GSE/RES Development of Gender-Based Science Performance Models

由加州大学洛杉矶分校领导的一个项目小组正在开发模型，记录从中学到大学的男性和女性在解决科学问题时如何发展和稳定策略。 他们正在建模：1）化学多个子领域中与性别相关的学习策略发展，2）解决化学问题的知识如何随着时间的推移而不同地保留，以及学习如何在需要时重新发生，以及，3）如何组织在线协作活动/环境，以最大限度地发挥不同问题的优势-解决男女学生的特点。智力优势虽然文献为各种STEM活动中的行为/认知性别差异提供了证据，关于这些差异如何有助于科学问题解决中的策略发展和使用的信息较少。该项目将使用人工神经网络和隐马尔可夫模型来研究学生在复杂科学问题解决任务中的表现和进展，以发展性别模型两个学区和两所大学承诺让数千名学生参加中学到大学的各个年级的活动，并参加由所有女性、所有男性和混合性别组成的准实验组。支持数据将包括内容知识，科学态度和教师和学生技术使用的前/后测试，以及包括国家标准化测试数据在内的整体学术表现。该项目小组包括加州的研究人员、教育工作者和学生（加州大学洛杉矶分校，胎盘-约巴琳达联合学区），南卡罗来纳州（克莱姆森），肯塔基州，和意大利（IRST），一个心理计量学家在性别研究和项目反应理论（ETS）的专业知识经验，并从性别研究界的顾问成员。调查结果将传播给教师，性别研究界，基础科学教育工作者，以及智能辅导和协作学习社区的成员。解决问题的任务将与由此产生的神经网络和马尔可夫进程和绩效模型一起沿着在线提供给世界各地的其他教师和研究人员。更广泛的影响长期目标是利用这些信息帮助机构系统更好地了解性别差异如何可能影响男性和女性在复杂的STEM学习中的表现和参与。测试环境，并为教师提供如何最大限度地提高所有学生的学习指导方针。所开发的模型的功能可能会通知6-16年级的教学实践。 虽然研究重点是化学内容，建模方法适用于其他科学领域。

A project team led by University of California Los Angeles is developing models documenting how males and females from middle school to the university develop and stabilize strategies during scientific problem solving. They are modeling: 1) The gender-related development of learning strategies in multiple sub-domains of chemistry, 2) How knowledge of solving problems in chemistry is differentially retained over time, and how learning re-occurs when needed, and, 3) How online collaborative activities / environments can be organized to maximize the strengths of the different problem-solving characteristics of female and male students.Intellectual MeritWhile the literature provides evidence for behavioral/cognitive gender differences in various STEM activities, there is less information on how these differences contribute to strategy development and use in scientific problem solving.The project will use artificial neural network and Hidden Markov Modeling of student performance and progress on complex science problem solving tasks to develop the gender models. There are commitments from two school districts and two universities to involve thousands of students across grade levels spanning middle school through university and in quasi-experimental groups with all female, all male and mixed gender groupings. Supporting data will include pre/post tests of content knowledge, science attitudes and teacher and student technology use, as well as overall academic performance including state standardized test data. The project team includes researchers, educators and students in California (UCLA, Placentia-Yorba Linda Unified School District), South Carolina (Clemson), Kentucky, and Italy (IRST), a psycho-metrician with experience in gender research and expertise in item response theory (ETS), and advisory members from the gender research community.Findings will be disseminated to teachers, the gender research community, basic science educators, and members of the intelligent tutoring and collaborative learning communities. The problem solving tasks will be available online to other teachers and researchers world wide along with the resulting neural network and Markov progress and performance models.Broader ImpactsThe long-term goal is to use this information to help institutional systems better understand how gender differences are likely to influence performance and participation of males and females in complex STEM learning/testing environments and to provide teacher guidelines on how to maximize learning for all students. The features of the models that are developed will potentially inform instructional practices from grades 6-16. While the study focuses on chemistry content, the modeling approach is applicable to other science domains.