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，并在商业领域取得了巨大的成就。迄今为止，它们在解决美国教育系统中的关键问题方面没有得到充分利用，特别是在理解科学，技术，工程和数学（STEM）学习和学习环境，扩大STEM的参与，以及提高传统上STEM服务不足的学生的保留率方面。教育和人力资源局（EHR）的目标是通过EHR核心研究计划，推进大数据科学工程（BIGDATA）计划的基础和应用的关键技术和技术，以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应用。深度学习是机器学习（ML）中一种相对较新的方法，在成功分类方面，它比以前的ML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。这个探索性的建议将调查使用这种方法与来自大规模开放式在线课程（MOOC）和学习管理系统（LMS）的教育数据的可能性。研究表明，使用MOOC数据的传统研究设计通常很困难，因为入学是开放的，许多不同类型的人都参加了MOOC。这一因素使得这些MOOC很难适应个别学习者，这是改善学习的最佳方法。首席研究员将对MOOC中相似的人进行分组，以了解参加MOOC的不同类型的人，以便为他们量身定制学习活动。首席研究员将检查各种数据，从完成练习的时间或活动之间的停顿的微观后端数据到课程历史和成绩等宏观数据。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。拟议中的研究雄心勃勃，风险很大。深度学习技术显示出巨大的前景，首席研究员证明，它们在解决关键的教育挑战方面非常有帮助。主要研究人员拥有技术和学习科学的专业知识来开展这项雄心勃勃的奋进。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，以从中学习。该提案在催化这一努力的潜力方面是独一无二的。该奖项由EHR核心研究（ECR）计划支持。 ECR计划强调基础STEM教育研究，产生该领域的基础知识。 投资是在关键领域是必不可少的，广泛的和持久的：干学习和干学习环境，扩大参与干，干劳动力发展。

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.