Predictively Improving the Problem Solving of Science Students

预测性地提高理科学生解决问题的能力

• 批准号: 0231995
• 负责人: Ronald Stevens
• 金额: $ 148.76万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-01-01 至 2005-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0231995&HistoricalAwards=false
• 关键词: Predictively; Improving; Problem; Solving; Science

This proposed three-year study will investigate how undergraduate and high school students make and use strategic choices as they engage in complex problem solving in chemistry. The researchers will then use this to develop effective collaborative approaches for modifying unproductive strategies.IMMEX software will sequentially capture the students' actions while they perform a series of related chemistry cases, and extract common strategies using artificial neural network technologies. These strategies will be aggregated into strategy types using evidence of the quality of student understanding, related to student ability, by Item Response Theory analysis. These analyses will suggest directed case delivery sequences for audiences with different abilities and provide an organizing framework for linking ability, and preferred problem-solving approaches.Steady state models of the development and persistent usage of particular strategies and strategy types within classrooms of students with different abilities (i.e. regular high school, AP, undergraduate) will then be developed through Hidden Markov Modeling.Here, additional student performances are predicted to have little change on the distribution of strategies used previously within a particular classroom. These models will provide baseline probabilities that students will transit from one strategy type to another on a subsequent series of IMMEX cases. These models are baseline in the sense that no particular interventions will be suggested or tested.Collaborative learning activities will then be constructed around these models to perturb these steady states. The most refined interventions will use the Intelligent CollaborativeLearning System that will relate the interaction sequence details of student's communication behaviors/skills with the effectiveness of these activities in modifying strategic approaches.The research will be used to develop practical, yet effective classroom interventions that teachers can use in conjunction with IMMEX performance data to accelerate the acquisition of their students' problem-solving skills. While targeted to chemistry, the studies may be applicable to many scientific educational activities.

这项为期三年的研究将调查本科生和高中生在解决化学复杂问题时如何做出和使用战略选择。 然后，研究人员将利用这一点来开发有效的协作方法，以修改非生产性策略。IMMEX软件将顺序捕捉学生在执行一系列相关化学案例时的动作，并使用人工神经网络技术提取常见策略。这些策略将被聚合到策略类型使用的证据，学生的理解质量，学生的能力，项目反应理论分析。这些分析将为具有不同能力的受众提供有针对性的案例交付序列，并为连接能力和首选的问题解决方法提供组织框架。具有不同能力的学生在课堂上发展和持续使用特定策略和策略类型的稳态模型（即普通高中，AP，本科）然后将通过隐马尔可夫模型开发。在这里，预测额外的学生表现对先前在特定教室内使用的策略的分布几乎没有变化。这些模型将提供学生在随后的IMMEX案例系列中从一种策略类型过渡到另一种策略类型的基线概率。这些模型是基线，在这个意义上，没有特别的干预措施将被建议或测试。协作学习活动将围绕这些模型构建，以扰乱这些稳定的状态。最完善的干预措施将使用智能协作学习系统，该系统将学生的沟通行为/技能的互动序列细节与这些活动在修改策略方法方面的有效性联系起来。该研究将用于开发实用而有效的课堂干预措施，教师可以结合IMMEX表现数据使用，以加速学生解决问题技能的获得。虽然以化学为目标，但这些研究可能适用于许多科学教育活动。