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.

这项建议是解决评估个别学生的基础工程类与高师生比例的知识的基本挑战。 目标是：开发一个计算评估框架，可以很容易地集成到教师的日常工作，以跟踪学生的知识，建议补救措施，并指导未来的考试。其基本原理是，学生个人的知识是一个假设/模型，需要使用科学方法进行测试。同样，评估工具只是实验，以发现学生掌握特定概念的程度。这自然符合概率方法论，如贝叶斯推理，形式化的科学方法。所采取的主要方法是通过开发基于贝叶斯网络的学生知识模型来跟踪个别学生的进展。这一建议解决了一个开放的基本问题，在构建和使用知识模型来评估学习，即如何将课程结构与知识模型，以及如何通知模型使用评估数据。所提出的方法强调的作用，概念库存通知贝叶斯网络模型的建设，并从这些模型中提取信息，为今后的考试提出翔实的问题。为了便于使用和教师更广泛的采用，软件工件将在开源许可证下提供，框架的功能将通过开发原型插件集成到广泛使用的学习管理系统中。这项建议的教育和推广方面包括培训参与者有效的教育战略和指导未来的教师。