1 citations · 1 across the 1 of their papers we have counts for
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Using Machine Learning to Identify the Most At-Risk Students in Physics Classes
Jie Yang, Seth DeVore, Dona Hewagallage +3
Machine learning algorithms have recently been used to predict students' performance in an introductory physics class. The prediction model classified students as those likely to r…
Extending Machine Learning to Predict Unbalanced Physics Course Outcomes
Seth DeVore, Jie Yang, John Stewart
Machine learning algorithms have recently been used to classify students as those likely to receive an A or B or students likely to receive a C, D, or F in a physics class. The per…
Extending Modified Module Analysis to Include Correct Responses: An Analysis of the Force Concept Inventory
Jie Yang, James Wells, Rachel Henderson +3
Brewe, Bruun, and Bearden first applied network analysis to understand patterns of incorrect conceptual physics reasoning in multiple-choice instruments introducing the Module Anal…
Exploring the Structure of Misconceptions in the Force Concept Inventory with Modified Module Analysis
James Wells, Rachel Henderson, John Stewart +3
Module Analysis for Multiple-Choice Responses (MAMCR) was applied to a large sample of Force Concept Inventory (FCI) pretest and post-test responses ( and $N_{post}=4…