IGE: Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE)

大数据和分析数据的数据科学技能在所有科学、技术、工程和数学（STEM）领域都是至关重要的。在这种数据丰富的背景下，具有不同水平的数据科学和STEM培训的即将到来的STEM研究生可以使掌握数据科学技术的课程方法复杂化。线性和统一的准备模式甚至可能扩大不同准备水平的学生之间的成绩差距。这个问题可以通过用个性化的电子培训模块取代传统的“一刀切”的课程内容来解决，这些模块可以根据每个学生的独特优势、劣势和培训目标进行定制。这项授予宾夕法尼亚州立大学的国家科学基金会研究生教育创新奖旨在开发、测试和完善一套数字教育工具，这些工具由个性化途径和资源汇集到自适应控制理论启发的科学教育系统（ipractice）中。ipractice的目标是引导每个学生走向个人优化的培训途径，以掌握数据科学技术。ipractice系统将允许学生指定自己的学习目标，提供定制的评估来评估他们的表现水平，并指导他们获得帮助他们实现目标的教育资源。通过这种方式，ipractice系统可以作为个性化数字研究生教育系统的初步概念验证，该系统可以在广泛的教育环境中使用，以增强个人学习。个性化教育可以看作是一个控制理论问题，在这个问题中，学生寻求持续的输入，如课程、课程资源和训练练习，以尽量减少他们实际和目标专业水平之间的差异。ipractice系统将包括：(1)从现有教学资源中整理的数字培训材料和一个用户界面，供教师在系统中填充新的培训材料；(2)用户界面，明确培训目标；(3)评估学生当前能力水平的计算机化评估系统；(4)控制理论算法，可以自动提供最优的个性化培训模块。该项目将从宾夕法尼亚州立大学多个校区的研究生和高级本科生组成的校准样本中收集测试数据，以开发和测试评估系统（用于评估学生能力）和控制理论算法（用于对培训内容进行个性化推荐）。在初步校准后，ipractice系统的产品将扩大，并在不同的学生群体中比较学习成果。研究生教育创新（IGE）项目专注于研究生教育的研究。IGE的目标是试点、测试和验证研究生教育的创新方法，并产生将这些方法推广到更广泛的社区所需的知识。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Big data and the data science skills to analyze data are critical in all science, technology, engineering, and mathematics (STEM) areas. Within this data-rich context, incoming STEM graduate students with different levels of data science and STEM training can complicate curricular approaches for mastery of data science techniques. Linear and uniform preparation models may even widen the performance gap among students with diverse levels of preparation. This issue might be circumvented by replacing traditional "one-size-fits-all" course content with personalized electronic training modules that are tailored to each student's unique strengths, weaknesses, and training goals. This National Science Foundation Innovations in Graduate Education award to Penn State University aims to develop, test, and refine a set of digital educational tools brought together by the Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education system (iPRACTISE). The goal of iPRACTISE is to direct each student toward a personally optimized training pathway for mastery of data science techniques. The iPRACTISE system will allow students to specify their own learning goals, provide customized assessments to evaluate their performance levels, and guide them to educational resources that help them reach their goals. In this way, the iPRACTISE system serves as an initial proof-of-concept for a personalized, digital graduate educational system that could be adapted for use in a broad array of educational settings to enhance individual learning.Personalized education can be viewed as a control theory problem in which students seek ongoing input, such as classes, curricular resources and training exercises to minimize the discrepancies between their actual and targeted levels of expertise. The iPRACTISE system will include: (1) digital training materials curated from existing teaching resources and a user-interface for instructors to populate the system with new training materials; (2) a user interface to specify training goals; (3) a computerized assessment system that evaluates students' current ability levels; and (4) control theory algorithms that automate the delivery of optimal, individualized training modules. The project will collect test data from a calibration sample consisting of graduate and advanced undergraduate students from multiple campuses at Penn State University to develop and test the assessment system (for evaluating student competency) and the control theory algorithm (for making personalized recommendations on training contents). After initial calibration, the iPRACTISE system offerings will be expanded and learning outcomes compared across diverse student cohorts. The Innovations in Graduate Education (IGE) program is focused on research in graduate education. The goals of IGE are to pilot, test and validate innovative approaches to graduate education and to generate the knowledge required to move these approaches into the broader community.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.