Improving STEM K-12 Learning Using Optimal Spaced Retrieval in an Existing Educational Technology Platform

掌握STEM课程的材料需要学生在短时间内学习和记忆大量的新知识。长期以来，人们一直认为改善这种学习的一种方法是让学生通过在时间上间隔问题来练习新知识（间隔检索练习）。然而，在STEM背景下间隔检索实践的好处的证据是有限的。通过涉及美国不同初中和高中数千名STEM学生的多个课堂研究，该项目旨在了解间隔检索实践如何改善STEM学习，并开发针对STEM教育的间隔算法，这些算法在真实的环境中发挥作用。本研究将有助于在STEM教育中更好、更精确地实施间隔检索练习。该项目探讨了初中和高中学习者如何在STEM教育背景下使用间隔和检索练习获得知识，以及哪些特征对产生学习收益至关重要。这个项目的中心假设是，最佳间距检索实践是适应性的。也就是说，这取决于每个学生的学习历史和正在学习的内容。研究1和2调查是否间距的做法，主题（无论具体问题）是有益的间隔个别问题，在至少25个教室。这项工作将创建一个高保真的学生计算模型的研究3和4调查的学习和动机的影响，自适应间隔检索实践时间表在基于网络的教育应用程序中实施，相比非优化的非自适应版本和频繁使用的启发式算法间隔调度。研究5将在大规模的课堂研究中利用这些发现。研究2，4和5将发生在约50间教室，超过1200名初中和高中学生。总的来说，这个项目将有助于更好地理解学习，以及它是如何敏感的个人和时间因素。该项目由NSF的EDU核心研究（ECR）计划支持。ECR计划强调基础STEM教育研究，产生该领域的基础知识。投资是在关键领域是必不可少的，广泛的和持久的：干学习和干学习环境，扩大参与干，干劳动力发展。该项目由学生和教师创新技术体验（ITEST）计划共同资助，该计划支持建立对实践，计划要素，背景和过程的理解的项目，有助于增加学生对科学，技术，工程，信息和通信技术（ICT）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。

Mastering material in STEM classes requires students to learn and memorize large amounts of new knowledge in a short period of time. One way that has long been argued to improve such learning is by having students practice new knowledge by spacing questions over time (spaced retrieval practice). However, the evidence for the benefits of spaced retrieval practice in STEM contexts is limited. Through multiple classroom studies involving thousands of STEM students across different US middle and high schools, this project aims to understand how spaced retrieval practice improves STEM learning and to develop spacing algorithms specific to STEM education that work in authentic contexts. This research will contribute to a better and more precise implementation of spaced retrieval practice in STEM education.The project explores how middle and high school learners acquire knowledge using spacing and retrieval practice in the context of STEM education, and which features are critical to yield learning gains. The central hypothesis of this project is that the optimal spaced retrieval practice is adaptive. That is, it depends on each student's study history and what is being learned. Studies 1 and 2 investigate whether spacing practice by topic (regardless of the specific question) is as beneficial as spacing individual questions in at least 25 classrooms. This work will create a high-fidelity student computational model for studies 3 and 4 to investigate the learning and motivational impacts of adaptive spaced retrieval practice schedules implemented in a web-based educational application, compared to a non-optimized non-adaptive version and a frequently used heuristic algorithm for spaced scheduling. Study 5 will leverage these findings in a large-scale classroom study. Studies 2, 4, and 5 will occur in approximately 50 classrooms with over 1200 middle and high-school students. Overall, this project will contribute to a better understanding of learning and how it is sensitive to individual and temporal factors. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This project is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.