CLASS: Continuous Learning and Automated Scoring in Science

课程：科学中的持续学习和自动评分

• 批准号: 1119670
• 负责人: Marcia Linn
• 金额: $ 314.77万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1119670&HistoricalAwards=false
• 关键词: CLASS; Continuous; Learning; Automated; Scoring

This five-year project investigates how to provide continuous assessment and feedback to guide students' understanding during science inquiry-learning experiences, as well as detailed guidance to teachers and administrators through a technology-enhanced system. The assessment system integrates validated automated scorings for students' written responses to open-ended assessment items (i.e., short essays, science narratives, concept mapping, graphing problems, and virtual experiments) into the "Web-based Inquiry Science Environment" (WISE) program. WISE is an online science-inquiry curricula that supports deep understanding through visualization of processes not directly observable, virtual experiments, graphing results, collaboration, and response to prompts for explanations. In partnership with Educational Testing Services (ETS), project goals are: (1) to develop five automated inquiry assessment activities that capture students' abilities to integrate their ideas and form coherent scientific arguments; (2) to customize WISE by incorporating automated scores; (3) to investigate how students' systematic feedback based on these scores improve their learning outcomes; and (4) to design professional development resources to help teachers use scores to improve classroom instruction, and administrators to make better informed decisions about teacher professional development and inquiry instruction. The project targets general science (life, physical, and earth) in three northern California school districts, five middle schools serving over 4,000 6th-8th grade students with diverse cultural and linguistic backgrounds, and 29 science teachers. It contributes to increase opportunities for students to improve their science achievement, and for teachers and administrators to make efficient, evidence-based decisions about high-quality teaching and learning. A key research question guides this effort: How automated scoring of inquiry assessments can increase success for diverse students, improve teachers' instructional practices, and inform administrators' decisions about professional development, inquiry instruction, and assessment? To develop science inquiry assessment activities, scoring written responses include semantic, syntax, and structure of meaning analyses, as well as calibration of human-scored items with a computer-scoring system through the c-rater--an ETS-developed cyber learning technology. Validity studies are conducted to compare automated scores with human-scored items, teacher, district, and state scores, including sensitivity to the diverse student population. To customize the WISE curriculum, the project modifies 12 existing units and develops nine new modules. To design adaptive feedback to students, comparative studies explore options for adaptive guidance and test alternatives based on automated scores employing linear models to compare student performance across randomly assigned guidance conditions; controlling for covariates, such as prior science scores, gender, and language; and grouping comparison studies. To design teacher professional development, synthesis reports on auto-scored data are created to enable them to use evidence to guide curricular decisions, and comments' analysis to improve feedback quality. Workshops, classroom observations, and interviews are conducted to measure longitudinal teachers' change over time. To empower administrators' decision making, special data reports, using-evidence activities, individual interviews, and observation of administrators' meetings are conducted. An advisory board charged with project evaluation addresses both formative and summative aspects.A research-informed model to improve science teaching and learning at the middle school level through cyber-enabled assessment is the main outcome of this effort. A total of 21 new, one- to three-week duration standards-based science units, each with four or more automatically scored items, serve as prototypes to improve students' performance, teachers' instructional approaches, and administrators' school policies and practices.

这个五年的项目调查了如何提供持续的评估和反馈，以指导学生在科学探究学习经验中的理解，以及通过技术增强的系统向教师和管理人员提供详细的指导。评估系统将经过验证的自动评分集成到学生对开放式评估项目的书面回答（即，简短的论文，科学叙事，概念映射，图形问题和虚拟实验）中的书面回答纳入“基于Web的询问科学环境”（明智）计划。 Wise是一项在线科学渠道课程，通过可视化不直接观察到的流程，虚拟实验，图形结果，协作和对解释提示的响应的过程来支持深入了解。与教育测试服务（ETS）合作，项目目标是：（1）开发五项自动调查评估活动，以捕捉学生的能力整合思想并形成连贯的科学论点； （2）通过合并自动分数来自定义明智； （3）研究基于这些分数的学生的系统反馈如何改善他们的学习成果； （4）设计专业发展资源，以帮助教师使用分数来改善课堂教学，而管理员为教师专业发展和询问指导做出更好的知识决定。该项目针对三个北加州学区的一般科学（生命，物理和地球），五所中学为4,000多名6至8年级的学生提供各种文化和语言背景的学生，以及29位科学教师。它有助于增加学生改善科学成就的机会，并使教师和管理人员对高质量的教学和学习做出有效的，基于证据的决定。一个关键的研究问题指出了这一努力：调查评估的自动评分如何增加不同学生的成功，改善教师的教学实践，并告知管理人员关于专业发展，询问指导和评估的决定？为了开发科学探究评估活动，书面回答包括语义，语法和含义分析的结构，以及通过C-Rater（通过C-Rater-ETS开发的网络学习技术）使用计算机得分系统对人数的项目进行校准。进行有效性研究是为了将自动分数与人尺寸的项目，教师，地区和州分数进行比较，包括对多样化学生群体的敏感性。为了自定义明智的课程，该项目修改了12个现有单元并开发了9个新模块。为了向学生设计自适应反馈，比较研究探讨了基于自动分数的自适应指导和测试替代方案的选择，该分数采用线性模型来比较随机分配的指导条件的学生表现；控制协变量，例如先前的科学分数，性别和语言；和分组比较研究。为了设计教师专业发展，创建了有关自动评分数据的综合报告，以使他们能够使用证据指导课程决策，并评论分析以提高反馈质量。进行讲习班，课堂观察和访谈是为了衡量纵向教师的变化。为了授权管理员的决策，进行了特殊的数据报告，使用证据活动，个人访谈以及对管理员会议的观察。负责项目评估的咨询委员会涉及形成性和总结性方面。通过支持网络支持的评估，一种研究知识的模型，以改善中学的科学教学和学习，这是这项工作的主要结果。共有21个新的，一到三周的基于标准标准的科学单元，每个科学单元都有四个或更多的自动评分项目，可作为改善学生表现，教师的教学方法以及管理员的学校政策和实践的原型。