SBIR Phase I: An Instruction and Learning Analytics Platform to Improve Learning and Teaching in Science, Technology, Engineering, and Mathematics

SBIR 第一阶段：用于改善科学、技术、工程和数学学习和教学的教学和学习分析平台

• 批准号: 1345627
• 负责人: Muniram Budhu
• 金额: $ 15万
• 依托单位: YourLabs, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1345627&HistoricalAwards=false
• 关键词: SBIR; Phase; Instruction; Learning; Analytics

This SBIR Phase I project proposes to develop a novel diagnostic algorithm and methodology for obtaining detailed learner feedback in multi-step problem solving questions. This feedback is fundamental to derive learning patterns, identify at-risk learners, assess knowledge mastery, and inform both students and teachers about what was learned, what needs to be learned, and where and how mistakes were made. It informs the teacher what the students know and don?t know. The wealth of data collected by the proposed prototype will promote peer standards of excellence and student self-regulated learning, inform teaching practice, revise curricula, and inform educational administrators. This project is responding to a need in the educational system for personalized learning experiences for every learner, evidence-centered learning, and data that will improve teaching efficiency and effectiveness. The diagnostic assessment of the product will help educators understand the extent to which learners have mastered critical Science, Technology, Engineering, and Mathematics skills to identify specific areas in which improvement is needed and in which areas learners have mastered. The broader/commercial impact of this proposal is the real-time student feedback and the subsequent analysis of that data to efficiently transform Science, Technology, Engineering, and Mathematics education. It provides a formative, low cost technology product that can help teachers personalize and revise curriculum in real-time and promote step-by-step problem solving skills required for 21st century careers. This will help the U.S compete effectively in the global market. The in-depth learner database informs school administrators and education policymakers on changes required to improve student performances and teaching practice, and compliance with recently adopted common core state standards. Data mining across multiple levels - from single problem to school to district to state and internationally - allows school administrators to compare performances within the U.S. and abroad. This project allows for the discovery of knowledge gaps while enhancing teaching, learning and training in all educational settings.

这个SBIR第一阶段项目提出了开发一种新的诊断算法和方法，以获得详细的学习者反馈，在多步骤的问题解决问题。这种反馈对于得出学习模式、识别有风险的学习者、评估知识掌握情况以及告知学生和教师学到了什么、需要学习什么以及在哪里和如何犯错误至关重要。它告诉老师学生知道什么和做什么？不知道所提出的原型收集的丰富的数据将促进卓越的同行标准和学生自我调节的学习，通知教学实践，修改课程，并通知教育管理人员。 该项目是为了满足教育系统对每个学习者个性化学习体验、以证据为中心的学习以及提高教学效率和有效性的数据的需求。该产品的诊断评估将帮助教育工作者了解学习者掌握关键科学，技术，工程和数学技能的程度，以确定需要改进的特定领域以及学习者掌握的领域。该提案的更广泛/商业影响是实时学生反馈和随后的数据分析，以有效地改变科学，技术，工程和数学教育。 它提供了一种形成性的低成本技术产品，可以帮助教师个性化和实时修改课程，并促进21世纪世纪职业所需的逐步解决问题的技能。这将有助于美国在全球市场上有效竞争。深入的学习者数据库向学校管理人员和教育政策制定者提供有关改善学生表现和教学实践所需的变化以及遵守最近通过的共同核心州标准的信息。跨多个级别的数据挖掘-从单一问题到学校到地区到州和国际-允许学校管理人员比较美国和国外的表现。该项目有助于发现知识差距，同时加强所有教育环境中的教学和培训。