In data collected from virtual learning environments (VLEs), item response theory (IRT) models can be used to guide the ongoing measurement of student ability. However, such applications of IRT rely on unbiased item parameter estimates associated with test items in the VLE. Without formal piloting of the items, one can expect a large amount of nonignorable missing data in the VLE log le data, and this is expected to negatively impact IRT item parameter estimation accuracy, which then negatively impacts any future ability estimates utilized in the VLE. In the psychometric literature, methods for handling missing data are mostly centered around conditions in which the data and the amount of missing data are not as large as those that come from VLEs. In this paper, we introduce a semi-supervised learning method to deal with a large proportion of missingness contained in VLE data from which one needs to obtain unbiased item parameter estimates. The proposed framework showed its potential for obtaining unbiased item parameter estimates that can then be fixed in the VLE in order to obtain ongoing ability estimates for operational purposes. [This paper was published in: V. Cavalli-Sforza, A. N. Rafferty, C. Romero, & J. Whitehill (Eds.), "Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020)," (pp. 715-719).]