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

该项目的第二阶段将商业化一个基于大数据分析的自适应在线学习平台，该平台建立在自适应学习的原则基础上。在线学习系统提供具有实时学习者分析的自适应学习策略。该系统从学习的四个方面整合了学习者分析：多媒体，学习策略，交互性和社交互动-为科学，技术，工程和数学（STEM）学生提供个性化的学习体验，以显着提高学习效果。具有个性化学习策略的自适应学习软件技术平台显示，在线学习中学后课程的学生完成率和满意率很高。在SBIR第二阶段，我们建议开发一个独特的数据驱动的决策支持界面，为个人和大量学习者提供实时大数据分析。大数据的数量、速度、多样性和准确性（大数据的4V）将首先通过在各个学校收集数据来产生，并有可能从地区、州甚至国家层面汇总数据。 通过自适应学习平台对学习者轨迹的大数据分析将揭示可以提高对教育中学习者行为的理解的模式。 与每种学习策略中的学习者行为相关的自适应学习系统生成的大数据将导致对拟议方法的有效性的宝贵见解，并在第二阶段进一步开发移动的平台上的产品。 学习者分析将为基于统计证据的智能反馈提供基础。 所提出的数据驱动决策过程的自适应学习方法是基于真实的时间互相关统计分析的预测变量的个人学习。 将在参与的高中进行拟议方法的实地试验。 在高中现场试验期间收集的一组学生的数据收集将有助于发现每种学习策略中学习者集群的学习模式。先前的第一阶段研究导致了在全校范围内收集的数据的数量，速度，种类和准确性的分析模型的发展。 此报告形成此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学校和大学STEM教育的能力和毕业率提供必要的反馈机制。

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.