Personalized Adaptive Learning in Undergraduate Mathematics: A Meta-Analysis

本科数学中的个性化适应性学习：荟萃分析

• 批准号: 2236074
• 负责人: Debbie Hahs-Vaughn
• 金额: $ 30万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236074&HistoricalAwards=false
• 关键词: Personalized; Adaptive; Learning; Undergraduate; Mathematics

This project aims to serve the national interest by examining prior research on personalized adaptive learning (PAL) specific to mathematics curricula, to identify where, when, and for whom PAL is helpful toward improving undergraduate students’ mathematics performance. PAL uses intelligent learning systems, incorporates preferences of the learner, and analyzes and incorporates individual learning data to create a unique learner pathway to maximize the chance of student success. This project will examine PAL within the population of undergraduate students enrolled in mathematics courses broadly along with a more focused study of its use in algebra. Each study will examine the overall student population as well as underserved students. Using meta-analytic procedures, the proposed research will investigate the extent to which PAL is effective in improving student learning outcomes in undergraduate mathematics courses and will provide insight on PAL for improving mathematics instruction, including mathematics course design and redesign. The meta-analytic approach will examine published literature, synthesizing findings across single studies using quantitative statistics to compute an overall measure of effectiveness. The results of this study are expected to help increase undergraduate student success in mathematics, undergraduate student retention and degree completion, and access to career opportunities and economic stability. The meta-analysis will begin with a thorough literature review, conducted using comprehensive keywords. Studies identified in the literature review will be examined to determine if they meet inclusion criteria. Abstracts of included studies will be screened by two coders, followed by full-text reviews. At the full-text stage, quantitative data needed for computing effect sizes will be extracted and then analyzed using multilevel meta-analysis. Moderator variables coded will include: student, type of PAL, class, and institutional characteristics. Robust variance estimation will be used to estimate standard errors and hypothesis tests, addressing dependencies of effect sizes within studies. Subgroup analyses will be used to evaluate the relationship between moderator variables and the magnitude of the effect size. Cochran’s nine standard criteria for assessing risk of bias in studies with a separate control group will be applied. Reporting bias will be assessed visually with funnel plots which assist in identifying small-study effects (of which, non-reporting bias may be the culprit). Dissemination efforts will aim to reach general audiences, practitioners, and researchers/scholars. The results have implications for improving faculty professional development, mathematics course redevelopment and redesign, course support services, and student advising, all of which may help to improve student success in mathematics. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在通过检查先前对数学课程的个性化自适应学习（PAL）的研究来服务于国家利益，以确定PAL在何处，何时以及对谁有助于提高本科生的数学成绩。PAL使用智能学习系统，结合学习者的偏好，分析并结合个人学习数据，以创建一个独特的学习者路径，最大限度地提高学生成功的机会。本计画将探讨PAL在就读数学课程的本科生人口中的广泛沿着，并更集中地研究其在代数中的应用。每项研究都将调查整体学生人口以及服务不足的学生。使用元分析程序，拟议的研究将调查PAL在多大程度上有效地提高学生的学习成果在本科数学课程，并将提供关于PAL的见解，以改善数学教学，包括数学课程设计和重新设计。荟萃分析方法将检查已发表的文献，使用定量统计综合单个研究的结果，以计算有效性的总体衡量标准。这项研究的结果预计将有助于提高本科生在数学，本科生保留和完成学位，并获得就业机会和经济稳定的成功。 荟萃分析开始时将使用综合关键词进行全面的文献综述。将对文献综述中确定的研究进行检查，以确定它们是否符合入选标准。纳入研究的摘要将由两名编码员筛选，然后进行全文综述。在全文阶段，将提取计算效应量所需的定量数据，然后使用多级荟萃分析进行分析。编码的调节变量将包括：学生，PAL类型，班级和机构特征。稳健方差估计将用于估计标准误和假设检验，解决研究中效应量的依赖性。亚组分析将用于评价调节变量与效应量大小之间的关系。将应用Cochran的9个标准，用于评估具有单独对照组的研究中的偏倚风险。将使用漏斗图对报告偏倚进行目视评估，漏斗图有助于识别小型研究效应（其中，非报告偏倚可能是罪魁祸首）。传播工作的目标是面向一般受众、从业人员和研究人员/学者。研究结果对提高教师的专业发展，数学课程的重新开发和重新设计，课程支持服务和学生咨询，所有这些都可能有助于提高学生在数学方面的成功。NSF IUSE：EHR计划支持研究和开发项目，以提高所有学生STEM教育的有效性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。