Science Talks最新文献

LGBTQIA+ inclusive health training in nursing education: An in-depth analysis of the current landscape 护理教育中的LGBTQIA+包容性健康培训：现状的深入分析

Science Talks

Pub Date : 2025-10-25 DOI: 10.1016/j.sctalk.2025.100499

Ani Grace Kalaimathi , Russell Franco D'Souza , Mary Mathew , Krishna Mohan Surapaneni

LGBTQIA+ individuals continue to face significant health disparities globally, driven by stigma, discrimination, and culturally insensitive healthcare practices. In India, despite progress such as the decriminalization of same-sex relationships in 2018, LGBTQIA+ individuals often encounter marginalization and exclusion within healthcare systems. Nurses, as primary healthcare providers, play a pivotal role in addressing these disparities; however, nursing education frequently lacks adequate training to prepare students for the unique needs of LGBTQIA+ patients. This study aimed to assess the perceptions, confidence, and institutional training of nursing students in India regarding LGBTQIA+-inclusive care. A quantitative, descriptive survey design was employed, involving 90 nursing students who completed a 16 item structured and validated questionnaire. Results revealed that while students exhibited positive perceptions toward LGBTQIA+ individuals, their confidence in performing critical tasks, such as sexual history-taking and physical examinations, was low. Institutional training was also found to be insufficient, indicating gaps in the current curriculum. The findings highlight the urgent need for comprehensive and practical LGBTQIA+-specific training in nursing education to enhance cultural competence and confidence among future nurses. Addressing these gaps is crucial to reducing health disparities and ensuring equitable, inclusive, and compassionate care for LGBTQIA+ individuals.

由于污名化、歧视和对文化不敏感的医疗保健做法，LGBTQIA+个体在全球范围内继续面临巨大的健康差距。在印度，尽管在2018年同性关系合法化等方面取得了进展，但LGBTQIA+个人经常在医疗保健系统中遭到边缘化和排斥。护士作为初级卫生保健提供者，在解决这些差异方面发挥着关键作用；然而，护理教育往往缺乏足够的培训，使学生为LGBTQIA+患者的独特需求做好准备。本研究旨在评估印度护生对LGBTQIA+包容性护理的认知、信心和制度培训。采用定量、描述性调查设计，对90名护理专业学生进行了16项结构化和有效的问卷调查。结果显示，虽然学生们对LGBTQIA+个体表现出积极的看法，但他们在完成关键任务（如性史记录和体检）时的信心却很低。机构培训也不足，这表明目前的课程存在差距。研究结果表明，迫切需要在护理教育中开展全面实用的LGBTQIA+培训，以增强未来护士的文化能力和信心。解决这些差距对于减少健康差距和确保对LGBTQIA+个体的公平、包容和富有同情心的护理至关重要。

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引用次数: 0

Healing bones at the nanoscale: Black phosphorus dots for bone tissue repair, addressing long-term effects of chemotherapy 纳米级骨愈合：用于骨组织修复的黑磷点，解决化疗的长期影响

Science Talks

Pub Date : 2025-10-23 DOI: 10.1016/j.sctalk.2025.100498

Luz Izquierdo Juliá , Cristina Martín , Giacomo Reina , Amalia Ruiz

Chemotherapy-induced bone loss is a persistent clinical challenge, often leading to long-term skeletal complications. In this work, we developed black phosphorus (BP) dots as a nanoscale therapeutic platform to promote bone tissue repair. The BP dots were synthesized via liquid-phase exfoliation and subsequently functionalized with poly-l-lysine to enhance their colloidal stability and cell uptake. Structural and physicochemical characterization was performed using transmission electron microscopy (TEM), Raman spectroscopy, and zeta potential analysis, confirming their nanoscale morphology, crystalline integrity, and surface charge modification. We demonstrate that BP dots possess intrinsic biocompatibility for osteoblast cells. Additionally, BP dots promote biomineralisation by increasing local calcium phosphate concentrations, thereby accelerating hydroxyapatite formation and bone matrix deposition. Our findings highlight the functionality of BP dots as biocompatiblemineralization support, as a promising strategy to counteract chemotherapy-associated bone degeneration and restore bone homeostasis.

化疗引起的骨质流失是一个持续的临床挑战，经常导致长期的骨骼并发症。在这项工作中，我们开发了黑磷（BP）点作为纳米级治疗平台来促进骨组织修复。BP点通过液相剥离合成，随后用聚赖氨酸功能化，以提高其胶体稳定性和细胞摄取。利用透射电子显微镜（TEM）、拉曼光谱和zeta电位分析进行了结构和物理化学表征，确认了它们的纳米级形貌、晶体完整性和表面电荷修饰。我们证明BP点对成骨细胞具有内在的生物相容性。此外，BP点通过增加局部磷酸钙浓度促进生物矿化，从而加速羟基磷灰石形成和骨基质沉积。我们的研究结果强调了BP点作为生物相容性矿化支持的功能，作为一种有希望的策略来对抗化疗相关的骨变性和恢复骨稳态。

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引用次数: 0

Novel material innovations for the efficient extraction and separation of critical elements 新材料的创新，有效地提取和分离关键元素

Science Talks

Pub Date : 2025-10-16 DOI: 10.1016/j.sctalk.2025.100497

Iryna Protsak , Freddy Kleitz , Thilo Hofmann

Critical materials (CMs) are essential for the modern economy but face challenges due to current or potential supply shortages. Notable examples include scandium (Sc), rare earth elements (REEs), indium (In), and gallium (Ga). Recovering REEs often requires their separation from Thorium (Th) and Uranium (U), which are commonly found together in various feedstocks. Given the value of these elements, as well as the critical role of other metals like Sc, In, and Ga in technology, the development of advanced materials and innovative strategies for their sustainable recovery is crucial. Solid-phase extraction (SPE) represents a versatile approach within experimental extraction technologies. Silica-based materials, with tunable surface properties and good stability, excel as sorbents for these applications. This presentation highlights ligand-functionalized SBA-15, which demonstrated remarkable selectivity for Th at pH 2, even with competing elements (REEs, Al, Fe), and efficiently extracted Sc and U at pH 4, showcasing its dual functionality. It also efficiently recovered In from model solutions simulating semiconductor materials and separated In from Sn in solutions mimicking ITO (indium tin oxide) films compositions. Additionally, bare SBA-15 exhibited strong selectivity for Ga in model solutions replicating semiconductor substrates and effectively separated Ga from Al, showcasing its versatility and efficiency.

关键材料（CMs）对现代经济至关重要，但由于当前或潜在的供应短缺而面临挑战。值得注意的例子包括钪（Sc）、稀土元素（ree）、铟（In）和镓（Ga）。回收稀土通常需要将它们从钍（Th）和铀(U)中分离出来，这两种元素通常存在于各种原料中。考虑到这些元素的价值，以及Sc、In和Ga等其他金属在技术中的关键作用，开发先进材料和创新策略以实现其可持续回收至关重要。固相萃取（SPE）代表了实验萃取技术中的一种通用方法。硅基材料具有可调的表面性能和良好的稳定性，是这些应用的优秀吸附剂。这篇论文强调了配体功能化的SBA-15，它对pH为2的Th具有显著的选择性，即使是与竞争元素（稀土、铝、铁），并在pH为4的情况下有效地提取Sc和U，展示了它的双重功能。它还能有效地从模拟半导体材料的模型溶液中回收铟，并在模拟ITO（氧化铟锡）薄膜成分的溶液中分离铟和锡。此外，裸SBA-15在复制半导体衬底的模型溶液中对Ga表现出很强的选择性，并有效地将Ga从Al中分离出来，显示了其通用性和效率。

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引用次数: 0

The effect of infill type ratio on strength of PLA and ABS thermoplastics 填充型比对PLA和ABS热塑性塑料强度的影响

Science Talks

Pub Date : 2025-10-13 DOI: 10.1016/j.sctalk.2025.100496

K.V. Vara Lakshmi, B. Jayanth Venkat Surya, K. Shiva Narasimha

This study investigates the effect of honeycomb infill density on the tensile strength of 3D-printed specimens fabricated using Fused Deposition Modeling (FDM) technology with PLA (Polylactic Acid) and ABS (Acrylonitrile Butadiene Styrene) thermoplastics. The motivation for this work stems from the increasing demand for lightweight polymer components in engineering applications, where reliable tensile performance is critical for structural safety. Although honeycomb structures are traditionally preferred for compression-dominated applications such as heels, heat exchangers, and lightweight cores, they are also increasingly employed in tensile load-bearing components such as lightweight brackets, connecting links, prosthetic parts, and aerospace/automotive fixtures. Understanding their tensile properties is therefore essential for effective design. Specimens with infill densities ranging from 15 % to 100 % were modeled in CATIA V5 and analyzed in ANSYS R21 using a honeycomb (hexagonal) pattern. Tensile strength was evaluated for each density and compared against ASTM E8 tensile properties of plastics. Results show that PLA specimens achieved a maximum tensile strength of 60.02 MPa at 100 % infill, while ABS specimens reached 52.54 MPa. Strength improvement from 15 % to 100 % infill was approximately 60–70 %. PLA exhibited superior stiffness and strength, making it suitable for tensile applications, whereas ABS showed higher strain and deformability. The findings confirm that infill density is a key parameter influencing the tensile performance of thermoplastic parts produced by FDM.

本研究研究了蜂窝填充密度对使用熔融沉积建模（FDM）技术用PLA（聚乳酸）和ABS（丙烯腈-丁二烯-苯乙烯）热塑性塑料制作的3d打印样品的拉伸强度的影响。这项工作的动机源于工程应用中对轻质聚合物组件日益增长的需求，在这些应用中，可靠的拉伸性能对结构安全至关重要。虽然蜂窝结构传统上更适合于压缩为主的应用，如鞋跟、热交换器和轻质核心，但它们也越来越多地用于拉伸承重部件，如轻质支架、连接链接、假肢部件和航空航天/汽车夹具。因此，了解它们的拉伸性能对于有效设计至关重要。在CATIA V5中对填充密度为15%至100%的试件进行建模，并在ANSYS R21中采用蜂窝（六角形）模式进行分析。对每种密度的拉伸强度进行评估，并与ASTM E8塑料的拉伸性能进行比较。结果表明，在100%填充时，PLA试样的最大抗拉强度为60.02 MPa，而ABS试样的最大抗拉强度为52.54 MPa。充填强度从15%提高到100%，约为60 - 70%。PLA具有优异的刚度和强度，适合拉伸应用，而ABS具有更高的应变性和变形性。结果表明，填充密度是影响FDM热塑性件拉伸性能的关键参数。

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引用次数: 0

Trustworthy AI knowledge systems for precision agriculture and agribusiness management: Detecting crop health using satellite imagery 用于精准农业和农业综合企业管理的可信赖人工智能知识系统：利用卫星图像检测作物健康状况

Science Talks

Pub Date : 2025-10-09 DOI: 10.1016/j.sctalk.2025.100494

V. Dhilipkumar , S. DineshKumar , S. Maheswari , Esra Sipahi Döngül

Precision agriculture urgently requires trustworthy AI knowledge systems to translate complex remote sensing data into actionable insights for crop health management. This study develops a robust framework for detecting critical crop stress indicators—specifically water stress and nutrient deficiency—in seasonal rice and mustard plantations within Majuli, Assam, India a region highly vulnerable to flooding. Leveraging high-resolution LANDSAT 8/9 OLI/TIRS satellite data, we compute essential vegetation (NDVI) and land surface temperature (LST) indices. These multi-modal inputs are processed using a pre-trained U-Net++ deep learning architecture for precise pixel-level segmentation of stressed crop areas. Evaluation demonstrates exceptional performance, with segmentation accuracy reaching 96.1 % and an F1-score of 94.5 %, confirming the model's high reliability. Beyond technical accuracy, this work explicitly addresses the trustworthy AI Knowledge for precision agriculture through transparent data sourcing by standardized indices, model explainability by using established U-Net++ architectures, and validation against known flood-impact scenarios. Organizational integration is facilitated by generating diagnostic knowledge directly usable by farmers and agricultural extension services in vulnerable ecosystems like Majuli, enabling timely interventions for improved resilience and yield. This approach exemplifies the development of trustworthy AI knowledge systems for sustainable agricultural decision-making.

精准农业迫切需要可靠的人工智能知识系统，将复杂的遥感数据转化为作物健康管理的可操作见解。本研究开发了一个强大的框架，用于检测印度阿萨姆邦Majuli地区季节性水稻和芥菜种植园的关键作物胁迫指标，特别是水分胁迫和营养缺乏，该地区极易受洪水影响。利用LANDSAT 8/9 OLI/TIRS高分辨率卫星数据，我们计算了基本植被（NDVI）和地表温度（LST）指数。这些多模态输入使用预训练的U-Net++深度学习架构进行处理，以精确地对受胁迫作物区域进行像素级分割。结果表明，该模型的分割准确率达到96.1%，f1得分为94.5%，具有较高的可靠性。除了技术准确性之外，这项工作还通过标准化指数的透明数据来源、使用已建立的U-Net++架构的模型可解释性以及针对已知洪水影响情景的验证，明确解决了可信赖的精准农业人工智能知识。通过在Majuli等脆弱生态系统中产生农民可直接使用的诊断知识和农业推广服务，促进了组织整合，从而能够及时采取干预措施，提高抗灾能力和产量。这种方法体现了可信赖的人工智能知识系统在可持续农业决策中的发展。

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引用次数: 0

CO2 capture and electro-reduction with the aid of ionic liquids and deep eutectic solvents 离子液体和深共晶溶剂辅助CO2捕获和电还原

Science Talks

Pub Date : 2025-10-08 DOI: 10.1016/j.sctalk.2025.100495

Fangfang Li , Xiaoyan Ji

The development of CO₂ capture and electro-reduction technologies offers a promising way to mitigate CO₂ emissions and enable CO₂ utilization under mild and sustainable conditions. However, current technologies still face challenges, including the high energy usage associated with CO₂ capture, and the low selectivity and limited reaction rates in CO₂ electro-reduction. Ionic liquids (ILs) and deep eutectic solvents (DESs), owing to their tunable nature, high thermal stability and CO₂ solubility, have emerged as versatile materials for addressing these bottlenecks. In our work, a series of ILs and DESs were systematically designed and modified with co-solvents or porous materials to enhance CO₂ capture performance. Comparative studies with conventional sorbents demonstrated the superior CO₂ capture performance of IL/DESs-based systems, highlighting their potential for industrial application. In addition, ILs/DESs were also employed as electrolytes in CO₂ electro-reduction, facilitating the conversion of CO₂ into value-added products. A techno-economic analysis was conducted for several possible products. Among them, CO was identified as the most economically product. To further enhance the catalytic performance, novel electrocatalysts were developed and evaluated, showing significant improvements. Nevertheless, continued innovation is still required to achieve a technological breakthrough toward scalable and efficient CO₂ capture and electro-reduction technologies.

二氧化碳捕获和电还原技术的发展为减少二氧化碳排放和在温和和可持续的条件下实现二氧化碳利用提供了一种有前途的方法。然而，目前的技术仍然面临挑战，包括与CO2捕获相关的高能量消耗，以及CO2电还原的低选择性和有限的反应速率。离子液体（ILs）和深共晶溶剂（DESs）由于其可调性质、高热稳定性和CO2溶解度，已成为解决这些瓶颈的通用材料。在我们的工作中，系统地设计了一系列的il和DESs，并使用共溶剂或多孔材料进行改性，以提高CO2捕获性能。与传统吸附剂的比较研究表明，基于IL/ dess的系统具有优越的二氧化碳捕获性能，突出了其工业应用潜力。此外，ILs/DESs还被用作CO2电还原的电解质，促进CO2转化为增值产品。对几种可能的产品进行了技术经济分析。其中，CO被认为是最经济的产品。为了进一步提高催化性能，新型电催化剂的开发和评价取得了显著进展。然而，为了实现可扩展和高效的二氧化碳捕获和电还原技术的技术突破，仍然需要持续的创新。

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引用次数: 0

Self-assesment instrument for measuring employability skills in chemistry vocational school students: A case study on Volta cell material topics 测量化学职业学校学生就业能力技能的自评工具：以伏特电池材料主题为例

Science Talks

Pub Date : 2025-10-03 DOI: 10.1016/j.sctalk.2025.100493

Harjono, Dimas Gilang Ramadhani, Sri Haryani, Ibrahim Wicaksono, Indri Hestinasari

Employability is a multidimensional construct, which encompasses technical, cognitive, and soft skills collectively improving ability to gain, keep, and advance in jobs. The purpose of this study is to create and validate a self-assessment instrument for measuring employability skills in vocational school students in the chemical engineering field, specifically on Volta Cell Material Topics. This study is part of a three-phase design: We developed an instrument based on the review of available literature and expert input, followed by pilot testing among vocational students, and finally, analysed data using descriptive statistics, skill gap analysis, and machine learning-based feature importance analysis and clustering techniques. Its profile assesses nine critical employability skill domains: communication, teamwork, problem-solving, initiative & entrepreneurship, planning & organizing, self-management, learning, technology, and occupational health & safety (OHS). The second focuses on identifying constructs to be developed, including obtaining increased levels of initiative, entrepreneurial skills, and communication, which indicates the need for purpose-built training programs specifically targeting workforce readiness. Our findings demonstrate the need for structured skills training in employability to be embedded within vocational education curricula, ensuring that students have the skills and competencies to meet the needs of the industry.

就业能力是一个多维度的结构，它包括技术、认知和软技能，这些技能共同提高了获得、保持和提升工作的能力。本研究的目的是建立并验证一个自我评估工具来衡量职业学校学生在化学工程领域的就业能力技能，特别是在伏特电池材料主题。本研究是三阶段设计的一部分：我们基于对现有文献和专家输入的回顾开发了一种工具，然后在职业学生中进行试点测试，最后使用描述性统计、技能差距分析和基于机器学习的特征重要性分析和聚类技术分析数据。它的简介评估了九个关键的就业技能领域：沟通、团队合作、解决问题、主动性和企业家精神、计划和组织、自我管理、学习、技术和职业健康和安全（OHS）。第二个重点是确定要开发的结构，包括获得更高水平的主动性、创业技能和沟通，这表明需要专门针对劳动力准备的专门构建的培训计划。我们的研究结果表明，有必要在职业教育课程中嵌入结构化的就业技能培训，以确保学生拥有满足行业需求的技能和能力。

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引用次数: 0

“Exploring tourism externalities in Karimunjawa: A participatory approach using photovoice” “探索Karimunjawa的旅游外部性：使用photovoice的参与式方法”

Science Talks

Pub Date : 2025-09-24 DOI: 10.1016/j.sctalk.2025.100492

Hapsari Ayu Kusumawardhani , Indah Susilowati , Deden Dinar Iskandar , Hadiyanto

This article aims to explores existing resources in Karimunjawa and analyzes externalities that occur in Karimunjawa caused by tourism activities. We used descriptive statistics, literature review, and photovoice. GIS complemented this research. Photovoice methods to elicit major themes regarding community participation perceptions of the impact of tourism. Overall, 16 participants photographed negative and positive impacts. Resources in Karimunjawa were divided based on physical attributes, community attributes, and rules in use. The externalities, divided into socio-economic and environmental aspects, were highlighted as photographed by photovoice participants. However, in documenting these insights, this chapter also draws out the strengths and limitations of photovoice as a participatory method in conservation contexts, including participants' perception of the approach and its ability to capture the dynamic impact of tourism in Karimunjava, Indonesia.

本文旨在探索卡里蒙贾瓦的现有资源，并分析卡里蒙贾瓦因旅游活动而产生的外部性。我们使用了描述性统计、文献回顾和照片语音。GIS补充了这项研究。Photovoice方法引出关于社区参与对旅游影响的看法的主要主题。总的来说，16名参与者拍摄了消极和积极的影响。Karimunjawa的资源划分基于物理属性、社区属性和使用规则。外部性，分为社会经济和环境方面，由photovoice参与者拍摄的照片强调。然而，在记录这些见解的同时，本章还指出了光声作为一种参与式方法在保护环境中的优势和局限性，包括参与者对该方法的看法及其捕捉印度尼西亚卡里蒙爪哇旅游业动态影响的能力。

引用次数: 0

Grand round teleconference as an inclusive pedagogy to empower NP education: Curriculum innovation for inclusive learning and competency enhancement 以全纳教学法推动NP教育：课程创新促进全纳学习与能力提升

Science Talks

Pub Date : 2025-09-19 DOI: 10.1016/j.sctalk.2025.100490

Eunyoung Lee, Marjorie Young, Carey Cole

Background

In ever-evolving higher education and online programs, faculty are challenged to keep students up to date and engaged in their learning. Grand Rounds (GR) have been primarily utilized in hospital settings for medical education for doctors and residency students.

Purpose/methods

Since 2016, Radford University has incorporated GRs into NP education to promote inclusive learning and enhance clinical reasoning/knowledge among students/faculty. GRs utilized a format of real clinical case analysis and offered weekly via teleconference. A student from the advanced clinical courses presents the case. Differential diagnoses, diagnostic tests, management, and relevant current practice guidelines were discussed as a group. Students in the early phase of the program were invited to the discussion. Additionally, GR has been used as a platform to discuss emerging or sensitive healthcare issues, incorporating real cases. Google Jamboard and SNAPPS methodology were incorporated to visualize interactive discussion and enhance oral case report skills.

Lessons learned

GR is an effective strategy to enhance student engagement and provide a safe shared-learning environment to empower students to refine clinical reasoning skills. Students reported increased engagement in their learning, feeling competent in diagnostic reasoning, treatment/management planning, and case reporting skills. This is supported by the preceptor's student evaluation report.

在不断发展的高等教育和在线课程中，教师面临的挑战是让学生了解最新情况，并参与他们的学习。大查房（GR）主要在医院环境中用于医生和住院医师学生的医学教育。目的/方法自2016年以来，雷德福大学将GRs纳入NP教育，以促进包容性学习，提高学生/教师的临床推理/知识。GRs采用真实临床病例分析的形式，每周通过电话会议提供。一位高级临床课程的学生介绍了这个病例。鉴别诊断、诊断试验、管理和相关的现行实践指南作为一组进行了讨论。该项目早期阶段的学生被邀请参加讨论。此外，GR还被用作讨论新兴或敏感医疗保健问题的平台，并结合实际案例。谷歌Jamboard和SNAPPS方法被纳入可视化互动讨论和提高口头病例报告技能。经验教训dgr是一种有效的策略，可以提高学生的参与度，并提供安全的共享学习环境，使学生能够完善临床推理技能。学生们报告说，他们的学习参与度有所提高，在诊断推理、治疗/管理计划和病例报告技能方面感到有能力。这是由导师的学生评估报告支持的。

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引用次数: 0

Evaluation of teachers' explaining skills: An approach based on artificial intelligence and AI causal analysis 教师讲解能力评价：基于人工智能和人工智能因果分析的方法

Science Talks

Pub Date : 2025-09-19 DOI: 10.1016/j.sctalk.2025.100491

Nafi'’atun Amaliya , Harjono , Sri Susilogati Sumarti , Ella Kusumastuti , Dimas Gilang Ramadhani

This study develops an innovative assessment system based on Artificial Intelligence (AI) and causal inference to objectively evaluate teachers' explaining skills. Traditional assessments often suffer from subjectivity and inconsistency, limiting their effectiveness in capturing the complex and dynamic nature of teaching skills. To overcome these limitations, this research integrates a black box AI model (Google Gemini 1.5 Flash LLM) and a glass box approach (multivariate linear regression) to provide transparent and interpretable assessments. Using 200 video transcripts from various teaching contexts, the results indicate a high correlation (r = 0.91) between the AI-generated scores and regression model predictions, confirming the validity and reliability of the approach. Causal analysis through Directed Acyclic Graphs (DAG) and Propensity Score Matching (PSM) identifies critical teaching indicators, such as “Adaptation to Student Understanding” (ATE = 0.41) and “Wait Time” (ATE = 0.48), which significantly impact teaching effectiveness. Counterfactual simulations further reveal potential score improvements by up to 0.6 points when enhancing these key areas. The proposed method provides systematic, transparent, and actionable insights, contributing significantly to improving educational quality through precise and evidence-based teacher evaluations. This research also aligns with the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 10 (Reduced Inequalities), by promoting equitable, innovative, and high-quality education practices.

本研究开发了一种基于人工智能和因果推理的创新评估系统，以客观地评估教师的解释技能。传统的评估往往存在主观性和前后不一致的问题，限制了评估教学技能的复杂性和动态性的有效性。为了克服这些限制，本研究将黑盒人工智能模型（谷歌Gemini 1.5 Flash LLM）和玻璃盒方法（多元线性回归）集成在一起，以提供透明和可解释的评估。使用来自不同教学背景的200个视频文本，结果表明人工智能生成的分数与回归模型预测之间存在高度相关性（r = 0.91），证实了该方法的有效性和可靠性。通过有向无环图（DAG）和倾向得分匹配（PSM）进行因果分析，发现了影响教学效果的关键教学指标，如“适应学生理解”（ATE = 0.41）和“等待时间”（ATE = 0.48）。反事实模拟进一步显示，当增强这些关键领域时，得分可能会提高0.6分。所提出的方法提供了系统、透明和可操作的见解，通过精确和基于证据的教师评估，为提高教育质量做出了重大贡献。本研究还通过促进公平、创新和高质量的教育实践，与可持续发展目标（SDG）保持一致，特别是可持续发展目标4（优质教育）、可持续发展目标9（产业、创新和基础设施）和可持续发展目标10（减少不平等）。

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引用次数: 0