Knowledge Tracing plays a key role to personalize learning in an Intelligent Tutoring System including funtoot. Bayesian Knowledge Tracing, apart from other models, is the simplest well-studied model which is known to work well. Recently, Deep Knowledge Tracing based on Deep Neural Networks, was proposed with huge promises. But, soon after, it was discovered that the gains achieved by DKT were not of significant magnitude as compared to Performance Factor Analysis and BKT and its variants proposed. In the quest of examining and studying these models, we experiment with them on our dataset. We also introduce a logical extension of DKT, Multi-Skill DKT, to incorporate items requiring knowledge of multiple skills. We show that PFA clearly outperforms all the above mentioned models when the AUC results were averaged on skills while PFA and DKT, both were equally good, when they were averaged on all data points.