With Big Data revolution, the education sector is being reshaped. The current data-driven education system provides many opportunities to utilize the enormous amount of collected data about students' activities and performance for personalized education, adapting teaching methods, and decision making. On the other hand, such benefits come at a cost to privacy. For example, the identification of a student's poor performance across multiple courses. While several works have been conducted on quantifying the re-identification risks of individuals in released datasets, they assume an adversary's prior knowledge about target individuals. Most of them do not utilize all the available information in the datasets. For example, event-level information that associates multiple records to the same individual and correlation between attributes. In this work, we propose a method using a Markov Model (MM) to quantify re-identification risks using all available information in the data under a more realistic threat model that assumes different levels of an adversary's knowledge about the target individual, ranging from any one of the attributes to all given attributes. Moreover, we propose a workflow for efficiently calculating MM risk which is highly scalable to large number of attributes. Experimental results from real education datasets show the efficacy of our model for re-identification risk.