In this paper, we proposed a new cognitive modeling approach: Instructional Factors Analysis Model (IFM). It belongs to a class of Knowledge-Component-based cognitive models. More specifically, IFM is targeted for modeling student’s performance when multiple types of instructional interventions are involved and some of them may not generate a direct observation of students’ performance. We compared IFM to two other pre-existing cognitive models: Additive Factor Models (AFMs) and Performance Factor Models (PFMs). The three methods differ mainly on how a student’s previous experience on a Knowledge Component is counted into multiple categories. Among the three models, instructional interventions without immediate direct observations can be easily incorporate into the AFM and IFM models. Therefore, they are further compared on two important tasks—unseen student prediction and unseen step prediction—and to determine whether the extra flexibility afforded by additional parameters leads to better models, or just to over fitting. Our results suggested that, for datasets involving multiple types of learning interventions, dividing student learning opportunities into multiple categories is beneficial in that IFM out-performed both AFM and PFM models on various tasks. However, the relative performance of the IFM models depends on the specific prediction task; so, experimenters facing a novel task should engage in some measure of model selection.