Using Learning Analytics to Understand Scientific Modeling in the Classroom

Frontiers in ICT

Ostwald, Jonathan¹  McNamara, Conor¹  Quigley, David²  Sumner, Tamara³ 

[1] Department of Computer Science, University of Colorado Boulder, United States;Institute for Cognitive Science, University of Colorado Boulder, United States;University Center for Atmospheric Research, United States

关键词: Learning analytics;    scientific modeling;    Sequential models;    Classification;    Instructional Equity;   

DOI  :  10.3389/fict.2017.00024

学科分类：计算机网络和通讯

来源: Frontiers

【 摘 要 】

Scientific models represent ideas, processes, and phenomena by describing important components, characteristics, and interactions. Models are constructed across a variety of scientific disciplines, such as the food web in biology, the water cycle in Earth science, or the structure of the solar system in astronomy. Models are central for scientists to understand phenomena, construct explanations, and communicate theories. Constructing and using models to explain scientific phenomena is also an essential practice in contemporary science classrooms. Our research explores new techniques for understanding scientific modeling and engagement with modeling practices. We work with students in secondary biology classrooms as they use a web-based software tool - EcoSurvey - to characterize organisms and their interrelationships found in their local ecosystem. We use learning analytics and machine learning techniques to answer the following questions: 1) How can we automatically measure the extent to which students’ scientific models support complete explanations of phenomena? 2) How does the design of student modeling tools influence the complexity and completeness of students’ models? 3) How do clickstreams reflect and differentiate student engagement with modeling practices? We analyzed EcoSurvey usage data collected from two different deployments with over 1000 secondary students across a large urban school district. We observe large variations in the completeness and complexity of student models, and large variations in their iterative refinement processes. These differences reveal that certain key model features are highly predictive of other aspects of the model. We also observe large differences in student modeling practices across different classrooms and teachers. We can predict a student’s teacher based on the observed modeling practices with a high degree of accuracy without significant tuning of the predictive model. These results highlight the value of this approach for extending our understanding of student engagement with scientific modeling, an important contemporary science practice, as well as the potential value of analytics for identifying critical differences in classroom implementation.

【 授权许可】

CC BY