{"id":39546,"date":"2019-10-31T22:35:27","date_gmt":"2019-10-31T19:35:27","guid":{"rendered":"https:\/\/prohoster.info\/blog\/kak-my-ispolzuem-tsepi-markova-v-otsenke-reshenij-i-poiske-bagov-so-skriptom-na-python\/"},"modified":"2019-10-31T22:35:27","modified_gmt":"2019-10-31T19:35:27","slug":"kak-my-ispolzuem-tsepi-markova-v-otsenke-reshenij-i-poiske-bagov-so-skriptom-na-python","status":"publish","type":"post","link":"https:\/\/prohoster.info\/en\/blog\/news\/kak-my-ispolzuem-tsepi-markova-v-otsenke-reshenij-i-poiske-bagov-so-skriptom-na-python","title":{"rendered":"How we use Markov chains in decision evaluation and bug finding. With a Python script","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

It's important for us to understand what happens to our students during their learning journey and how these events affect the outcome, which is why we build a Customer Journey Map. The learning process is not a continuous and cohesive entity; it's a chain of interconnected events and student actions, and these actions can vary greatly among different learners. After completing a lesson, what will they do next? Will they tackle their homework? Open the mobile app? Change courses or request a different teacher? Immediately jump into the next lesson? Or will they just leave feeling disillusioned? Can we analyze this map to identify patterns that lead to successful course completion or, conversely, to student drop-off?<\/p>\n

\"How
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\nTypically, specialized and quite expensive closed-source tools are used to create CJMs. However, we wanted to devise something simple that requires minimal effort and is ideally open-source. Thus, the idea to use Markov chains was born, and it worked out for us. We built a map, interpreted student behavior data as a graph, discovered completely non-obvious answers to global business questions, and even found deeply hidden bugs. We achieved all of this using open-source Python script solutions. In this article, I will discuss two cases with those very non-obvious results and share the script with anyone interested.
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\nSo, Markov chains demonstrate the probability of transitions between events. Here\u2019s a simple example from Wikipedia:<\/p>\n

\"How
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\nIn this case, 'E' and 'A' are events, the arrows represent transitions between them (including transitions from an event to itself), and the weights of the arrows signify the probability of the transition (a 'weighted directed graph').<\/p>\n

What was used<\/h2>\n

\nThe chain was trained using standard Python functionality, which processed student activity logs. The graph based on the resulting matrix was constructed using the NetworkX library.<\/p>\n

The log looks like this:<\/p>\n

\"How
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\nThis is a CSV file containing a table with three columns: student ID, event name, and the time when it occurred. These three fields are sufficient to track client movements, build the map, and ultimately create a Markov chain.<\/p>\n

The library returns constructed graphs in .dot or .gexf format. To visualize the former, you can use the free Graphviz package (the gvedit tool); we worked with .gexf and Gephi, which is also free.<\/p>\n

Next, I want to provide two examples of using Markov chains, which allowed us to rethink our goals, learning processes, and the Skyeng ecosystem itself while also addressing some bugs.<\/p>\n

First case: mobile application<\/h2>\n

\nTo start, we explored the student journey through our most popular product \u2014 the General course. At that time, I was working in the children's department at Skyeng and we wanted to see how effectively the mobile application works with our young audience.<\/p>\n

By taking logs and running them through a script, I obtained something like this:<\/p>\n

\"How<\/a><\/noindex><\/p>\n

The starting node is Start General, and at the bottom, there are three exit nodes: the student 'fell asleep,' changed courses, or finished the course.<\/p>\n