Students often annotate texts they are reading using highlighting, underlining, and written comments and marks in the margins of the text. These may serve various functions and will reflect each student's goals and understanding of the text. This research proposes two simple biology-inspired approaches to represent the patterns of student annotations and to cluster students based on the similarity between their annotations; the annotations produced were simple highlighting. To verify the effectiveness of the proposed approaches, the research compared the processing speed of these approaches with generic hierarchical clustering algorithm implemented in Matlab and compared the accuracy of the clusters with the clusters created by human raters. The results show that both of the proposed approaches are more efficient and accurate than the generic hierarchical clustering algorithm. The proposed methodology can be implemented as an add-on to existing learning management systems and e-book readers, to automatically offer the students important notes and annotations conducted by others (either peers or students in the past) who have similar annotation behaviour pattern and style to the students.