Peer Reviewed Papers and Book Chapters

von Davier, A.A. & Wong, P.C. (2018). Making “It’s All About the Data” a Reality in Education. Getting Smart.

Chopade, P., Khan, S., Edwards, D., & von Davier, A. A. (2018). Machine learning for efficient assessment and prediction of human performance in collaborative learning environments.

Yudelson, M., Attali, M., Maris, G., Deonovic, B., & Bolsinova, M. (2018). Learning meets assessment: On the relation between item response theory and Bayesian knowledge tracing. Behaviormetrika, 45(2), 457-474.

Huang, Y. & Khan, S. (2018). Generating Photorealistic Facial Expressions in Dyadic Interactions. The British Machine Vision Conference. doi: http://bmvc2018.org/contents/papers/0590.pdf

Huang, Y. & Khan, S. (2018). A Generative Approach for Dynamically Varying Photorealistic Facial Expressions in Human-Agent Interactions. ACM Digital Library. doi: 10.1145/3242969.3243031

Savi, A. O., Ruijs, N., Maris, G., & van der Maas, H. (2017). Delaying Access to a Problem-Skipping Option Increases Effortful Practice: Application of an A/B Test in Large-Scale Online Learning. Computers & Education. 119, 84-94. Retrieved from https://doi.org/10.1016/j.compedu.2017.12.008

Epskamp, S., Maris, G. K., Waldorp, L. J., & Borsboom, D. (2018). Network psychometrics. In: P. Irwing, T. Booth & D. J. Hughes (Eds.). The Wiley Handbook of Psychometric Testing: A Multidisciplinary Reference on Survey, Scale and Test Development. London: John Wiley & Sons. (pp. 953-986). Retrieved from https://doi.org/10.1002/9781118489772.ch30

Waldorp, L., Marsman, M. & Maris, G. (2018). Logistic regression and Ising networks: prediction and estimation when violating lasso assumptions. Behaviormetrika. doi:10.1007/s41237-018-0061-0

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