BIGDATA: EAGER: Deep Learning in Higher Education Big Data to Explore Latent Student Archetypes and Knowledge Profiles

BigData：高等教育中的深度学习大数据探索潜在的学生原型和知识概况数据科学技术已经彻底改变了许多学术领域，并在商业领域带来了巨大的收益。到目前为止，它们在解决美国教育系统中的关键问题方面一直没有得到充分利用，特别是在了解科学、技术、工程和数学(STEM)学习和学习环境、扩大STEM的参与以及增加传统上STEM服务不足的学生的留住方面。教育与人力资源局(EHR)通过EHR核心研究计划，为推进大数据科学与工程(BigData)计划的基础和应用的关键技术和技术而制定的目标是推进旨在理解和解决这些关键问题的基础研究，并促进数据科学在教育研究中的使用。深度学习是机器学习中的一种相对较新的方法，它在成功分类方面比以往的机器学习方法有了很大的改进，对数据应用的先验定义很少。例如，在图像分类中，这些技术在区分物种和特定动物方面具有很高的成功率。这项探索性的建议将利用来自大规模在线公开课(MOOC)和学习管理系统(LMS)的教育数据来研究使用这种方法的可能性。研究表明，使用MOOC数据的传统学习设计往往很难使用，因为注册是开放的，而且许多不同类型的人注册了MOOC。这一因素使得这些MOOC很难适应个体学习者，这是改善学习的最佳途径。首席调查员将在MOOC中将相似的人分组，以了解参加MOOC的不同类型的人，以便为他们量身定做学习活动。首席调查员将审查各种数据，从完成练习的时间或活动之间的停顿的微观后端数据，到宏观层面的数据，如课程历史和成绩。他们将使用深度学习技术来识别具有相似特征的学习者群体，这是使学习环境更具适应性的第一步。拟议中的研究是雄心勃勃的，也是有风险的。深度学习技术显示出巨大的前景，首席调查人员证明，它们可以在解决关键的教育挑战方面提供极大的帮助。首席调查员拥有技术和学习科学的专门知识，可以开展这项雄心勃勃的努力。随着越来越多和更大的教育数据集的开发，该领域需要扩大其方法，以向它们学习。这项提议的独特之处在于其催化这一努力的潜力。该奖项得到了EHR核心研究(ECR)计划的支持。ECR计划强调基础STEM教育研究，以产生该领域的基础知识。在基本、广泛和持久的关键领域进行投资：STEM学习和STEM学习环境，扩大STEM的参与，以及STEM劳动力发展。

BIGDATA: Deep Learning in Higher Education Big Data to Explore Latent Student Archetypes and Knowledge ProfilesData science techniques have revolutionized many academic fields and led to terrific gains in the commercial sector. They have to date been underutilized in solving critical problems in the US educational system, particularly in understanding Science, Technology, Engineering and Mathematics (STEM) learning and learning environments, broadening participation in STEM, and increasing retention for students traditionally underserved in STEM. The goals of the Directorate for Education and Human Resources (EHR), through the EHR Core Research program, for the Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA) program are to advance fundamental research aimed at understanding and solving these critical problems, and to catalyze the use of data science in Education Research. Deep learning is a relatively novel method in machine learning (ML) that has shown great improvements over prior ML approaches in successful classification with very little a priori definitions applied to the data. For example, in image classification, these techniques have a high rate of success at distinguishing species and particular animals. This exploratory proposal will investigate the possibilities of using this approach with educational data from Massive Open Online Courses (MOOCs) and Learning Management Systems (LMSs). Research has shown that it is often difficult to use traditional study designs with MOOC data as enrollment is open and many different types of people enroll in MOOCs. This factor has made it difficult to make these MOOCs adaptive to individual learners, an optimal approach to improving learning. The Principal Investigators will group people in MOOCs who are similar to each other to understand the different types of people taking the MOOCs so that learning activities can be tailored to them.The Principal Investigators will examine a variety of data, from the micro-level of backend data on timing to complete an exercise, or pauses between activities, to macro-level data such as course history and grades. They will use deep learning techniques to identify groups of learners with similar characteristics, the first step in making a learning environment more adaptive. The proposed research is ambitious and risky. Deep learning techniques show great promise and the Principal Investigators demonstrate that they could be extremely helpful in solving key educational challenges. The Principal Investigators have the technical and learning science expertise to carry out this ambitious endeavor. As more and larger educational datasets are developed, the field needs to expand its methodologies to learn from them. This proposal is unique in its potential for catalyzing this effort.This award is supported by the EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development.