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

协作研究：新手程序员学习的可扩展支架以及在线活动的自动分析

• 批准号: 1503834
• 负责人: Rudolf Wiegand
• 金额: $ 6万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1503834&HistoricalAwards=false
• 关键词: Collaborative; Research; Scalable; scaffolding; novice

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.

对精通编程的劳动力的需求、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（MOOC）的出现都强调了彻底改变现有技术的重要性，这些技术旨在通过无监督的实践来支持学习。智能辅导系统对手工设计学习体验的依赖为此类技术的增强造成了瓶颈。该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练习题的系统。 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，并确定创建帕森斯谜题的进化算法方法是否能有效产生相同或更好的学习结果。为此，该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已经在支持编程技能的发展方面显示出了巨大的希望。通过将维果茨基的最近发展区理论整合到我们的适应度函数的设计中，进化的帕森斯谜题的教学可靠性将得到进一步增强。其次，协同进化算法的最新突破将用于对潜在的学习者问题交互空间进行数据挖掘。这将允许自主提取有关信息最多的交互的见解。这些信息有可能为进化和手工设计的帕森斯谜题的重要性提供新的视角，以及引导学生完成这些谜题的顺序，以确保正确的脚手架。对平均难度和解决尝试次数差异的定量和客观测量将用于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。