Ausgabe 2/2021
Special Issue on Applying Machine Learning in Science Assessment
Inhalt (13 Artikel)
Open Access
Comparison of Machine Learning Performance Using Analytic and Holistic Coding Approaches Across Constructed Response Assessments Aligned to a Science Learning Progression
Lauren N. Jescovitch, Emily E. Scott, Jack A. Cerchiara, John Merrill, Mark Urban-Lurain, Jennifer H. Doherty, Kevin C. Haudek
Machine Learning-Enabled Automated Feedback: Supporting Students’ Revision of Scientific Arguments Based on Data Drawn from Simulation
Hee-Sun Lee, Gey-Hong Gweon, Trudi Lord, Noah Paessel, Amy Pallant, Sarah Pryputniewicz
Testing the Impact of Novel Assessment Sources and Machine Learning Methods on Predictive Outcome Modeling in Undergraduate Biology
Roberto Bertolini, Stephen J. Finch, Ross H. Nehm
How Does Augmented Observation Facilitate Multimodal Representational Thinking? Applying Deep Learning to Decode Complex Student Construct
Shannon H. Sung, Chenglu Li, Guanhua Chen, Xudong Huang, Charles Xie, Joyce Massicotte, Ji Shen
Relationships between Facial Expressions, Prior Knowledge, and Multiple Representations: a Case of Conceptual Change for Kinematics Instruction
Hongming Liaw, Yuh-Ru Yu, Chin-Cheng Chou, Mei-Hung Chiu
Using Machine Learning to Score Multi-Dimensional Assessments of Chemistry and Physics
Sarah Maestrales, Xiaoming Zhai, Israel Touitou, Quinton Baker, Barbara Schneider, Joseph Krajcik
Combining Machine Learning and Qualitative Methods to Elaborate Students’ Ideas About the Generality of their Model-Based Explanations
Joshua M. Rosenberg, Christina Krist
Correction
Correction to: Combining Machine Learning and Qualitative Methods to Elaborate Students’ Ideas About the Generality of their Model-Based Explanations
Joshua M. Rosenberg, Christina Krist
Automated Scoring of Chinese Grades 7–9 Students’ Competence in Interpreting and Arguing from Evidence
Cong Wang, Xiufeng Liu, Lei Wang, Ying Sun, Hongyan Zhang
Computational Modeling of the Effects of the Science Writing Heuristic on Student Critical Thinking in Science Using Machine Learning
Richard Lamb, Brian Hand, Amanda Kavner
On the Validity of Machine Learning-based Next Generation Science Assessments: A Validity Inferential Network
Xiaoming Zhai, Joseph Krajcik, James W. Pellegrino