Page - 92 - in Radical Solutions and Open Science - An Open Approach to Boost Higher Education

6 OnlineTechnology inKnowledgeTransfer 97 and learningmanagement systems likeMoodle, Sakai, LAMS, andClaroline, and contentmanagers likeDrupalorPHPNuke,usercommunitiessuchastheubiquitous Facebook, instant messaging services likeMessenger, file exchange services like eMule, andmany other services flourished after 2000—since everything that has happened in these last fifteen years, the beast’s evolution has been relentless.Any online service becomes a de facto social network: Ali Express, Telegram,What- sApp, TripAdvisor, Booking, etc. (Karapanos, Teixeira, &Gouveia, 2016). From travel agencies to supermarkets to forumsabout automobiles or in journals, all ser- vices, ormost of them, require user traffic and visits, and transform their original objective in order to embrace volumewhich will enable higher invoicing, obtain more publicity, or increase their value in a sale or a place on the stock exchange. In all of them it is common to use forums, downloads, ratings (the poorly named gamificationwhichtrivializesausefulyetcomplicatedprocessfor improvementand advancement,merely by attributing little stars bornof afleeting feeling and a sim- plified system) (de SousaBorges,Durelli, Reis,& Isotani, 2014), assessment, file exchange, access toprivileged informationaccording todifferentmetrics, andother tools. The entire ecosystem of interaction becomes crucial to knowledge transfer whendesignedwith the appropriate approach. In the case of publication, the argu- ment seems obvious: generating a community of users (or sharing the results of research, development, or innovationbywayof an already-established community of users) implies an immediate and broad scope of possible impact. The trend is confirmed by universities’ increasingly common use of networks or serviceswith communities, suchasLinkedIn,YouTube,Twitter, orYammer. These communitiesmaybegeneral or categorisedbya thousandfilters, ranging fromlanguageorgender togeographyorexperience, topersonal interestor income range; those obtained throughmeans both legitimate and subtly or directly illegal, as in the case of Cambridge Analytica (Cadwalladr & Graham-Harrison, 2018); throughAlessiaorSiri,or throughthousandsofunnecessarycookies.Theymayalso be thematiccommunities focusedonresearch, suchasResearchGateorAcademia; ondataset repositories, likeMendeley;oronpublicationindexing, likeScopusIDor Orcid.Inanycase,theyallgroupandconnectusersaccordingtothesystem’soruser’s owncriteria,oftenwithrecommendationsbasedonbehaviourandprofile,inthesame spiritasNewtonLearning,Netflix,orAmazon.Thus,theybecomepowerfultoolsnot onlyforknowledgetransfer,butalsoforcultivatingideas,maintainingrelationships, screening profiles, and enabling the creation of partnerships for new projects. On theotherhand, exposingpersonaldataor individualorcollectivebehavioursallows the recovery or cataloguing of information for multiple uses. Characterisation of the user or the group favours a more direct and precise identification according to search criteria,which can be used towards noble ends (educational supervision, psychological support, sports training,etc.)or forother-notnecessarilyorexpressly authorised- services, such as consumer profiling, political campaigning, triage of currentorfuturepatients,identitytheft,andmanyotherpurposes(Tene&Polonetsky, 2012).