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Comparison of UBCSAND and Hypoplastic soil model predictions using the Material Point Method

, , , and . Soils and Foundations, 60 (4): 989-1000 (2020)
DOI: 10.1016/j.sandf.2020.06.001

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Comparison of UBCSAND and Hypoplastic soil model predictions using the Material Point Method, , , and . Soils and Foundations, 60 (4): 989-1000 (2020)Content Learning Analysis Using the Moment-by-Moment Learning Detector., , and . ITS, volume 7315 of Lecture Notes in Computer Science, page 434-443. Springer, (2012)Towards Predicting Future Transfer of Learning., , and . AIED, volume 6738 of Lecture Notes in Computer Science, page 23-30. Springer, (2011)Predicting Future Learning Better Using Quantitative Analysis of Moment-by-Moment Learning., , , and . EDM, page 74-81. International Educational Data Mining Society, (2013)Affective states and state tests: investigating how affect throughout the school year predicts end of year learning outcomes., , , , and . LAK, page 117-124. ACM, (2013)An Analysis of the Differences in the Frequency of Students' Disengagement in Urban, Rural, and Suburban High Schools., and . EDM, page 11-20. www.educationaldatamining.org, (2010)The sum is greater than the parts: ensembling models of student knowledge in educational software., , , and . SIGKDD Explorations, 13 (2): 37-44 (2011)Ensembling Predictions of Student Knowledge within Intelligent Tutoring Systems., , , , and . UMAP, volume 6787 of Lecture Notes in Computer Science, page 13-24. Springer, (2011)Sensor-free automated detection of affect in a Cognitive Tutor for Algebra., , , , , , , , , and . EDM, page 126-133. www.educationaldatamining.org, (2012)Towards an Understanding of Affect and Knowledge from Student Interaction with an Intelligent Tutoring System., , , and . AIED, volume 7926 of Lecture Notes in Computer Science, page 41-50. Springer, (2013)