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Revolutionizing Education with Digital InkLeveraging Trends in Student Interaction to Enhance the Effectiveness of Sketch-Based Educational Software

Revolutionizing Education with Digital Ink: Leveraging Trends in Student Interaction to Enhance... [With the rapid adoption of software-based learning in classrooms, it is increasingly important to design more intelligent educational software, a goal of the emerging field of educational data mining. In this work, we analyze student activities from using a learning tool for engineers, Mechanix, in order to find trends that may be used to make the software a better tutor, combining its natural, sketch-based input with intelligent, experience-based feedback. We see a significant correlation between student performance and the amount of time they work on a problem before submitting; students who attempt to “game” the system by submitting their results too often perform worse than those who work longer (p< 0.05). We also found significance in the number of times a student attempted a problem before moving on, with a strong correlation between being willing to switch among problems and better performance (p< 0.05). Overall, we find that student trends like these could be paired with machine learning techniques to make more intelligent educational tools.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Revolutionizing Education with Digital InkLeveraging Trends in Student Interaction to Enhance the Effectiveness of Sketch-Based Educational Software

Part of the Human–Computer Interaction Series Book Series
Editors: Hammond, Tracy; Valentine, Stephanie; Adler, Aaron
Polsley, Seth; Ray, Jaideep; Nelligan, Trevor; Helms, Michael; Linsey, Julie; Hammond, Tracy
Revolutionizing Education with Digital Ink — May 19, 2016
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Publisher
Springer International Publishing
Copyright
© Springer International Publishing Switzerland 2016
ISBN
978-3-319-31191-3
Pages
103 –114
DOI
10.1007/978-3-319-31193-7_7
Publisher site
See Chapter on Publisher Site

Abstract

[With the rapid adoption of software-based learning in classrooms, it is increasingly important to design more intelligent educational software, a goal of the emerging field of educational data mining. In this work, we analyze student activities from using a learning tool for engineers, Mechanix, in order to find trends that may be used to make the software a better tutor, combining its natural, sketch-based input with intelligent, experience-based feedback. We see a significant correlation between student performance and the amount of time they work on a problem before submitting; students who attempt to “game” the system by submitting their results too often perform worse than those who work longer (p< 0.05). We also found significance in the number of times a student attempted a problem before moving on, with a strong correlation between being willing to switch among problems and better performance (p< 0.05). Overall, we find that student trends like these could be paired with machine learning techniques to make more intelligent educational tools.]

Published: May 19, 2016

Keywords: Intelligent Educational Software; Mechanix; Educational Data Mining (EDM); Sketch Recognition; Homework Completion Rates

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