-based interventions tailored to specific procrastination patterns.
学习拖延症在青少年中很普遍,而且差异很大。然而,关于学习拖延症发展的具体模式以及家庭数字环境如何影响这些发展模式,人们知之甚少。我们对1130名中国青少年(男性47.10%,年龄13.59岁,SD = 2.14)进行了三波纵向调查,以确定不同的拖延轨迹,并研究学生和父母的手机使用(MPU)如何预测轨迹。潜在类别增长分析显示了四个不同的轨迹:高稳定(47.40%)、低增长(14.10%)、中稳定(22.20%)和低稳定(16.30%)。机器学习分析表明,学生的手机依赖和逃避动机预测了适应性较差的轨迹(即高稳定、中等稳定和低增长群体)的成员资格。对于MPU的特定目的,用于寻找生活信息、在线课程学习和玩游戏预测了低稳定组的成员资格;而与网友聊天时预测会员在低增长群体。值得注意的是,父母低头也预示着这些适应性较差的群体的成员资格,而积极的父母调解则预示着低稳定群体的成员资格。这些发现为学习拖延症的异质性发展提供了第一个经验证据,并强调了家庭数字生态系统在塑造这些轨迹方面的重要作用。实际上,这项研究支持针对特定的拖延症模式制定针对性的、以家庭为基础的干预措施。
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引用次数: 0
The indirect role of children’s screen time and the moderating role of problematic parental screen use on the relationships between different parental mediation strategies and preschoolers’ developmental outcomes 儿童屏幕时间的间接作用和问题父母屏幕使用对不同父母中介策略与学龄前儿童发展结果的调节作用
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-05-01 Epub Date: 2025-12-27 DOI: 10.1016/j.compedu.2025.105552
Siyu Wu , Xiaodan Yu , Wei Wei
Parents can mitigate screens’ negative effects on school-aged children and adolescents by monitoring their screen use and improving their screen use skills. However, many preschoolers spend more time on screens than recommended. It remains unclear whether parental mediation is associated with preschoolers’ development through children’s screen time, and whether the first part of this pathway is moderated by problematic parental screen use. This analysis utilized parent-reported data collected in June 2019 about 57,827 children aged 4–5 years. Data included children’s developmental outcomes, children’s screen time, parental mediation (restrictive mediation, instructive mediation, and co-use) frequency, problematic parental screen use level, family income, and parental education. A significant negative correlation was found between children’s screen time and developmental outcomes (r = −0.07, 95 % confidence interval (CI) = [−0.08, −0.06]). Children’s screen time mediated the relations between parental mediation strategies and developmental outcomes. Restrictive mediation frequency was positively associated with developmental outcomes through children’s screen time (β = 0.016, 95 % CI = [0.013, 0.018]). Instructive mediation (β = −0.005, 95 % CI = [−0.006, −0.005]) and co-use (β = −0.004, 95 % CI = [−0.005, −0.003]) were indirectly, negatively associated with developmental outcomes through children’s screen time. Problematic parental screen use moderated the relations between parental mediation and children’s screen time. Higher problematic parental screen use strengthened restrictive mediation’s negative (β = −0.023, 95 % CI = [−0.032, −0.011]) and instructive mediation’s positive (β = 0.047, 95 % CI = [0.037, 0.057]) effects. Despite the modest effect sizes, the statistically robust results suggest that population-level adoption of combined parental strategies—reducing problematic parental screen use alongside implementing restrictive mediation—could translate into public health benefits for early childhood development.
家长可以通过监测学龄儿童和青少年的屏幕使用情况和提高他们的屏幕使用技能来减轻屏幕对他们的负面影响。然而,许多学龄前儿童花在屏幕上的时间超过了建议的时间。目前尚不清楚父母的调解是否通过儿童的屏幕时间与学龄前儿童的发展有关,以及这一途径的第一部分是否被有问题的父母屏幕使用所缓和。该分析利用了2019年6月收集的约57,827名4-5岁儿童的家长报告数据。数据包括儿童发育结果、儿童屏幕时间、父母干预(限制性干预、指导性干预和共同使用)频率、父母有问题的屏幕使用水平、家庭收入和父母受教育程度。儿童屏幕时间与发育结果呈显著负相关(r = - 0.07, 95%可信区间(CI) =[- 0.08, - 0.06])。儿童屏幕时间在父母调解策略与发展结果之间起中介作用。限制性中介频率通过儿童屏幕时间与发育结果呈正相关(β = 0.016, 95% CI =[0.013, 0.018])。指导性中介(β = - 0.005, 95% CI =[- 0.006, - 0.005])和共同使用(β = - 0.004, 95% CI =[- 0.005, - 0.003])与儿童屏幕时间的发展结果呈间接负相关。有问题的父母屏幕使用调节了父母调解与儿童屏幕时间之间的关系。较高的问题父母筛选率强化了限制性中介的负作用(β = - 0.023, 95% CI =[- 0.032, - 0.011])和指导性中介的正作用(β = 0.047, 95% CI =[0.037, 0.057])。尽管效果不大,但统计结果表明,在人口水平上采用联合父母策略——减少有问题的父母屏幕使用,同时实施限制性调解——可以转化为儿童早期发展的公共卫生效益。
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引用次数: 0
Assessing mindset states of Hong Kong secondary students using machine learning in real-world online learning environment 评估香港中学生在真实网上学习环境下使用机器学习的心态
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-05-01 Epub Date: 2025-12-26 DOI: 10.1016/j.compedu.2025.105554
Elly Cheng Wang, Xin Guan, Xiaolong Chen, Tai Kai Ng
This study investigates the use of machine learning to detect mindset states — concentration, motivation, perseverance, engagement, and self-initiative among secondary school students in Hong Kong during two real-life online courses. Prior research has explored AI for mindset detection in controlled settings. Addressing real-life challenges such as low data quality, lighting variability, movement, and privacy concerns, this study explores the feasibility of detecting mindset states in a real-life environment with a combination of inputs, including quiz scores, facial expression, and categorized learning behavior logs. Using a Recurrent Neural Network (RNN), we achieved a modest yet significant prediction accuracy, even with a small dataset of approximately one hundred students. In particular, our results demonstrate the potential of logging data as a scalable and privacy-preserving approach for understanding students’ psychological states. We caution that while our results are encouraging, the modest accuracy highlights the need for further optimization before the approach can be reliably applied in real-world educational settings.
From a pedagogical perspective, our findings suggest that real-time feedback from well-trained AI tool may provide useful information about students’ mindset states which educators can use to create better student-centered adaptive teaching practices that promote personalized learning while addressing ethical considerations such as data privacy and accessibility.
本研究调查了香港中学生在两个现实生活中的在线课程中使用机器学习来检测心态状态-集中,动力,毅力,参与和自我主动性。之前的研究已经探索了人工智能在受控环境下的心态检测。为了解决现实生活中的挑战,如低数据质量、光照可变性、运动和隐私问题,本研究探索了在现实生活环境中通过组合输入(包括测验分数、面部表情和分类学习行为日志)检测心态状态的可行性。使用递归神经网络(RNN),即使使用大约100名学生的小数据集,我们也实现了适度但显著的预测准确性。特别是,我们的研究结果证明了记录数据作为一种可扩展和隐私保护的方法来理解学生的心理状态的潜力。我们提醒说,虽然我们的结果令人鼓舞,但适度的准确性表明,在该方法能够可靠地应用于现实世界的教育环境之前,还需要进一步优化。从教学的角度来看,我们的研究结果表明,训练有素的人工智能工具的实时反馈可以提供有关学生心态状态的有用信息,教育工作者可以利用这些信息来创建更好的以学生为中心的适应性教学实践,促进个性化学习,同时解决数据隐私和可访问性等道德问题。
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引用次数: 0
The effects of GAI-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students’ conceptual understanding and cognitive engagement patterns GAI-enhanced teaching agents in meta - verse (GPAiM)对小学生概念理解和认知投入模式的影响
IF 10.5 1区 教育学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Pub Date : 2026-05-01 Epub Date: 2025-12-30 DOI: 10.1016/j.compedu.2025.105555
Tinghui Wu , Xuesong Zhai , Yanjie Song
This study examines the effects of generative artificial intelligence (GAI)-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students' conceptual understanding of traditional festival customs and on the students' cognitive engagement patterns. The participants included 116 students from three intact classes. These classes were randomly assigned to two experimental groups (with 2D-GPAiM and 3D-GPAiM, respectively) and one control group (without GPAiM but with a real-person teacher). All the participants learned in the metaverse, and students in different groups were allowed to interact with 2D-GPAiM, 3D-GPAiM, and the real-person teacher during their learning, respectively. This study was conducted under a three-week AI literacy project with the learning topic of traditional festival customs. The results showed that the experimental groups (both 2D-GPAiM and 3D-GPAiM) had a positive impact on the students' conceptual understanding of traditional festivals, while the control group did not. More importantly, the 2D-GPAiM group showed a significantly positive difference in the participants’ conceptual understanding compared with the control group. In addition, regarding cognitive engagement, the 2D-GPAiM group showed a highly interactive, low-fluctuating, and high-level cognitive engagement pattern; The 3D-GPAiM group demonstrated a highly interactive, highly fluctuating, medium-level cognitive engagement pattern, while the control group exhibited a low-interactive, low-fluctuating, low-level cognitive engagement pattern. These findings provide valuable insights into future GAI-assisted pedagogical designs.
本研究探讨了生成式人工智能(GAI)增强的元世界教学代理(GPAiM)对小学生传统节日习俗概念理解和学生认知投入模式的影响。参与者包括来自三个完整班级的116名学生。这些班级被随机分为两个实验组(分别使用2D-GPAiM和3D-GPAiM)和一个对照组(不使用GPAiM但有真人教师)。所有参与者都在虚拟世界中学习,不同组的学生在学习过程中分别与2D-GPAiM、3D-GPAiM和真人老师进行互动。本研究是在一个为期三周的人工智能扫盲项目下进行的,学习主题是传统节日习俗。结果表明,实验组(2D-GPAiM和3D-GPAiM)对学生对传统节日的概念理解有积极的影响,而对照组则没有。更重要的是,与对照组相比,2D-GPAiM组在参与者的概念理解上有显著的正差异。此外,在认知投入方面,2D-GPAiM组表现出高度互动、低波动、高水平的认知投入模式;3D-GPAiM组表现为高互动、高波动、中等水平的认知投入模式,而对照组表现为低互动、低波动、低水平的认知投入模式。这些发现为未来的人工智能辅助教学设计提供了有价值的见解。
{"title":"The effects of GAI-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students’ conceptual understanding and cognitive engagement patterns","authors":"Tinghui Wu ,&nbsp;Xuesong Zhai ,&nbsp;Yanjie Song","doi":"10.1016/j.compedu.2025.105555","DOIUrl":"10.1016/j.compedu.2025.105555","url":null,"abstract":"<div><div>This study examines the effects of generative artificial intelligence (GAI)-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students' conceptual understanding of traditional festival customs and on the students' cognitive engagement patterns. The participants included 116 students from three intact classes. These classes were randomly assigned to two experimental groups (with 2D-GPAiM and 3D-GPAiM, respectively) and one control group (without GPAiM but with a real-person teacher). All the participants learned in the metaverse, and students in different groups were allowed to interact with 2D-GPAiM, 3D-GPAiM, and the real-person teacher during their learning, respectively. This study was conducted under a three-week AI literacy project with the learning topic of traditional festival customs. The results showed that the experimental groups (both 2D-GPAiM and 3D-GPAiM) had a positive impact on the students' conceptual understanding of traditional festivals, while the control group did not. More importantly, the 2D-GPAiM group showed a significantly positive difference in the participants’ conceptual understanding compared with the control group. In addition, regarding cognitive engagement, the 2D-GPAiM group showed a <em>highly interactive, low-fluctuating, and high-level cognitive engagement</em> pattern; The 3D-GPAiM group demonstrated a <em>highly interactive, highly fluctuating, medium-level cognitive engagement</em> pattern, while the control group exhibited a <em>low-interactive, low-fluctuating, low-level cognitive engagement pattern</em>. These findings provide valuable insights into future GAI-assisted pedagogical designs.</div></div>","PeriodicalId":10568,"journal":{"name":"Computers & Education","volume":"245 ","pages":"Article 105555"},"PeriodicalIF":10.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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Computers & Education
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