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: 1822752
• 负责人: Peter Brusilovsky
• 金额: $ 25.05万
• 依托单位: University of Pittsburgh
• 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=1822752&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.

计算技能，如计算机编程，是许多学科的组成部分，包括科学、技术、工程和数学（STEM）领域。尽管对这些技能的需求很大，而且有抱负的计算机科学（CS）学生的数量也令人鼓舞，但CS毕业生的供应与需求之间的巨大差距仍然存在，例如，大学CS课程的入门课程的流失率很高。计算机科学入门课程的高流失率的一个原因是计算机科学概念和任务固有的复杂性。为了帮助学生更好地应对高水平的复杂性，该项目研究了一种新的教育技术，称为CSEdPad (CS教育Pad)，旨在减轻学生在早期接触CS概念和任务时对编程的介绍。此外，该项目通过一个研究项目开拓了计算机科学教育的新领域，该研究项目促进了我们对学生源代码理解、学习和激励过程的理解。CSEdPad项目有可能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。其结果将是一个三赢的局面，对有抱负的学生，计算机专业和他们的组织，以及整体经济。CSEdPad系统的设计带来了成熟的教育技术和技巧，以提高学生的心理模型的构建，学习，参与和保留在计算机科学教育。特别是，该系统的目标是源代码理解，这对学习者和专业人员来说都是一项关键技能。当学生从事各种代码理解任务时，它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。结果变量包括理解测量、学习收获、参与水平、保留和自我效能。由于其跨学科性质，该项目将影响多个领域，包括计算机科学教育、认知心理学、智能辅导系统和人工智能。参与实验的学生将根据性别、种族和社会经济地位从多样化的学生群体中选出。从这些人口中招收和保留更多的学生将产生深远的影响。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。

项目成果

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.

Knowledge Annotation for Intelligent Textbooks

• DOI: 10.1007/s10758-021-09544-z
• 发表时间: 2020-05
• 期刊: Technology, Knowledge and Learning
• 作者: Mengdi Wang;Hung Chau;Khushboo Thaker;Peter Brusilovsky;Daqing He

Experiments with a Socratic Intelligent Tutoring System for Source Code Understanding

苏格拉底式源代码理解智能辅导系统实验

• 发表时间: 2020
• 期刊: In Proceedings of the Thirty-Third International FLAIRS Conference (FLAIRS-32
• 作者: Alshaikh, Z.

Improving Engagement in Program Construction Examples for Learning Python Programming

提高学习 Python 编程的程序构建示例的参与度

• DOI: 10.1007/s40593-020-00197-0
• 发表时间: 2020
• 期刊: International Journal of Artificial Intelligence in Education
• 影响因子: 4.9
• 作者: Hosseini, Roya;Akhuseyinoglu, Kamil;Brusilovsky, Peter;Malmi, Lauri;Pollari-Malmi, Kerttu;Schunn, Christian;Sirkiä, Teemu
• 通讯作者: Sirkiä, Teemu

Preliminary Experiments with Transformer based Approaches To Automatically Inferring Domain Models from Textbooks

• 发表时间: 2022
• 期刊: Scandinavian Journal of Public Health
• 影响因子: 3.4
• 作者: Rabin Banjade