SBIR Phase II: Big data Analytics Driven Adaptive Learning for STEM Education

SBIR 第二阶段：大数据分析驱动的 STEM 教育自适应学习

• 批准号: 1632481
• 负责人: Nishikant Sonwalkar
• 金额: $ 74.73万
• 依托单位: EDWISETECH INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632481&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Big; data

This Phase II project will commercialize a big-data analytics-based adaptive online learning platform founded on the principles of adaptive learning. The online learning system provides adaptive learning strategies with real-time learner analytics. The system integrates learner analytics from four dimensional aspects of learning: multi-media, learning strategies, interactivity, and social interaction-- to deliver a personalized learning experience for science, technology, engineering, and math (STEM) students for significant improvement in the learning outcome. The adaptive learning software technology platforms with personalized learning strategies have demonstrated high completion and satisfaction rates for online students taking post-secondary courses. In this SBIR Phase II we propose to develop a unique data driven decision support interface that will result in real-time big data analytics for both individuals and large numbers of learners. The volume, velocity, variety, and veracity (the 4 Vs of big data) will be generated by the collection of data at individual schools first, with the potential to aggregate data from district, state, and even national levels. The big-data analytics of the learner trajectories through the adaptive learning platform will uncover patterns that can improve understanding of learner behavior in education. Big data, generated by the adaptive learning systems related to learner behavior in each learning strategy, will lead to valuable insights on efficacy of the proposed methodology and further development of the product on mobile platforms in the Phase II. The learner analytics will provide the basis for intelligent feedback based on the statistical evidence. The proposed method of data driven decision process for adaptive learning is based on the real- time cross-correlation statistical analysis of the predictor variables for an individual learner. The field trials of the proposed method will be conducted in the participating high schools. Collection of data for a group of students collected during the field trials in high schools will lead to discovery of learning patterns for the clusters of learners in each learning strategy. The previous Phase I research has led to the development of an analytical model for volume, velocity, variety, and veracity of data collected at school-wide level. This reports form this data is used for the development of decision tree and regression analysis to find correlations (knowledge discovery) that can be used for the improvement of learning enterprise (school). The data-driven feedback to students and the group analytics for teachers will provide necessary feedback mechanism for improving competency and graduation rates for STEM education in schools and colleges.

该阶段II项目将使基于自适应学习原理建立的基于大数据的自适应在线学习平台将其商业化。在线学习系统通过实时学习者分析提供自适应学习策略。该系统从学习的四个方面集成了学习者分析：多媒体，学习策略，互动性和社交互动 - 为科学，技术，工程和数学（STEM）学生提供个性化的学习经验，以显着改善学习成果。具有个性化学习策略的自适应学习软件技术平台已显示出在线课程的在线学生很高的完成和满意度。在SBIR II阶段，我们建议开发一个独特的数据驱动决策支持接口，该界面将为个人和大量学习者提供实时的大数据分析。首先，在单个学校的数据收集中，将产生数量，速度，品种和准确性（大数据的4 Vs），并有可能汇总来自地区，州甚至国家一级的数据。 通过自适应学习平台对学习者轨迹的大数据分析将发现可以提高对教育学习者行为的理解的模式。 大数据是由每个学习策略中与学习者行为相关的自适应学习系统生成的，将导致对拟议方法的有效性和在第二阶段移动平台上的进一步开发的有价值的见解。 学习者分析将根据统计证据为智能反馈提供基础。 自适应学习的数据驱动决策过程的提议的方法基于对单个学习者的预测变量的实时互相关统计分析。 拟议方法的现场试验将在参与的高中进行。 在高中的现场试验期间收集的一组学生的数据收集将导致每个学习策略中学习者的学习模式。上一阶段的研究导致开发了用于在学校范围内收集的数据的数量，速度，多样性和准确性的分析模型。 该报告构成了此数据，用于开发决策树和回归分析，以找到可用于改善学习企业（学校）的相关性（知识发现）。向学生和教师的小组分析的数据驱动反馈将提供必要的反馈机制，以提高学校和学院的STEM教育能力和毕业率。