Collaborative Research: Scalable scaffolding of novice programmers' learning and automated analysis of their online activities

对精通编程的劳动力的需求，在全国范围内教授编程技能所遇到的挑战，以及大规模开放在线课程（MOOCs）的出现，都强调了对现有技术进行革命的重要性，这些技术旨在通过无监督的实践来支持学习。在智能辅导系统中，对手工设计的学习体验的依赖已经在这些技术的增强中造成了瓶颈。这个项目的意义和重要性在于创建一个通过辅导系统自动提供编程实践问题的系统。帕森斯谜题是这项工作的重点，它已经证明了编程技能的提高。该项目将继续研究帕森斯谜题的有效性，并确定创造帕森斯谜题的进化算法方法是否能有效地产生相同或更好的学习结果。为此，本项目将实现两个互补的目标。首先，交互式进化算法将用于自主设计帕森斯谜题。这些相对较新的实践问题在支持编程技能的发展方面已经显示出巨大的希望。通过将维果茨基的近端发展区理论整合到我们的适应度函数设计中，进化后的帕森斯谜题的教学合理性将进一步增强。其次，协同进化算法的最新突破将被用于数据挖掘潜在的学习者-问题交互空间。这将允许自动提取有关最具信息的交互的见解。这些信息有可能为进化和手工设计的帕森斯谜题的意义提供新的视角，以及指导学生通过这些谜题以确保适当脚手架的顺序。在比较手工设计的帕森斯谜题和进化算法创建的帕森斯谜题时，将使用平均难度和尝试解决次数的定量和客观度量。

The need for a programming-savvy workforce, the challenges encountered nation-wide in teaching programming skills, and the advent of massively open online courses (MOOCs) all stress the importance of revolutionizing existing technologies meant to support learning via unsupervised practice. The reliance on hand-designed learning experiences in Intelligent Tutoring Systems has created a bottleneck in the enhancement of such technologies. The significance and importance of this project will be the creation of a system that automatically provides programming practice problems via a tutoring system. Parsons puzzles, which are the focus of this work, have already demonstrated improvement in programming skills. This project will continue research on the effectiveness of Parsons puzzles and determine whether an evolutionary algorithm approach to creating Parsons puzzles is effective in producing the same or better learning outcomes.To this end, this project will achieve two complementary goals. First, Interactive Evolutionary Algorithms will be used to autonomously design Parsons puzzles. These relatively new practice problems have already shown great promise in supporting the development of programming skills. By integrating Vygotsky's Zone of Proximal Development theory in the design of our fitness function, the pedagogical soundness of the evolved Parsons puzzles will be further enhanced. Second, recent breakthroughs in Co-evolutionary Algorithms will be leveraged to data-mine the underlying learners-problems interaction space. This will permit autonomous extraction of insights about the most informational interactions. Such information has the potential to provide new perspectives on the significance of both evolved and hand-designed Parsons puzzles, along with the order in which to guide students through these puzzles to ensure proper scaffolding. Quantitative and objective measures of differences in mean difficulty and number of attempts to solution will be used in comparing hand-designed Parsons puzzles and evolutionary algorithm created Pasons puzzles.