Machine Learning-Enabled Automated Feedback: Supporting Students’ Revision of Scientific Arguments Based on Data Drawn from Simulation

A design study was conducted to test a machine learning (ML)-enabled automated feedback system developed to support students’ revision of scientific arguments using data from published sources and simulations. This paper focuses on three simulation-based scientific argumentation tasks called Trap, A...

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Bibliographic Details
Published in:Journal of science education and technology 2021-04, Vol.30 (2), p.168-192
Main Authors: Lee, Hee-Sun, Gweon, Gey-Hong, Lord, Trudi, Paessel, Noah, Pallant, Amy, Pryputniewicz, Sarah
Format: Article
Language:English
Subjects:
Analysis
Aquifers
Artificial Intelligence
Automation
Content Area Writing
Curricula
Education
Educational Technology
Feedback
Feedback (Response)
Groundwater
Learning algorithms
Machine learning
Natural Resources
Persuasive Discourse
Revision (Written Composition)
Revisions
Science Curriculum
Science Education
Sciences education
Scoring models
Secondary School Science
Secondary School Students
Secondary Schools
Simulation
Students
Technology application
Water
Water, Underground
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Summary:A design study was conducted to test a machine learning (ML)-enabled automated feedback system developed to support students’ revision of scientific arguments using data from published sources and simulations. This paper focuses on three simulation-based scientific argumentation tasks called Trap, Aquifer, and Supply. These tasks were part of an online science curriculum module addressing groundwater systems for secondary school students. ML was used to develop automated scoring models for students’ argumentation texts as well as to explore emerging patterns between students’ simulation interactions and argumentation scores. The study occurred as we were developing the first version of simulation feedback to augment the existing argument feedback . We studied two cohorts of students who used argument only (AO) feedback ( n  = 164) versus argument and simulation (AS) feedback ( n  = 179). We investigated how AO and AS students interacted with simulations and wrote and revised their scientific arguments before and after receiving their respective feedback. Overall, the same percentages of students (49% each) revised their arguments after feedback, and their revised arguments received significantly higher scores for both feedback conditions, p  
ISSN:1059-0145
1573-1839
DOI:10.1007/s10956-020-09889-7