Project Engage: Training Secondary Teachers to Deliver Computer Science and Engineering Instruction

项目参与：培训中学教师提供计算机科学与工程教学

• 批准号: 1441009
• 负责人: Pauline Dow
• 金额: $ 45.73万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1441009&HistoricalAwards=false
• 关键词: Project; Engage; Training; Secondary; Teachers

Developing computer science teachers who can support high school students in being successful in rigorous, academic computer science courses is a national need. Project Engage: Training Secondary Teachers to Deliver Computer Science and Engineering Instruction will support the implementation of an emerging Computer Science Principles course called Thriving in Our Digital World (TODW) in forty-five urban, suburban, and rural schools, including public, private, magnet, and charter schools, in Texas. The TODW course will be offered in a unique dual enrollment mode (concurrent high school and college credit). This project will test two innovative professional development techniques for expanding the reach to more schools: flipped classroom techniques, in which teachers receive recorded video-based professional development, and then use face-to-face time for more hands-on activities; and an automated system which will use artificial intelligence technology to support negotiation of common grades for student work between high school teachers and college faculty. This project's research agenda will address the following core challenge: In a field such as computer science where there is limited expertise, how can we most efficiently scale professional development so as many schoolteachers as possible can provide high quality instruction? The STEM-C (Science, Technology, Engineering, and Mathematics, including Computing) Partnerships program supports research-driven partnerships between STEM experts and K-12 school systems to bring about institutional change for better STEM education at the K-12 level. This STEM-C Partnerships' Computer Science Education Expansion project builds on prior funding of the UTeachEngineering: Training Secondary Teachers to Deliver Design-Based Engineering Instruction Partnerships through the National Science Foundation's Math and Science Partnership program. This project will produce the scientific foundation and the concrete artifacts needed to deliver a highly scalable curriculum for TODW. The artifacts will include a differentiated curriculum, scalable professional development, and scalable assessment tools and processes. The differentiated curriculum will include formative assessments which can be used by teachers to architect novel pathways through the curriculum for individual students or whole classes. Assessments will be created that can be delivered through a double-blind collaborative review (DBCR) rubric scoring tool, which will include machine learning algorithms that help detect consistent discrepancies between scores of individual (novice) computer science teachers and (expert) college-level computer science professors. The DBCR tool will use machine learning techniques such as basic text features (Bag of Words), syntactic and semantic modeling (n-grams and Latent Dirichlet Allocation), and cluster analysis against existing artifact corpora. Since the assessments of student artifacts are explicitly tied to rubrics, human evaluators may also use the DBCR tool to specify specific features that support line item rubric scores. The available features will vary from project to project but will leverage a variety of tools available in the DBCR user interface: (1) selectable categorical classifications, (2) the highlighting of relevant text features, and (3) annotations to mark document structures, such as cross references and supporting evidence. Project evaluation will focus on both implementation fidelity of the TODW course and student-level outcomes in classes that implement TODW. If successful, the professional development could serve as a model for more scalable teacher professional development in many domains.

培养能够支持高中生在严格的、学术的计算机科学课程中取得成功的计算机科学教师是国家的需要。项目参与：培训中学教师教授计算机科学和工程课程将支持在德克萨斯州的45所城市、郊区和农村学校(包括公立、私立、磁铁和特许学校)实施一门名为在我们的数字世界中蓬勃发展(TODW)的新兴计算机科学原理课程。TODW课程将以独特的双重招生模式提供(高中和大学学分并行)。该项目将测试两种创新的专业发展技术，以将覆盖范围扩大到更多的学校：翻转课堂技术，教师接受基于视频的专业发展录音，然后利用面对面的时间进行更多的动手活动；以及一个自动化系统，将使用人工智能技术支持高中教师和大学教师之间就学生作业的共同分数进行谈判。该项目的研究议程将解决以下核心挑战：在计算机科学这样一个专业知识有限的领域，我们如何才能最有效地扩展专业发展，以便尽可能多的学校教师能够提供高质量的教学？STEM-C(科学、技术、工程和数学，包括计算)伙伴关系计划支持STEM专家和K-12学校系统之间以研究为导向的伙伴关系，以实现制度变革，以更好地开展K-12水平的STEM教育。STEM-C合作伙伴关系的计算机科学教育扩展项目建立在UTeachEngineering：培训中学教师通过国家科学基金会的数学和科学合作伙伴计划提供以设计为基础的工程教学伙伴关系的先前资金的基础上。该项目将为TODW提供高度可扩展的课程所需的科学基础和具体的人工制品。这些成果将包括差异化的课程、可扩展的专业发展以及可扩展的评估工具和流程。不同的课程将包括形成性评估，教师可以利用这些评估为个别学生或整个班级设计新的课程路径。将创建可通过双盲协作审查(DBCR)评分工具提供的评估，该工具将包括机器学习算法，帮助检测个人(新手)计算机科学教师和(专家)大学级别计算机科学教授之间的一致差异。DBCR工具将使用机器学习技术，例如基本文本特征(词袋)、句法和语义建模(n-gram和潜在Dirichlet分配)以及针对现有人工语料库的聚类分析。由于学生人工制品的评估明确地与标准相关联，因此人工评估者还可以使用DBCR工具来指定支持行项目标准分数的特定功能。可用的功能因项目而异，但将利用DBCR用户界面中提供的各种工具：(1)可选择的分类，(2)相关文本特征的突出显示，以及(3)标记文档结构的注释，如交叉引用和佐证。项目评估将侧重于TODW课程的实施保真度和实施TODW的班级的学生水平的结果。如果成功，专业发展可以作为许多领域更具可伸缩性的教师专业发展的模式。