SciLAF: Scientific-based Learning Assessment Framework for Student Knowledge Tracking

SciLAF：基于科学的学生知识跟踪学习评估框架

• 批准号: 1504728
• 负责人: Gabriel Terejanu
• 金额: $ 24.97万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2018-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1504728&HistoricalAwards=false
• 关键词: SciLAF; Scientific; based; Learning; Assessment

This proposal is addressing the fundamental challenge of assessing individual student's knowledge in cornerstone engineering classes with high student-to-faculty ratios. The goal is to: develop a computational assessment framework that easily integrates into an instructor's routine efforts to track student knowledge, suggest remedial interventions, and guide future examinations. The rationale is that individual student knowledge is a hypothesis/model that needs to be tested using the scientific method. Similarly, assessment instruments are just experiments to discover how well a student masters specific concepts. This fits naturally with probabilistic methodologies such as the Bayesian inference that formalize the scientific method.The main approach taken is to track the progress of individual students by developing student knowledge models based on Bayesian networks. This proposal addresses an open fundamental problem in constructing and using knowledge models to assess learning, namely how to relate curricular structure to knowledge models and how to inform the models using assessment data. The proposed methodology emphasizes the role of concept inventories to inform the construction of Bayesian networks models and to extract information from these models to suggest informative questions for future examinations. To facilitate ease of use and broader adoption by faculty, the software artifacts will be made available under open source licenses and the functionality of the framework will be integrated within a widely used learning management system through the development of a prototype plugin. The education and outreach aspects of this proposal include training participants in effective educational strategies and mentoring of future faculty.

该建议是针对评估学生与教师比率高的基石工程课程中个人知识的基本挑战。 目标是：开发一个计算评估框架，该框架可以轻松地集成到讲师的常规工作中，以跟踪学生知识，建议补救干预并指导未来的考试。理由是，个人知识是一种假设/模型，需要使用科学方法进行测试。同样，评估工具只是发现学生掌握特定概念的效果的实验。这自然与概率方法论（例如贝叶斯推论）形式化了科学方法。所采用的主要方法是通过基于贝叶斯网络开发学生知识模型来跟踪个别学生的进步。该建议解决了构建和使用知识模型评估学习的一个开放基本问题，即如何将课程结构与知识模型联系起来以及如何使用评估数据向模型提供信息。拟议的方法强调了概念清单在为贝叶斯网络模型的构建提供信息并从这些模型中提取信息的作用，以提出有关未来考试的信息性问题。为了促进教师的易用性和更广泛的采用，将在开源许可下提供软件工件，并且该框架的功能将通过开发原型插件将广泛使用的学习管理系统集成到。该建议的教育和外展方面包括培训有效的教育策略和未来教师的指导参与者。