title: What is the simplest way to do pattern recognition on logs of user navigation on webpage
body: <p>Some context here: I am building an adaptive quiz/assessment system for my research. It's built with React on the frontend and is supported by the APIs that runs on Flask.</p>
  <p>In the quiz, the student may choose to navigate forwards/backwards (question-wise) or select/modify the answer for the current question. I am keeping track of these interactions using an array that looks something like this:</p>
  <pre><code>[SELECT_INCORRECT, NEXT, SELECT_INCORRECT, NEXT, SELECT_CORRECT, PREV, NEXT]
  </code></pre>
  <p>On the logs, there are only 4 possible actions: NEXT/PREV/SEL_CORRECT/SEL_INCORRECT. The length of each log could go up to possibly ~50-60 events.</p>
  <p>What I am trying to do here is to identify certain patterns that may reflect manipulative behavior (in the context of an adaptive test).</p>
  <p>For example, if at any section of the sequence, I detect a repeated / multiple subsequence(s) of <code>[PREV,SELECT_CORRECT]</code> or <code>[PREV,PREV,CORRECT,NEXT,CORRECT,NEXT]</code> I want to flag the quiz as potentially being manipulated by the student.</p>
  <p>Now, I am not sure how I can approach this problem without diving into too complicated stuff because I do not have access to any past/historical data. As such, I don't think I can do any kind of supervised learning using existing Python libraries. I also don't have prior experience with unsupervised learning, and it could also perhaps be an overkill?</p>
  <p>Apologies if the question seems too open-ended, but any advice to point me in the right directions would be greatly appreciated.</p>