Show simple item record

dc.contributorUniversitat Ramon Llull. La Salle
dc.contributor.authorAmo Filvà, Daniel
dc.contributor.authorCea Torrescassana, Sandra
dc.contributor.authorJiménez Burayag, Nicole Marie
dc.contributor.authorGómez Ponce, Pablo
dc.contributor.authorFonseca, David
dc.date.accessioned2021-07-25T17:12:05Z
dc.date.accessioned2023-10-02T06:27:07Z
dc.date.available2021-07-25T17:12:05Z
dc.date.available2023-10-02T06:27:07Z
dc.date.created2021-02
dc.date.issued2021-05
dc.identifier.urihttp://hdl.handle.net/20.500.14342/3162
dc.description.abstractEducational 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 adoptioneng
dc.format.extent28 p.cat
dc.language.isoengcat
dc.publisherMDPIcat
dc.relation.ispartofSustainability, 2021, 13 (9)cat
dc.rightsAttribution 4.0 International
dc.rights© L'autor/a.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceRECERCAT (Dipòsit de la Recerca de Catalunya)
dc.subject.otherArquitectura -- Informàticacat
dc.subject.otherUsabilitat (Disseny de sistemes)cat
dc.subject.otherInnovacions tecnològiquescat
dc.titleA Privacy-Oriented Local Web Learning Analytics JavaScript Library with a Configurable Schema to Analyze Any Edtech Log: Moodle’s Case Studycat
dc.typeinfo:eu-repo/semantics/articlecat
dc.typeinfo:eu-repo/semantics/publishedVersioncat
dc.rights.accessLevelinfo:eu-repo/semantics/openAccess
dc.embargo.termscapcat
dc.subject.udc004
dc.subject.udc62
dc.identifier.doihttps://doi.org/10.3390/su13095085cat
dc.relation.projectIDinfo:eu-repo/grantAgreement/URL i SUR del DEC/Projectes recerca PDI/2020-URL-Proj-058cat


Files in this item

 

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International
Except where otherwise noted, this item's license is described as http://creativecommons.org/licenses/by/4.0/
Share on TwitterShare on LinkedinShare on FacebookShare on TelegramShare on WhatsappPrint