Meta-Analysis of Effectiveness of Simulation and Adaptive Learning Systems in STEM Education

STEM 教育中模拟和自适应学习系统有效性的元分析

• 批准号: 1661105
• 负责人: Shuyan Sun
• 金额: $ 29.98万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1661105&HistoricalAwards=false
• 关键词: Meta; Analysis; Effectiveness; Simulation; Adaptive

This proposal was submitted in response to EHR Core Research (ECR) program announcement NSF 15-509. The ECR program of fundamental research in STEM education provides funding in critical research areas that are essential, broad and enduring. EHR seeks proposals that will help synthesize, build and/or expand research foundations in the following focal areas: STEM learning, STEM learning environments, STEM workforce development, and broadening participation in STEM. The ECR program is distinguished by its emphasis on the accumulation of robust evidence to inform efforts to (a) understand, (b) build theory to explain, and (c) suggest interventions (and innovations) to address persistent challenges in STEM interest, education, learning, and participation.Simulation and adaptive learning systems are widely used instructional technologies in STEM education from K-12 to graduate levels. Their use was endorsed by the U.S. Department of Education in its 2016 national technology plan. Using simulation to provide rich learning experiences and timely formative assessment is aligned with the trend towards competency-based education and the NSF's work in advancing cyberlearning. However, evidence-based practices for using the two technologies to engage learners and enhance learning outcomes are lacking. To address this urgent need, this project will synthesize existing knowledge on the effectiveness of two technologies in STEM education and identify evidence-based practices of technology use. These efforts will generate new knowledge about how to use technology effectively to transform STEM education, build a larger and stronger STEM workforce for the U.S., and ultimately ensure the nation's future prosperity, innovation potential, and global economic competitiveness. The project team will conduct two separate meta-analyses to quantify the effects of simulation and adaptive learning systems on STEM learning and engagement. Meta-analysis is a powerful method to synthesize findings from multiple research studies and produce robust and accumulative evidence to inform future research and practice. This project will focus on research studies that evaluated the effectiveness of simulation and adaptive learning systems in STEM education. For each study included in meta-analyses, effect sizes of technology use on learners' engagement (e.g., participation, enjoyment, interest) and achievement (e.g., grades) will be calculated. Effect size is a standardized statistical measure that quantifies the magnitude of effects and allows comparison of effects across research studies. Effect sizes from all included studies will be pooled to estimate an overall effect size for each technology and the degree to which effect sizes vary from one study to another. Analyses will also be conducted to determine the extent to which effect sizes are related to how technology was used (i.e., pedagogy) and whether effect sizes vary by learner characteristics (e.g., gender, age, ethnicity) and learning environments (e.g., delivery modes, courses, settings, institutions). This project will provide timely and robust evidence regarding the effectiveness of each technology to inform STEM learning, teaching, future research, and technology development

该提案是针对电子病历核心研究(ECR)计划公告NSF 15-509而提交的。ECR STEM教育基础研究计划为关键的研究领域提供资金，这些领域是基本的、广泛的和持久的。EHR寻求的建议将有助于综合、建立和/或扩大以下重点领域的研究基础：STEM学习、STEM学习环境、STEM劳动力发展和扩大STEM的参与。ECR计划的特点是强调积累可靠的证据，为以下努力提供信息：(A)理解，(B)建立解释理论，(C)建议干预措施(和创新)，以解决STEM兴趣、教育、学习和参与方面的持续挑战。模拟和自适应学习系统广泛用于从K-12到研究生水平的STEM教育。它们的使用得到了美国教育部在2016年国家技术计划中的批准。利用模拟提供丰富的学习经验和及时的形成性评估符合以能力为基础的教育的趋势和国家科学基金会在推进网络学习方面的工作。然而，使用这两种技术来吸引学习者并提高学习成果的循证做法是缺乏的。为了满足这一迫切需要，该项目将综合现有关于两种技术在STEM教育中的有效性的知识，并确定技术使用的循证做法。这些努力将产生关于如何有效利用技术来转变STEM教育的新知识，为美国建立更大、更强大的STEM劳动力队伍，并最终确保美国未来的繁荣、创新潜力和全球经济竞争力。项目组将进行两项单独的荟萃分析，以量化模拟和适应性学习系统对STEM学习和参与的影响。Meta分析是综合多项研究结果的有力方法，并为未来的研究和实践提供可靠和积累的证据。该项目将侧重于评估模拟和自适应学习系统在STEM教育中的有效性的研究。对于包括在元分析中的每一项研究，将计算技术使用对学习者的参与(例如，参与、享受、兴趣)和成绩(例如，成绩)的影响程度。效应大小是一种标准化的统计测量方法，可以量化影响的大小，并允许在研究研究中比较影响。所有纳入的研究的效果大小将被汇集在一起，以估计每种技术的总体效果大小，以及不同研究的效果大小不同的程度。还将进行分析，以确定影响大小在多大程度上与技术的使用方式(即教学方法)有关，以及影响大小是否因学习者特征(如性别、年龄、族裔)和学习环境(如授课方式、课程、环境、机构)而异。该项目将为STEM的学习、教学、未来研究和技术开发提供及时和有力的证据，说明每种技术的有效性