跨平台分类的学习技能等效性
Learning Skill Equivalencies Across Platform Taxonomies
摘要：
技能评估和报告是许多数字学习平台的核心功能。随着学生经常使用多个平台，跨平台评估已成为一个新的挑战。虽然学习工具互操作性（LTI）等技术已经实现了平台之间的通信，但协调它们采用的不同技能分类尚未大规模解决。在本文中，我们介绍和评估一种方法，通过利用问题的内容以及平台的点击流数据来寻找和链接平台之间的等效技能。我们提出了六种模型来表示技能作为连续的实值向量，并利用机器翻译来映射技能空间。这些方法在三个数字学习平台上进行了测试：ASSISTments，Khan Academy和Cognitive Tutor。我们的研究结果表明，从细粒度分类到粗粒度分类，技能等效性预测的准确性是合理的，在三个平台之间实现了0.8的平均recall@5。我们的技能翻译方法有助于在辅导和标准化测试世界中进行繁琐的分类到分类映射工作（也称为crosswalk）的手动过程。
英文摘要：
Assessment and reporting of skills is a central feature of many digital learning platforms. With students often using multiple platforms, cross-platform assessment has emerged as a new challenge. While technologies such as Learning Tools Interoperability (LTI) have enabled communication between platforms, reconciling the different skill taxonomies they employ has not been solved at scale. In this paper, we introduce and evaluate a methodology for finding and linking equivalent skills between platforms by utilizing problem content as well as the platform's clickstream data. We propose six models to represent skills as continuous real-valued vectors and leverage machine translation to map between skill spaces. The methods are tested on three digital learning platforms: ASSISTments, Khan Academy, and Cognitive Tutor. Our results demonstrate reasonable accuracy in skill equivalency prediction from a fine-grained taxonomy to a coarse-grained one, achieving an average recall@5 of 0.8 between the three platforms. Our skill translation approach has implications for aiding in the tedious, manual process of taxonomy to taxonomy mapping work, also called crosswalks, within the tutoring as well as standardized testing worlds.
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。
