  Students interactions while solving problems in learning environments (i.e.
log data) are often used to support students learning. For example, researchers
use log data to develop systems that can provide students with personalized
problem recommendations based on their knowledge level. However, anomalies in
the students log data, such as cheating to solve programming problems, could
introduce a hidden bias in the log data. As a result, these systems may provide
inaccurate problem recommendations, and therefore, defeat their purpose.
Classical cheating detection methods, such as MOSS, can be used to detect code
plagiarism. However, these methods cannot detect other abnormal events such as
a student gaming a system with multiple attempts of similar solutions to a
particular programming problem. This paper presents a preliminary study to
analyze log data with anomalies. The goal of our work is to overcome the
abnormal instances when modeling personalizable recommendations in programming
learning environments.
