Trans-modal Analysis: A Mathematical and Computational Framework for Equity Assessment of Multi-modal STEM Learning Processes

跨模态分析：多模态 STEM 学习过程公平评估的数学和计算框架

• 批准号: 2201723
• 负责人: David Shaffer
• 金额: $ 250万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-15 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2201723&HistoricalAwards=false
• 关键词: Trans; modal; Analysis; Mathematical; Computational

In modern classrooms, students learn science, technology, engineering, and mathematics (STEM) through interactions not only with teachers and textbooks but also with computer games and simulations, automated tutors, and online resources. Education researchers thus have access to large amounts of data about students’ STEM learning processes, from classroom or online conversations to detailed records of student activity in educational apps. Despite the potential of such rich data for curriculum development and personalized assessment, there are significant technical and conceptual challenges to analyzing data that come from different sources or modalities. To address these challenges, this project will develop and test trans-modal analysis (TMA). TMA is a statistical technique and software package that will help researchers better and more easily integrate multiple types of data into analyses of STEM learning. This will enable more accurate understanding of students’ STEM learning processes, and in turn help identify potential inequities in assessment of student learning, informing education policy and practice for diverse learners. This project will include post-doctoral scholars, graduate student researchers, and undergraduate research interns, who will develop skills and experience in data science, learning analytics, software development, and scientific communication, providing training and mentoring for the next generation of education researchers. Although most analyses of learning processes are based on a single type or modality of data, STEM learning typically takes place in a multimodal setting. Models of STEM learning processes thus need to account for multiple sources and types of data to account for complex interactions between learners and the setting(s) in which they learn. For example, there are different types of events (questions from a teacher, chats with a peer, views of a resource) and different properties of events (gender of a person gesturing, linguistic fluency of a speaker, reading level of a person reading a document) that may influence future events with more or less impact over time. In addition, the structure of a learning environment creates a horizon of observation for each student, making some events (e.g., a conversation in another group of students) more or less visible. Finally, different characteristics of students (age, cultural or ethnic background, gender identification, whether instruction is in their native language or a non-native language) may lead them to respond to events in different ways. Extant learning analytic techniques account for the influence of prior events by lagging: for example, using some fixed number of prior events to predict future events. TMA will enable those same techniques to operate not on properties of the events themselves but on underlying functions that represent claims or hypotheses about the interaction between different learning modalities, the structure of the learning environment, and the ways in which students might systematically differ as STEM learners. The project team hypothesizes that TMA models will provide a more nuanced, more accurate, and more equitable view of STEM learning processes for diverse learners. This approach will expand the understanding of effective multi-modal STEM learning processes and allow researchers to account for diversity and address questions of equity in multi-modal STEM learning. TMA will be developed and tested first as a set of algorithms for conducting trans-modal analyses with three widely used learning analytic tools: process mining, epistemic network analysis, and dynamic Bayesian networks. The investigators aim to use simulation studies and the analysis of actual STEM learning datasets to address two fundamental research questions regarding the science of learning: (1) Under what conditions (if any) are trans-modal models of STEM learning processes more informative than uni-modal models? And (2) Can TMA model meaningful differences in trans-modal learning processes for minoritized groups of STEM learners?This project is supported by NSF's EHR Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. The program supports the accumulation of robust evidence to inform efforts to understand, build theory to explain, and suggest intervention and innovations to address persistent challenges in education.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.

在现代教室中，学生不仅通过与教师和教科书，计算机游戏和仿真，自动化导师以及在线资源进行互动，学习科学，技术，工程和数学（STEM）。因此，教育研究人员可以访问有关学生STEM学习过程的大量数据，从课堂或在线对话到教育应用程序中学生活动的详细记录。尽管如此丰富的数据有潜力用于课程开发和个性化评估，但分析来自不同来源或模式的数据仍存在重大的技术和概念挑战。为了应对这些挑战，该项目将开发和测试跨模式分析（TMA）。 TMA是一种统计技术和软件包，将帮助研究人员更好，更容易地将多种类型的数据整合到STEM学习分析中。这将使对学生的STEM学习过程有更准确的理解，进而有助于确定学生学习评估的潜在不平等，为潜水员学习者提供教育政策和实践。该项目将包括博士后学者，研究生研究人员和本科研究实习生，他们将在数据科学，学习分析，软件开发和科学沟通方面发展技能和经验，为下一代教育提供培训和心理，尽管大多数学习过程分析基于数据或基于数据的单一类型或模式，STEM学习，STEM学习典型地用于多层设置。因此，STEM学习过程的模型需要考虑多种来源和数据类型，以说明学习者与学习的设置之间的复杂交互。例如，有不同类型的事件（老师的问题，与同伴聊天，资源的观点）和事件的不同属性（一个人的性别手势，说话者的语言流利度，阅读文档的阅读水平）可能会影响未来事件，这些事件或更少会随着时间的推移而产生或更少的影响。此外，学习环境的结构为每个学生创造了观察的视野，使一些事件（例如，在另一组学生中的对话中）或多或少可见。最后，学生的不同特征（年龄，文化或种族背景，性别识别，教学是用母语还是非本地语言）可能会导致他们以不同的方式对事件做出回应。现有的学习分析技术解释了先前事件的影响：例如，使用一些固定数量的先前事件来预测未来事件。 TMA将使这些相同的技术不在事件本身的属性上，而是在代表主张或假设不同学习方式之间相互作用的主张或假设的基本功能上运作，而学习环境的结构以及学生可能会系统地差异作为STEM学习者。项目团队假设TMA模型将为多样性学习者提供更细微的，更准确，更公平的STEM学习过程的看法。这种方法将扩大对有效多模式STEM学习过程的理解，并使研究人员能够考虑多样性的多样性，并解决多模式STEM学习中的公平问题。将首先开发和测试TMA作为一组算法，用于使用三种广泛使用的学习分析工具进行跨模式分析：过程挖掘，认知网络分析和动态贝叶斯网络。研究人员的目的是使用模拟研究和对实际STEM学习数据集的分析来解决有关学习科学的两个基本研究问题：（1）在哪些条件（如果有的话）下是STEM学习过程的跨模型模型，而不是Uni-Modal模型？ （2）TMA可以模拟少数STEM学习者群体跨模式学习过程中有意义的差异吗？该项目得到NSF的EHR核心研究（ECR）计划的支持。 ECR计划强调了基本的STEM教育研究，该研究在该领域产生了基础知识。投资是在重要，广泛和结束的关键领域进行的：STEM学习和STEM学习环境，扩大参与STEM以及STEM劳动力的发展。该计划支持积累强大的证据，以告知努力，以理解，建立理论来解释理论，并提出干预和创新，以应对教育中的持续挑战。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。

项目成果

The role of data simulation in quatitative ethnography

数据模拟在定量民族志中的作用

• 发表时间: 2022
• 期刊: ICQE 2022
• 作者: Swiecki, Z. &

Ordered network analysis

有序网络分析

• 发表时间: 2023
• 期刊: ICQE 2022
• 作者: Tan, Y.;Ruis, A.R.;Marquart, C.L.;Cai, Z.;Knowles, M.;Shaffer, D.W.
• 通讯作者: Shaffer, D.W.

An Epistemic Network Analysis of Patient Decision-Making Regarding Choice of Therapy

• DOI: 10.31234/osf.io/azrym
• 发表时间: 2021-01-01
• 期刊: Quality and Quantity-International Journal of Methodology
• 作者: Zorgo, S.;Peters, G.-J.;Ruis, A.
• 通讯作者: Ruis, A.

Mediating and perspective-taking manipulatives: Fostering dynamic perspective-taking by mediating dialogic thinking and bolstering empathy in role-play and reflection for microteaching

调解和观点采择操作：通过调解对话思维并增强角色扮演和微格教学反思中的同理心来培养动态的观点采择

• 发表时间: 2022
• 期刊: International journal of computersupported collaborative learning
• 作者: Mochizuki, T.

Using multi-modal network models to visualize and understand how players learn a mechanic in a problem-solving game

使用多模态网络模型可视化并了解玩家如何在解决问题的游戏中学习机制

• 发表时间: 2023
• 期刊: The Thirteenth International Conference on Learning Analytics & Knowledge
• 作者: Caprenter, Z.