A Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Study

Abstract

Educational institutions are transferring analytics computing to the cloud to reduce costs.Any data transfer and storage outside institutions involve serious privacy concerns, such as studentidentity exposure, rising untrusted and unnecessary third-party actors, data misuse, and data leakage.Institutions that adopt a “local first” approach instead of a “cloud computing first” approach canminimize these problems. The work aims to foster the use of local analytics computing by offeringadequate nonexistent tools. Results are useful for any educational role, even investigators, to conductdata analysis locally. The novelty results are twofold: an open-source JavaScript library to analyzelocally any educational log schema from any LMS; a front-end to analyze Moodle logs as proof ofwork of the library with different educational metrics and indicator visualizations. Nielsen heuristicsuser experience is executed to reduce possible users’ data literacy barrier. Visualizations are validatedby surveying teachers with Likert and open-ended questions, which consider them to be of interest,but more different data sources can be added to improve indicators. The work reinforces that localeducational data analysis is feasible, opens up new ways of analyzing data without data transfer tothird parties while generating debate around the “local technologies first” approach adoption