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Task-Analysis-Guided Deliberate Practice for Learning Free-Body Diagrams

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

The ABCs of FBDs

Tagged Division

Mechanics

Page Count

9

Permanent URL

https://peer.asee.org/37803

Download Count

30

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Paper Authors

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Yan Tang Embry-Riddle Aeronautical University-Daytona Beach Orcid 16x16 orcid.org/0000-0002-9089-5746

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Dr. Yan Tang is an associate professor of mechanical engineering at Embry-Riddle Aeronautical University in Daytona Beach, Fla. Her current research in engineering education focuses on cognitive load theory, deliberate practice, and effective pedagogical practices. Her background is in dynamics and controls.

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biography

Haiyan Bai University of Central Florida

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Haiyan Bai, PhD., is Professor of Quantitative Research Methodology in the College of Education and Human Performance at the University of Central Florida. Her interests include resampling method, propensity score analysis, research design, measurement and evaluation, and the applications of statistical methods in educational research and behavioral sciences. She is actively involved educational and social science research projects. Dr. Bai has published books and many professional articles in refereed national and international journals. She has won several competitive awards at the University of Central Florida for her excellent teaching and research. Dr. Bai also served on several professional journal editorial boards, such as Journal of Experimental Education, Frontiers in Quantitative Psychology and Measurement, and Journal of Data Analysis and Information Processing. She is also the Fellow of the Academy for Teaching, Learning, and Leadership and the Faculty Fellow at The University of Central Florida.

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Richard Catrambone Georgia Institute of Technology Orcid 16x16 orcid.org/0000-0002-7334-7406

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Richard Catrambone is a Professor in the School of Psychology at the Georgia Institute of Technology. He received his B.A. from Grinnell College and his Ph.D. in Experimental Psychology from the University of Michigan.

The question Catrambone likes to ask--and the thread that runs through the projects he does alone and in collaboration with others--is: What does someone need to know in order to solve novel problems or carry out tasks within a particular domain?

Catrambone’s research interests include problem solving, educational technology, and human-computer interaction. He is particularly interested in how people learn from examples in order to solve problems in domains such as algebra, probability, and physics. He explores how to create instructional materials that help learners understand how to approach problems in a meaningful way rather than simply memorizing a set of steps that cannot easily be transferred to novel problems. He researches the design of teaching and training materials--including software and multimedia environments--based on cognitive principles that help students learn basic tasks quickly and promote transfer to novel problems. He uses task analysis to identify what someone needs to know in order to solve problems or carry out tasks in a domain and then to use the results of the task analysis to guide the construction of teaching and training materials/environments.

Catrambone has served on the Cognitive Science Society governing board from 2011-2016 and was chair of the Society in 2015. He was co-chair of the Cognitive Science Conference in 2010. He has served as a consulting editor for the Journal of Educational Psychology (1/2008 - 12/2011), the Journal of Experimental Psychology: Learning, Memory, and Cognition (6/2000 - 12/2001 and 1/2009 - 12/2009), the Journal of Experimental Psychology: Applied (1/2001 - 12/2007), and the Journal of Experimental Psychology: General (6/2000 - 12/2001). He has published his research in journals such as the Journal of Experimental Psychology: General; Journal of Experimental Psychology: Learning, Memory, and Cognition; Journal of Experimental Psychology: Applied; Memory & Cognition; Journal of Educational Psychology; Human-Computer Interaction; Human Factors; and other basic and applied journals. He has also served on grant review panels for a variety of funding agencies including the National Science Foundation and the Institute of Education Sciences (U.S. Department of Education).

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Abstract

Free-body diagrams (FBDs) are diagrammatic representations of external forces and moments exerted on an object of interest for solving kinetics problems. Several studies have reported different ways of teaching FBDs in terms of pictorial representation of forces (e.g., placement of vectors or labeling). However, there is little research on practice strategies for helping students learn how to draw FBDs. Through the use of task analysis and a model of subgoal learning, we will develop task-analysis-guided deliberate practice to enhance learning.

Task analysis is often used in instructional design to extract knowledge requirements for acquiring a skill. Skill acquisition is usually divided into three phases including declarative, knowledge compilation, and procedural. Task analysis in our study will identify relevant declarative and procedural knowledge related to drawing FBDs. The findings will be used to develop deliberate practice. Deliberate practice can help novices develop good representations of the knowledge needed to produce superior problem solving performance. This has been viewed as a gold standard for practice. Although deliberate practice is mainly studied among elite performers, the recent literature has revealed promising results for novices. Considering cognitive capacity limitations, we will apply cognitive load theory to develop deliberate practice to help students build declarative and procedural knowledge without exceeding their working memory limitations.

A knowledge extraction expert will take an iterative approach to conduct task analyses with a subject matter expert (or experts)to distill knowledge to a level that is appropriate for students in the dynamics course. We will then integrate the task analysis results with instructional design strategies derived from cognitive load theory and the subgoal learning model to develop deliberate practice and assessment materials. Examples and assessment results will be provided to evaluate the effectiveness of the instructional design strategies as well as the challenges.

Tang, Y., & Bai, H., & Catrambone, R. (2021, July), Task-Analysis-Guided Deliberate Practice for Learning Free-Body Diagrams Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37803

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