Collaborative Research: CSEdPad: Investigating and Scaffolding Students' Mental Models during Computer Programming Tasks to Improve Learning, Engagement, and Retention

合作研究：CSEdPad：调查和搭建学生在计算机编程任务期间的心理模型，以提高学习、参与度和保留率

• 批准号: 1822816
• 负责人: Vasile Rus
• 金额: $ 49.91万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1822816&HistoricalAwards=false
• 关键词: Collaborative; Research; CSEdPad; Investigating; Scaffolding

Computing skills, such as computer programming, are an integral part of many disciplines, including the fields of science, technology, engineering, and math (STEM). Although such skills are in high-demand, and the number of aspiring Computer Science (CS) students is encouraging, a large gap between the supply of CS graduates and the demand persists because, for instance, college CS programs suffer from high attrition rates in introductory CS courses. One reason for the high attrition rates in introductory CS courses is the inherent complexity of CS concepts and tasks. To help students better cope with the high level of complexity, this project investigates a novel education technology, called CSEdPad (CS Education Pad), meant to ease students' introduction to programming during their early encounters with CS concepts and tasks. Moreover, the project forges new frontiers in CS education through a research program that advances our understanding of students' source code comprehension, learning, and motivational processes. The CSEdPad project has the potential to transform how students perceive computer science, increase their programming skills and self-efficacy, and lead to increased retention rates. The result will be a win-win-win situation for aspiring students, CS programs and their organizations, and the overall economy.The CSEdPad system design brings to bear proven educational technologies and techniques to improve students' mental model construction, learning, engagement, and retention in CS education. In particular, the system targets source code comprehension, a critical skill for both learners and professionals. It monitors and scaffolds source code comprehension processes while students engage in a variety of code comprehension tasks. Key approaches being explored include Animated Pedagogical Agents, self-explanation, and the Open Social Learner Model. Outcome variables include comprehension measures, learning gains, engagement level, retention, and self-efficacy. Due to its interdisciplinary nature, the project will impact several fields including Computer Science education, cognitive psychology, intelligent tutoring systems, and artificial intelligence. Students participating in the experiments will be selected from a diverse student body with respect to gender, ethnicity, and socioeconomic status. An increase in recruitment and retention of students from these populations will have far-reaching implications.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.

计算机编程等计算技能是许多学科不可或缺的一部分，包括科学、技术、工程和数学 (STEM) 领域。尽管对此类技能的需求量很大，而且有抱负的计算机科学 (CS) 学生的数量令人鼓舞，但计算机科学毕业生的供应与需求之间仍然存在巨大差距，因为例如，大学计算机科学课程的计算机科学入门课程的流失率很高。 CS 入门课程流失率高的原因之一是 CS 概念和任务固有的复杂性。为了帮助学生更好地应对高度的复杂性，该项目研究了一种新颖的教育技术，称为 CSEdPad（CS Education Pad），旨在帮助学生在早期接触 CS 概念和任务时轻松入门编程。此外，该项目通过一项研究计划开辟了计算机科学教育的新领域，该计划增进了我们对学生源代码理解、学习和激励过程的理解。 CSEdPad 项目有潜力改变学生对计算机科学的看法，提高他们的编程技能和自我效能，并提高保留率。其结果将是有抱负的学生、计算机科学项目及其组织以及整体经济的三赢局面。CSEdPad 系统设计带来了经过验证的教育技术和技巧，以改善学生在计算机科学教育中的心智模型构建、学习、参与和保留。特别是，该系统的目标是源代码理解，这对于学习者和专业人士来说都是一项关键技能。当学生参与各种代码理解任务时，它会监视和支撑源代码理解过程。正在探索的主要方法包括动画教学代理、自我解释和开放社会学习者模型。结果变量包括理解程度、学习收益、参与程度、记忆力和自我效能。由于其跨学科性质，该项目将影响计算机科学教育、认知心理学、智能辅导系统和人工智能等多个领域。参与实验的学生将从性别、种族和社会经济地位多样化的学生群体中挑选。增加对这些人群的学生招募和保留将产生深远的影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Towards Concept Map Based Free Student Answer Assessment

迈向基于概念图的免费学生答案评估

• 发表时间: 2019
• 期刊: Proceedings of the 32nd International Florida Artificial Intelligence Research Society Conference (FLAIRS 2019
• 作者: Maharjan, N.;Rus, V.
• 通讯作者: Rus, V.

Explaining educational recommendations through a concept-level knowledge visualization

通过概念级知识可视化解释教育建议

• DOI: 10.1145/3308557.3308690
• 发表时间: 2019
• 期刊: Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion
• 作者: Barria-Pineda, Jordan;Brusilovsky, Peter
• 通讯作者: Brusilovsky, Peter

The Effects of Open Self-Explanation Prompting During Source Code Comprehension

源代码理解过程中开放式自我解释提示的效果

• 发表时间: 2020
• 期刊: In Proceedings of the Thirty-Third International FLAIRS Conference (FLAIRS-33
• 作者: Tamang, L.J.

Attention Based Transformer for Student Answers Assessment

用于学生答案评估的基于注意力的变压器

• 发表时间: 2020
• 期刊: Proceedings of the Thirty-Third International FLAIRS Conference (FLAIRS-32
• 作者: Ait Khayi, N. &

Explaining Need-based Educational Recommendations Using Interactive Open Learner Models

使用交互式开放学习者模型解释基于需求的教育建议

• DOI: 10.1145/3314183.3323463
• 发表时间: 2019
• 期刊: UMAP '19
• 作者: Barria-Pineda, Jordan;Akhuseyinoglu, Kamil;Brusilovsky, Peter
• 通讯作者: Brusilovsky, Peter