The arrangement of students in the classroom is a critical factor to consider, as it has never been predetermined. Despite the plethora of studies and research regarding classroom seating arrangements and evacuation procedures utilizing cellular automata, there remains a scarcity of literature exploring the detection of patterns and formation of student clusters within the classroom setting. To address this gap, we have devised a model referred to as the Distance from Whiteboard. The students’ seating arrangement depends on this model due to the impact the distance and visibility to the board have on their ability to comprehend the lecture content. In this master’s thesis, we seek to identify specific patterns and clusters that arise when students self-organize themselves within the classroom. To this end, we have proposed various methods for determining the existence of distinct seating arrangements. After establishing these patterns, we analyzed the resulting clusters to determine the seats that tend to be occupied by the most and least number of students. Our method for pattern detection involves computing the similarity between original data matrices and then between randomly generated matrices. The similarity is calculated using the Optimized Manhattan method, which has been found to be effective. Our findings demonstrate that specific patterns and clusters exist, revealing the seats students prefer based on their proximity to the whiteboard as determined by the Distance from the Whiteboard model.