Optimized scripting in Massive Open Online Courses

The Time Machine MOOC, currently under preparation, is designed to provide the necessary knowledge for students to use the editing tool of the Time Machine platform. The first test case of the platform in centered on our current work on the City of Venice and its archives. Small Teaching modules focus on specific skills of increasing difficulty: segmenting a word on a page, transcribing a word from a document series, georeferencing ancient maps using homologous points, disambiguating named entities, redrawing urban structures, finding matching details between paintings and writing scripts that perform automatically some of these tasks. Other skills include actions in the physical world, like scanning pages, books, maps or performing a photogrammetric reconstruction of a sculpture taking a large number of pictures. Eventually, some other modules are dedicated to general historic, linguistic, technical or archival knowledge that constitute prerequisites for mastering specific tasks. A general dependency graph has been designed, specifying in which order the skills can be acquired. The performance of most tasks can be tested using some pre-defined exercises and evaluation metrics, which allows for a precise evaluation of the level of mastery of each student. When the student successfully passes the test related to a skill, he or she gets the credentials to use that specific tool in the platform and starts contributing. However, the teaching options can vary greatly for each skill. Building upon the script concept developed by Dillenbourg and colleagues, we designed each tutorial as a parameterized sequence. A simple gradient descent method is used to progressively optimize the parameters in order to maximize the success rate of the students at the skill tests and therefore seek a form of optimality among the various design choices for the teaching methods. Thus, the more students use the platform, the more efficient teaching scripts become.