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

SBIR 第一阶段：大数据分析驱动的 STEM 教育自适应学习

• 批准号: 1520242
• 负责人: Nishikant Sonwalkar
• 金额: $ 15万
• 依托单位: EDWISETECH INC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1520242&HistoricalAwards=false
• 关键词: SBIR; Phase; Big; data; Analytics

This SBIR Phase I project will develop a big-data analytics-based adaptive online learning platform founded on the principles of adaptive learning. The online learning system will provide adaptive learning strategies with real-time learner analytics. The systems will integrate 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 I 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 development of the product in 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 the 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 proposed research will then lead to the development of an analytical model for volume, velocity, variety, and veracity of data collected at school-wide level. This data then will be 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.

这个SBIR一期项目将开发一个基于大数据分析的自适应在线学习平台，建立在自适应学习的原则上。在线学习系统将提供自适应的学习策略和实时的学习者分析。这些系统将从学习多媒体、学习策略、互动和社交四个方面整合学习者分析--为科学、技术、工程和数学(STEM)学生提供个性化的学习体验，显著提高学习结果。具有个性化学习策略的自适应学习软件技术平台显示，在线学生参加大专课程的完成率和满意率很高。在此SBIR第一阶段，我们建议开发一个独特的数据驱动型决策支持界面，为个人和大量学员提供实时大数据分析。数量、速度、多样性和准确性(大数据的4V)将首先由个别学校的数据收集生成，并有可能聚合来自地区、州甚至国家层面的数据。通过自适应学习平台对学习者轨迹的大数据分析将揭示出有助于提高对教育中学习者行为的理解的模式。每种学习策略中与学习者行为相关的自适应学习系统产生的大数据将导致对所提出的方法的有效性和第二阶段产品开发的有价值的见解。学习者分析将为基于统计证据的智能反馈提供基础。提出的数据驱动的自适应学习决策过程方法是基于对个体学习者预测变量的实时互相关统计分析。提议的方法的实地试验将在参与的高中进行。在高中实地试验期间收集的一组学生的数据将导致发现每种学习策略下的学习者群的学习模式。然后，这项拟议的研究将导致开发一个分析模型，用于分析全校范围内收集的数据的数量、速度、多样性和准确性。然后将这些数据用于决策树的开发和回归分析，以找到可用于学习型企业(学校)改进的相关性(知识发现)。对学生的数据驱动反馈和对教师的群体分析将为提高中小学和大学STEM教育的能力和毕业率提供必要的反馈机制。