Connecting Silos : Automation system for thesis processing in Canvas and DiVA

Sammanfattning: As the era of digitalization dawns, the need to integrate separate silos into a synchronized connected system is becoming of ever greater significance. This thesis focuses on the Canvas Learning Management System (LMS) and the Digitala vetenskapliga arkive (DiVA) as examples of separate silos. The thesis presents several methods of automating document handling associated with a degree project. It exploits the fact that students will submit their thesis to their examiner via Canvas. Canvas is the LMS platform used by students to submit all their coursework. When the examiner approves the thesis, it will be archived in DiVA and optionally published on DiVA. DiVA is an institutional repository used for research publications and student theses. When manually archiving and publishing student theses on DiVA several fields need to be filled in. These fields provide meta data for the thesis itself. The content of these fields (author, title, keywords, abstract, …) can be used when searching via the DiVA portal. It might not seem like a massive task to enter this meta data for an individual thesis; however, given the number of theses that are submitted every year, this process takes a large amount of time and effort. Moreover, it is important to enter this data correctly, which is difficult when manually doing this task. Therefore, this thesis project seeks to automate this process for future theses. The proposed solution parses PDF documents and uses information from the LMS in order to automatically generate a cover for the thesis and fill in the required DiVA meta data. Additionally, information for inserting an announcement of the student's oral thesis presentation into a calendar system will be provided. Moreover, the data in each case will be checked for correctness and consistency. Manually filling in DiVA fields in order to publish theses has been a quite demanding and time-consuming process. Thus, there is often a delay before a thesis is published on DiVA. Therefore, this thesis project’s goal is to provide KTH with an automated means to handle thesis archiving and publication on DiVA, while doing so more efficiently, and with fewer errors. The correctness of the extracted meta data will be evaluated by comparing the results to the previously entered meta data for theses that have previously been achieved in DiVA. The automated process has been calculated to take roughly 50 seconds to prepare the information needed to publish a thesis to DiVA with ~71% accuracy, compared with 1 hour and 34% accuracy in the previous manual method.