Due to the use of online learning platforms and learning management systems, students are now working unsupervised on quiz-taking platforms. These unsupervised online forms of assessment are replacing traditional supervised quizzes in conventional classrooms. It is unclear whether the quiz-taking behaviors of students in these settings align with the expectations of instructors who set the quizzes. For example, do students engage in effective learning behaviors such as active recall and spaced practice? The quiz-taking behaviors of students using an online unsupervised platform were tracked, and the students were classified using the k-means algorithm based on their quiz-taking behaviors. This work is unique as it captures meaningful learner behaviors through data mining performed on an online platform, in contrast to conventional frequency-related measures such as the number of student logins or clicks. Later, the exam scores of the identified student groups were examined to identify the relationship between quiz-taking behaviors and performance on exams. Results indicate that the majority of students did not engage in effective quiz-taking behaviors. Effective quiz- taking behaviors were found to be correlated with high performance in exams. This work highlights the role of learning analytics in understanding the alignment of student behaviors with the learning design. Analytics can provide instructors with insights into learner behaviors in unsupervised learning platforms and play an important role in offering actionable, real-time feedback to correct these behaviors. Instructors can help bridge the gap between the intent of the learning design and the actual behaviors of learners by using the input available from analytics effectively.