论文标题
从{溶液合成}到基于块的视觉编程任务的{学生尝试合成}
From {Solution Synthesis} to {Student Attempt Synthesis} for Block-Based Visual Programming Tasks
论文作者
论文摘要
基于块的视觉编程环境越来越多地用于向初学者介绍计算概念。鉴于编程任务是开放式和概念性的，新手学生在这些环境中学习时经常挣扎。 AI驱动的编程导师在自动协助苦苦挣扎的学生方面拥有巨大的希望，并且需要几个组成部分来实现这一潜力。我们特别研究了学生建模的关键组成部分，特别是自动推断学生对预测（综合）行为的误解的能力。我们介绍了一个小说的基准Sudendyn，围绕以下挑战为中心：对于给定的学生，在观察到学生对固定参考任务的尝试后，综合了学生对新目标任务的尝试。这个挑战类似于程序合成。但是，与其综合{solution}（即，专家会编写的程序），不如说是综合一个{student of trim}（即给定学生会写作的程序）。我们首先表明，人类专家（导师）可以在基准上实现高性能，而简单的基线表现不佳。然后，我们开发了两种神经/符号技术（神经和符号），以寻求用导师缩小这一差距。
Block-based visual programming environments are increasingly used to introduce computing concepts to beginners. Given that programming tasks are open-ended and conceptual, novice students often struggle when learning in these environments. AI-driven programming tutors hold great promise in automatically assisting struggling students, and need several components to realize this potential. We investigate the crucial component of student modeling, in particular, the ability to automatically infer students' misconceptions for predicting (synthesizing) their behavior. We introduce a novel benchmark, StudentSyn, centered around the following challenge: For a given student, synthesize the student's attempt on a new target task after observing the student's attempt on a fixed reference task. This challenge is akin to that of program synthesis; however, instead of synthesizing a {solution} (i.e., program an expert would write), the goal here is to synthesize a {student attempt} (i.e., program that a given student would write). We first show that human experts (TutorSS) can achieve high performance on the benchmark, whereas simple baselines perform poorly. Then, we develop two neuro/symbolic techniques (NeurSS and SymSS) in a quest to close this gap with TutorSS.
