Supporting Student Planning with Open Learner Models in Middle Grades Science

通过中年级科学的开放学习者模型支持学生规划

• 批准号: 1761178
• 负责人: James Lester
• 金额: $ 149.92万
• 依托单位: North Carolina State University
• 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: 2018-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1761178&HistoricalAwards=false
• 关键词: Supporting; Student; Planning; Open; Learner

This project is supported by the Education and Human Resources Core Research (ECR) program, which supports fundamental research in STEM learning and learning environments. Self-regulated learning requires learners to set learning goals, plan how to achieve them, monitor the success of their plans, and make needed changes. Critical gaps exist in our understanding of how and to what extent middle school students can engage in self-regulated learning during science inquiry activities. Analogous gaps exist in our understanding of how to help students become self-regulated learners. Computer-based learning tools might help fill these gaps, but these tools are usually "closed," meaning that how the software interprets students' knowledge and progress is hidden from the student. In contrast, "open" learning tools provide students with understandable, visual representations of their knowledge and progress. Such "open learner models" may be useful for helping students develop self-regulated learning skills. For example, by providing easy-to-understand representations of student progress, open learner models may support the self-regulated learning processes of goal setting and planning. This project focuses on the design, development, and investigation of open learner models for student goal setting and planning in middle school science. The project will explore how students engage in goal-directed learning behaviors using data from eye tracking, log files, and think-aloud exercises. It is anticipated that the project results can advance the goal of high quality STEM learning experiences for all students. A major project goal is improving students' problem-solving abilities and learning outcomes through a theoretically grounded, data-driven open learner model. The project is designed to integrate an open learner model into the Future Worlds science learning environment for middle school ecosystems education. A culminating between-subjects experiment will be conducted to compare a baseline version of Future Worlds that does not have an open learner model (control condition) with a version of Future Worlds that has an embedded open learner model (experimental condition). The project will use state-of-the-art artificial intelligence computational frameworks to recognize students' goals and plans from observations of their problem-solving activities in an online learning environment. It is hypothesized that the open learner model will yield better student learning outcomes, with improved science problem-solving skills, increased science content knowledge, increased metacognitive awareness, and enhanced science self-efficacy. The project is designed to make significant contributions to both theory and practice of self-regulated learning. The project's aim is to make contributions to foundational knowledge and theory by advancing our understanding of how to improve science learning with open learner models: (1) With a focus on student goal setting and planning in technology-rich learning environments, the project aims to formulate an empirically-based theoretical framework for open learner model-enhanced learning that addresses both cognitive and metacognitive components of middle grades science education. (2) By conceptualizing goal setting and planning to account for self-regulated learning in the context of science problem solving, the project is designed to create a rich framework that connects goal setting and planning to students' problem solving and metacognitive processes. (3) By expanding to fine-grained process data, the project is intended to make methodological contributions that will enable the field to go beyond self-report measures, which have long dominated research on self-regulated learning. (4) The project plans to produce learning analytic techniques that yield predictive models of goal setting and planning in science problem solving, as well as open learner models to effectively support student goal setting and problem solving.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.

该项目由教育和人力资源核心研究（ECR）项目支持，该项目支持STEM学习和学习环境的基础研究。自我调节学习要求学习者设定学习目标，计划如何实现这些目标，监督计划的成功，并做出必要的改变。我们对中学生在科学探究活动中如何进行自主学习以及在多大程度上进行自主学习的理解存在着严重的空白。我们对如何帮助学生成为自我调节的学习者的理解也存在类似的差距。基于计算机的学习工具可能有助于填补这些空白，但这些工具通常是“封闭的”，这意味着软件如何解释学生的知识和进步是对学生隐藏的。相比之下，“开放式”学习工具为学生提供了可理解的、可视化的知识和进步表现形式。这种“开放式学习模式”可能有助于帮助学生培养自我调节的学习技能。例如，通过提供易于理解的学生进度表示，开放式学习者模型可以支持目标设定和计划的自我调节学习过程。本课题的重点是设计、开发和调查中学科学课程中学生目标设定和计划的开放式学习者模型。该项目将利用眼动追踪、日志文件和有声思考练习的数据，探索学生如何参与目标导向的学习行为。预计项目成果可以推进为所有学生提供高质量STEM学习体验的目标。一个主要的项目目标是通过一个理论基础的、数据驱动的开放式学习者模型来提高学生解决问题的能力和学习成果。该项目旨在将开放式学习者模式融入中学生态系统教育的未来世界科学学习环境中。最后将进行受试者之间的实验，以比较没有开放学习者模型（控制条件）的基线版本和具有嵌入式开放学习者模型（实验条件）的未来世界版本。该项目将使用最先进的人工智能计算框架，通过观察学生在在线学习环境中解决问题的活动来识别学生的目标和计划。假设开放式学习者模式能提高学生解决科学问题的能力，增加科学内容知识，提高元认知意识，增强科学自我效能感，从而获得更好的学习效果。该项目旨在为自主学习的理论和实践做出重大贡献。该项目的目的是通过促进我们对如何利用开放式学习者模型改善科学学习的理解，为基础知识和理论做出贡献：(1)重点关注学生在技术丰富的学习环境中的目标设定和规划，该项目旨在为开放式学习者模型增强学习制定一个基于经验的理论框架，解决中学科学教育的认知和元认知组成部分。(2)通过将目标设定和计划概念化，以解释科学问题解决背景下的自我调节学习，该项目旨在创建一个丰富的框架，将目标设定和计划与学生的问题解决和元认知过程联系起来。(3)通过扩展到细粒度的过程数据，该项目旨在做出方法论上的贡献，使该领域能够超越自我报告测量，后者长期以来主导着自我调节学习的研究。(4)本项目计划建立学习分析技术，产生科学问题解决中目标设定和规划的预测模型，以及有效支持学生目标设定和问题解决的开放式学习者模型。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

Autonomy and types of informational text presentations in game-based learning environments

基于游戏的学习环境中信息文本呈现的自主性和类型

• 发表时间: 2019
• 期刊: Proceedings of the Twentieth International Conference on Artificial Intelligence in Education
• 作者: Dever, D. A.;Azevedo, R.
• 通讯作者: Azevedo, R.

Leveraging Student Goal Setting for Real-Time Plan Recognition in Game-Based Learning

在基于游戏的学习中利用学生目标设定进行实时计划识别

• DOI: 10.1007/978-3-031-11644-5_7
• 发表时间: 2022
• 期刊: Proceedings of the 23rd International Conference on Artificial Intelligence in Education
• 作者: Goslen, A.;Carpenter, D.;Rowe, J.;Henderson, N.;Azevedo, R.;Lester, J.
• 通讯作者: Lester, J.

Examining Gaze Behaviors and Metacognitive Judgments of Informational Text Within Game-Based Learning Environments

在基于游戏的学习环境中检查信息文本的注视行为和元认知判断

• 发表时间: 2019
• 期刊: International Conference on Artificial Intelligence in Education
• 作者: Dever, Daryn;Azevedo, Roger
• 通讯作者: Azevedo, Roger

“The role of achievement goal orientation on metacognitive processes in game-based learning.”

– 成就目标导向对基于游戏的学习中元认知过程的作用。 –

• 发表时间: 2019
• 期刊: International Conference on Artificial Intelligence in Education
• 作者: Cloude, Elizabeth;Taub, Michelle;Lester, James;Azevedo, Roger
• 通讯作者: Azevedo, Roger

Language Modeling for Plan Generation in Game-Based Learning Environments

基于游戏的学习环境中计划生成的语言建模

• 发表时间: 2023
• 期刊: AIED’23 : Empowering Education with LLMs - the Next-Gen Interface and Content Generation
• 作者: Goslen, A.;Kim, Y.;Rowe, J.;Lester, J.
• 通讯作者: Lester, J.