VLSI启发的学生学习社区创建和细化方法
VLSI-Inspired Methods for Student Learning Community Creation and Refinement
摘要：
COVID-19严重扰乱了学术机构提供教育内容的方式，迅速加速了在线和混合学习的采用。本论文探讨了如何创建和完善优化的学生学习社区，以支持疫情和疫情后环境下的在线和混合学习。注册大学课程的学生可以被建模为类似于电路网表的注册网络。学习社区是通过将学生分组来创建的，最大限度地优化内部联系以支持学生学习，最小限度地减少外部联系以减少疾病传播。三个基于超大规模集成的聚类算法：超边缘粗化，修改超边缘粗化，最佳选择，修改聚类学生招生网络。使用相同的优化标准，通过模拟退火算法进一步细化聚类算法创建的学习社区。学习社区创建和细化框架结合了网络建模、学习社区创建和学习社区细化的所有三个阶段。所提出的框架在第三年电气工程秋季2020招生数据集和一个非常大的秋季2020和冬季2021招生数据集上进行了测试。在聚类算法中，最佳选择算法表现最好，能够为给定的最大聚类大小创建优化标准的学习社区。模拟退火算法可以通过显著提高聚类质量来细化聚类结果。该框架能够为小型和大型招生网络创建和完善学习社区，但它更适合在项目层面创建量身定制的学习社区。未来的工作，包括创建基于其他优化标准的学生学习社区，应该探索。
英文摘要：
COVID-19 significantly disrupted how educational contents are delivered in academic institutions, rapidly accelerating the adoption of online and blended learning. This thesis explores the creation and refinement of optimized student learning communities as a mean to support online and blended learning in the pandemic and post-pandemic setting. Students enrolled in university courses can be modeled as an enrollment network akin to a circuit netlist. Learning communities are created by clustering students into groups, optimized for maximum internal connection to support student learning, and minimum external connection to reduce disease transmission. Three VLSI-based clustering algorithms: Hyperedge Coarsening, Modified Hyperedge Coarsening, and Best Choice, are modified to cluster student enrollment networks. The learning communities created by the clustering algorithms are further refined by the Simulated Annealing algorithm using the same optimization criteria. The Learning Community Creation and Refinement Framework combines all three stages of network modeling, learning community creation, and learning community refinement. The proposed framework is tested on both the 3rd year Electrical Engineering Fall 2020 enrollment dataset and a very large Fall 2020 and Winter 2021 enrollment dataset. Best Choice performed the best among the clustering algorithms, capable of creating learning communities for the optimization criteria for a given maximum cluster size. Simulated Annealing can refine the clustering results by significantly increase cluster quality. The framework is capable of creating and refining learning communities for both the small and the large enrollment networks, but it is better suited for creating tailored learning communities at a program level. Future work, including creating student learning communities based on other optimization criteria, should be explored.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
