Integrating Real-Time Drawing and Writing Diagnostic Models: An Evidence-Centered Design Framework for Multimodal Science Assessment

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• 关键词：Assessment ; Multimodalilty ; Evidence ; centered design
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9684
• 期：1
• 页码：165-175
• 全文大小：431 KB
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• 作者单位：Andy Smith (16)
  Osman Aksit (17)
  Wookhee Min (16)
  Eric Wiebe (17)
  Bradford W. Mott (16)
  James C. Lester (16)
  
  16. Department of Computer Science, North Carolina State University, Raleigh, NC, 27695, USA
  17. Department of STEM Education, North Carolina State University, Raleigh, NC, 27695, USA
  
• 丛书名：Intelligent Tutoring Systems
• ISBN：978-3-319-39583-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9684

文摘

Interactively modeling science phenomena enables students to develop rich conceptual understanding of science. While this understanding is often assessed through summative, multiple-choice instruments, science notebooks have been used extensively in elementary and secondary grades as a mechanism to promote and reveal reflection through both drawing and writing. Although each modality has been studied individually, obtaining a comprehensive view of a student’s conceptual understanding requires analyses of knowledge represented across both modalities. Evidence-centered design (ECD) provides a framework for diagnostic measurement of data collected from student interactions with complex learning environments. This work utilizes ECD to analyze a corpus of elementary student writings and drawings collected with a digital science notebook. First, a competency model representing the core concepts of each exercise, as well as the curricular unit as a whole, was constructed. Then, evidence models were created to map between student written and drawn artifacts and the shared competency model. Finally, the scores obtained using the evidence models were used to train a deep-learning based model for automated writing assessment, as well as to develop an automated drawing assessment model using topological abstraction. The findings reveal that ECD provides an expressive unified framework for multimodal assessment of science learning with accurate predictions of student learning.