Developing transparent predictive analytics has attracted significant research attention recently. There have been multiple theories on how to model learning transparency but none of them aims to understand the internal and often complicated modeling processes. In this paper we adopt a contemporary philosophical concept called ``constructivism’‘, which is a theory regarding how human learns. We hypothesis that a critical aspect of transparent machine learning is to ``reveal’’ model construction with two key process: (1) the assimilation process where we enhance our existing learning models and (2) the accommodation process where we create new learning models. With this intuition we propose a new learning paradigm using a Bayesian nonparametric to dynamically handle the creation of new learning tasks. Our empirical study on both synthetic and real data sets demonstrate that the new learning algorithm is capable of delivering higher quality models (as compared to base lines and state-of-the-art) and at the same time increasing the transparency of the learning process.