Personalized Adaptive Learning in Undergraduate Mathematics: A Meta-Analysis

本研究旨在检视针对数学课程的个人化自适应学习（PAL）的先前研究，以确定PAL在何时、何地、对谁有帮助，有助于提高大学生的数学成绩，从而服务于国家利益。PAL使用智能学习系统，结合学习者的偏好，并分析和整合个人学习数据，以创建一个独特的学习者途径，以最大限度地提高学生成功的机会。该项目将广泛研究PAL在选修数学课程的本科生群体中的应用，并重点研究其在代数中的应用。每项研究都将调查总体学生人数以及服务不足的学生。本研究将运用元分析方法，探讨PAL在改善本科数学课程学生学习成果方面的有效程度，并为改进数学教学，包括数学课程设计和重新设计提供见解。荟萃分析方法将检查已发表的文献，综合单个研究的发现，使用定量统计来计算有效性的总体衡量标准。本研究的结果有望帮助提高本科学生在数学方面的成功，本科学生的保留和学位完成，以及获得职业机会和经济稳定。荟萃分析将从全面的文献综述开始，使用综合关键词进行。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纳入标准。纳入研究的摘要将由两位编码员进行筛选，然后进行全文审查。在全文阶段，将提取计算效应量所需的定量数据，然后使用多水平meta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差和假设检验，解决研究中效应大小的依赖关系。亚组分析将用于评估调节变量与效应大小之间的关系。Cochran的9个标准标准用于评估有单独对照组的研究中的偏倚风险。报告偏倚将通过漏斗图进行视觉评估，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普通受众、从业人员和研究人员/学者。研究结果对教师的专业发展、数学课程的再开发与再设计、课程支持服务和学生建议等方面都有启示，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。通过其参与学生学习轨道，该计划支持有前途的实践和工具的创建，探索和实施。该奖项反映了美国国家科学基金会的法定使命，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。

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.