Investigating the use and acceptance of technologies by professors in a higher education institution

DOI: 10.4018/IJOPCD.2019040101



Copyright©2019,IGIGlobal.CopyingordistributinginprintorelectronicformswithoutwrittenpermissionofIGIGlobalisprohibited. 

Investigating the Use and Acceptance

of Technologies by Professors in

a Higher Education Institution

Carolina Costa, University of Aveiro, Portugal Helena Alvelos, University of Aveiro, Portugal Leonor Teixeira, University of Aveiro, Portugal

ABSTRACT Thisarticleanalysestheuseandacceptanceoftechnologiesbyprofessorsintheteachingand learningcontextinahighereducationinstitution.Intheempiricalstudy,aquestionnairebasedon thetechnologyacceptancemodelwasapplied.Theresultsindicatedthatthemostusedtechnologies areMoodle,FacebookandYouTubeanditwasconcludedthatingeneral,thosetechnologiesare wellaccepted.Fewstatisticallysignificantdifferencesbetweenrespondents’gender,scientificareas orageswerefound,revealingthattheuseofthosetechnologiesisalreadywidespreadinthestudied institution.Resultsalsoshowedthatperceivedusefulnessandperceivedeaseofusearetwoimportant determinantsofMoodleacceptance,andthatthemajorityofrespondentsdidnotknowtheMOOC concept.Thisarticleisvaluableforresearchersintheareaandforprofessorsthatwanttoimplement theusetechnologiesintheteachingandlearningcontext. KEywORDS

Higher education, Learning Management Systems, MOOCs, Technology Acceptance Model, Web 2.0

INTRODUCTION ManyHigherEducationInstitutions(HEI)havebeendevelopingcoursesusingavarietyoftechnologies todeliverdistanceeducationprogrammes,withe-learningbeingthemostpopularform(Arkorful,& Abaidoo,2015;Zimnas,Kleftouris,&Valkanos,2009).E-learningreferstotheuseoftechnologiesin ordertoprovidelearningsolutionswherethelearningcontextcanbeaccessedfromtheweb(Zimnas etal.,2009).ThetechnologiesthatusuallysupporttheTeachingandLearning(TL)processinHigher EducationInstitutions(HEI)canbeclassifiedinLearningManagementSystems(LMS),Web2.0 technologies,orMassiveOpenOnlineCourses(MOOCs)platforms. Themainobjectiveofthisworkistopresenttheresultsofanempiricalstudyabouttheuse andacceptanceoftheTLtechnologiesbyprofessorsinaPortugueseHigherEducationInstitution- UniversityofAveiro(UA). Thispaperisorganizedinfivesections.Thesecondsectionpresentingthetheoreticalbackground performsanoverviewofthemaintechnologiesusedinHE:LMS,Web2.0technologiesandMOOCs platforms,andreviewsthemainmodelsoftechnologies’acceptance.Thethirdsectiondescribesthe materialandmethodsusedinthisstudy.Thefourthsectionpresentstheresultsanddiscussion.Finally, thelastsectionpresentsthemainconclusionsofthestudyandrecommendationsforfurtherresearch.

THEORETICAL BACKGROUND Technologies Used in Higher Education

InformationandCommunicationTechnologies(ICTs)supportTLprocessandarefrequentlyinvolved indatacollection,informationprocessingandknowledgecreationactivities(Costa,Alvelos,& Teixeira,2015).Nowadays,UniversitiesadaptTLmethodsusingtheICTsforknowledgetransmission. Studentsownanduseadiversityoftechnologies,butinstitutionsandinstructorshaveyettoseize opportunitiestocreatemorevariedlearningexperiencesoutsidetheclassroom(Epelboin,2013). ICTsineducationcontexthavebeenchangingaccordingtotheevolutionoftechnology.The societyhasembracednewformsofcommunicationovertime.Atypicalexampleistheevolution fromthebasiccorrespondencethroughpostalservicetothevarietyoftoolsinWeb(Moore,Dickson-Deane,&Galyen,2011),wheree-mailplaysanimportantrole. NextsubsectionsaddresstheconceptsofLMS,Web2.0,andMOOCsplatformsasimportant representativesoftechnologiesusedineducation,particularly,inHEIs.

Learning Management Systems

LearningManagementSystems(LMS)aretechnologicalsystemsusedtocreateonlinecourses (Paulsen,2003)andgrewfromarangeofmultimediaandinternetdevelopmentsinthe1990s (Coates,James,&Baldwin,2005).Theyallowuserstoregister,monitorandevaluateactivitiesand tomanagecontents,aswellastoexchangeinformationamonggeographicallydispersedusers.In theeducationalcontext,LMSallowtheuseofvariousmethodstoimpartinformation,anddevelop skillsandcompetences(Ekúndayò&Tuluri,2011). LMSsupportdistanceeducationandcomplementthetraditionalwayofteaching(Costaet al.,2015),throughe-learningactivitiessuchascommunication,collaborationandinformation/ knowledgetransfer(Al-Busaidi&Al-Shihi,2012).Byusingthesesystems,studentscanaccess courses’contentsindifferentformats(text,image,sound),aswellasinteractwithteachersand/or colleagues,via,forexample,messageboards,forums,chats,video-conferences(Sanchez&Hueros, 2010).Theseplatformsareclosedtoauthorizedusers,areteacher-centredanddonotrelyaloton students’contribution(Manca&Ranieri,2016).TheLMScanbecommercialsolutionsasBlackboard, oropen-sourceones,suchasMoodle. ThecurrentLMSincorporateWeb2.0technologies(Holme&Prieto-Rodriguez,2018).These platformsstrengthentraditionalacademicvaluesofsharingandcollaborativecreationofknowledge byprovidingteachersandlearnerswithplatformsforcollaboration,thusenablingteachersand learnerstojointlydevelopeducationalcontent,supportingtheexchangeofmaterial,andfacilitating communitybuilding(Ornellas&Carril,2014).TheLMSplatformsallowmaintainingarepository ofinformation,butalsodesigninganactive,participativeandcollaborativevirtualteaching,since theyallowcommunicationbetweenallthemembersoftheplatform(Garciaetal.,2015). web 2.0 Technologies Web2.0isasecondgenerationofWebapplications,basedononlineservices,collaboration, communication,andsharing,andreflectsdifferentwaysofpromotinginteractionbetweenpeople (Bennett,Bishop,Dalgarno,Waycott,&Kennedy,2012).ItemergedinOctober2004,developed byO’ReillyandMediaLiveInternational(O’Reilly,2005)andsupportssocialinteraction,feedback, conversationandnetworking,beingendowedwithaflexibilitythatenablescollaboration.This paradigmredefinestheinteractionbetweenInternetandusers,allowingthecreationofvirtual applicationsusingdataandfunctionalityfromanumberofdifferentsources(Costa,Teixeira,& Alvelos,2014).TheuseofWeb2.0technologieshassignificantpotentialtosupportandenhance in-classTLinHEI(Ajjan&Hartshorne,2008;Jimoyiannis,Tsiotakis,Roussinos,&Siorenta,2013). TheWeb2.0technologiesareopentoeveryoneandanybodycanusethem(Ornellas&Carril,2014).

SomeoftheWeb2.0technologiesareWikis,Blogs,Microblogs,SocialNetworks,Social Bookmarks,andMediaSharing(VideoSharing,Podcasting,PhotoSharing,andSlidesSharing). WikisallowonepersonormoretobuildupacorpusofknowledgeinasetofinterlinkedWebpages, usingaprocessofcreating,writingandeditingpages(Grosseck,2009;Kear,Woodthorpe,Robertson, &Hutchison,2010).BlogsrepresentaWebpagewithbriefparagraphsofopinions,informationin theformoftext,images,video,audio,orlinks,calledposts,arrangedchronologicallybeingthe mostrecentthefirst(Grosseck,2009;Halic,Lee,Paulus,&Spence,2010).Microblogsaresimilar toBlogs,thatallowtopublishbriefonlinetextslimitedto140-200characters(Ebner,Lienhardt, Rohs,&Meyer,2010;Holotescu&Grosseck,2009;Hsu&Ching,2012).SocialNetworkssupport collaboration,knowledgesharing,interaction,andcommunicationofusersfromdifferentplaces withacommongoal(Al-Samarraie&Saeed,2018;Grosseck,2009).MediaSharingallowtostore, search,displayandsharemedia’files(Anderson,2007),beingthemostcommonVideoSharing, Podcasting,PhotoSharing,andSlideSharing.

Massive Open Online Courses

TheMassiveOpenOnlineCourses(MOOCs)conceptemergedin2008(Blackmon&Major,2017)and hasbeenadoptedbymanyuniversitiesacrosstheworld(Coatesetal.,2005;Hew,&Cheung,2014). MOOCscanbedefinedasonlinecoursesthatbringtogetherpeoplewhoareinterestedinlearning aboutaspecificsubject.Theirmaingoalistochange“thefixeddynamicsofrigiduniversitytraining modelsandthetraditionalorganizationalstructuresofuniversities”(Aguaded-Gomez,2013,p.7). Thesecoursesarebasedonlearningnetworks(Kopetal.,2011),areguidedbysubjects’expertsas learningfacilitators(Kop,Fournier,&Mak,2011;Liyanagunawardena,Adams,&Williams,2013), arefreeofcharge,andprovidethestudentswithflexibility,onavarietyofthemes(Daniel,Cano, &Cervera,2015).MOOCsprovideanopportunityforpeopletoaccessfreecoursesofferedbytop universitiesintheworldandthereforeattractedgreatattentionandengagementfromcollegeteachers andstudents(Xu&Yang,2015). In2011,therewasa‘waveofoffers’ofMOOCs(Tschofen&Mackness,2012).Someuniversities havebeenofferingonlineeducationalprogramsandcreatingtheirownMOOCs’platforms.This technologyisbeingusedasanewonlineeducationalmodel(Jung&Lee,2018;Sharma,Joshi,& Sharma,2016)whereparticipantsareencouragedtofreelyshareinformationbetweenthembymeans oftechnologiesasSocialNetworks(Baker,Bujak,&DeMillo,2012). TheLMSplatformscanalsobeusedforaformofcourseswhicharesmallMOOCswithless than10,000studentsenrolled(Al-Samarraie&Saeed,2018;Epelboin,2013),likeSmallPrivate OnlineCourses(SPOCs)(Liu,Cheng,Liu,&Sun,2017).

Acceptance of Technologies in Higher Education

TheacceptanceoftechnologiesisusuallyevaluatedthroughtheoreticalmodelssuchastheTAM- TechnologyAcceptanceModelorUTAUT-UnifiedTheoryofAcceptanceandUseofTechnology.The TAMisbasedontheTheoryofReasonedAction(TRA),inwhichtheTheoryofPlannedBehaviour (TPB)isalsobased.UTAUTwasdevelopedbasedonTRAandTAM(Venkateshetal.,2003). TheTAM,developedbyDavis(1986),isthemostwidelyusedmodeloftechnologyacceptance (Venkateshetal.,2003).Accordingtoit(Figure1),theActualSystemUse(ASU)ofthetechnology inevaluation,isdeterminedbytheAttitudeTowardUsingit(ATU),beingthisvariableinfluenced byothertwovariables:PerceivedEaseOfUse(PEOU)andPerceivedUsefulness(PU).Thosetwo variablescanbeinfluencedbyExternalVariables(EV)(Davis,1986). PerceivedEaseofUse(PEOU)isdefinedasthedegreetowhichanindividualbelievesthat theuseofaparticularsystemisintuitiveanddoesnotrequiregreateffort(Davis,1986;1989). PerceivedUsefulness(PU)isdefinedasthedegreetowhichanindividualbelievesthatuseofthe systemcontributestoincreasetheperformanceoftheirwork(Davis,1986;1989;Davisetal.,1989). Besidesbeinginfluencedbyexternalvariables(EV),itisalsoinfluencedbyPEOU,sincetechnologies

perceivedaseasiertousetendtobeperceivedasmoreuseful.AttitudeTowardUsing(ATU)isdefined asapositiveornegativefeelingofanindividualtowardstheuseofthesystem(Davis,1986;1989; Davisetal.,1989)andisinfluencedbyPUandPEOU. TheapplicationofTAMisanextensionoftheoriginalmodelwhereEVareaddedaccording tothespecificcharacteristicsoftheanalysedtechnology(Oum&Han,2011),suchasfeaturesof technology,usercharacteristics,environments,userinvolvement,andstructureoforganization(Chen etal.,2012). ConcerningtheUnifiedTheoryofAcceptanceandUseofTechnology(UTAUT),developed byVenkateshetal.(2003),andrepresentedinFigure2,itwasbasedonotherconceptualmodelsof technologies’acceptance.Thismodelconsistsoffourconstructs–PerformanceExpectancy,Effort Expectancy,SocialInfluence,andFacilitatingConditions–andalsobyfourmoderatingvariables– Gender,Age,Experience,andVoluntarinessofUse(Venkateshetal.,2003). ThePerformanceExpectancyisdefinedas“thedegreetowhichanindividualbelievesthatusing thesystemwillhelphimorhertoimprovejobperformance”(Venkateshetal.,2003,p.447).This constructevolvedfromothermodels’constructs,like,forexample,thePUofTAM(Venkateshetal., 2003).TheEffortExpectancyisdefinedas“thedegreeofeaseassociatedwiththeuseofthesystem” (Venkateshetal.,2003,p.450).ThisconstructincludesPEOUoftheTAM(Venkateshetal.,2003). TheSocialInfluenceisdefinedas“thedegreetowhichanindividualperceiveshowimportantitisfor otherpeopletousethesystem”(Venkateshetal.,2003,p.451).TheFacilitatingConditionsaredefined as“thedegreetowhichanindividualbelievesthatanorganizationalandtechnicalinfrastructure existtosupportthesystem”(Venkateshetal.,2003,p.453).Thefirstthreeconstructs(Performance Expectancy,EffortExpectancy,andSocialInfluence)influencetheBehaviouralIntentionandthe last(FacilitatingConditions)influencestheUseBehaviour. Inthisstudy,thevariablesATU,PEOUandPUofTAMwereused,aswellasSocialInfluence (SI)ofUTAUTwithExternalVariables.

MATERIAL AND METHODS

Thisstudy,carriedoutattheUniversityofAveiro(UA),aimedtoanalysetheuseandacceptanceof technologiesusedbyprofessorsintheTLcontext,beingitsmainobjectives:(i)tocharacterizethe usageandtheacceptanceofthetechnologiesused;(ii)tocomparetheacceptanceofthetechnologies betweensomegroupsofprofessors;(iii)tousetheTAMtobettercharacterizetheacceptance,by professors,ofthemostusedtechnologies;and(iv)toexploretheusageofMOOCs.

TheUAhasanintegratedstructurethatallowsthearticulationandharmonizationofteaching andresearchenvironmentsandoffersawiderangeofdegreeprogramsinseveralareasofknowledge. Consequently,ithasamultidisciplinaryandinnovativenature,offering184undergraduateandgraduate courses,14,280students,and903professors.TheUAhas16departmentsandfourpolytechnics schools,comprisingtheareasofLifeSciencesandHealth,NaturalandEnvironmentalSciences,Exact SciencesandEngineering,andSocialSciencesandHumanities(UA,2018).Inthisinstitution,the qualityissuehasbeenplacedasaprioritythatisreflectedinthethreeareasofitsmission:Education, ResearchandCooperation.ConsideringtheEducationarea,theUAoffersabroadrangeofICTthat supportitsprocesses. Thedatacollectionofthisstudywasperformedusingaquestionnairedesignedbasedonthe literaturereviewandappliedtoalltheprofessorsoftheUA(903)betweenMarchandMay,2016. Therewereobtained97answersfromdiversescientificareas.Thefinalquestionnaireresultedfrom theapplicationofapriorversiontoapilotsampleof5professorsandisdividedintothefollowing threesections: • Characterizationoftheparticipants; • CharacterizationoftheuseandacceptanceofsomeLMSandWeb2.0technologies; • CharacterizationoftheuseofMOOCs. Thetechnologies’acceptancewasassessedusingtheTAMvariablesandafive-pointLikert scalethatmeasuredthelevelofagreementoftherespondentwitheachitem(1-donotagreeatall; 5-completelyagree).Therewere19itemsforcharacterizingtheacceptanceofthetechnologiesnot providedbytheUA:Facebook,Linkedln,YouTube,Flickr,Instagram,iTunes,MediaWiki,Blogger, Twitter,andonemore(20items)forcharacterizingtheacceptanceoftechnologiesprovidedbythe

UA:Moodle,Educast,andaWeb2.0platformnamedSapo campusthatprovidesVideoSharing, PhotoSharing,WikisandBlogs. Table1presentsthevariablesandtheTAMitemsconsideredintheevaluationofthereferred technologies’acceptance.Theexpression“TECHNOLOGYX”shouldbereplacedbyeachofthe technologiesunderevaluation. ThecollecteddatawereanalysedusingtheIBMSPSSStatistics23software.First,adescriptive analysiswasperformed,inordertocharacterizetheparticipantsandthebehaviourofeachvariable measured.Following,Mann-WhitneyandKruskal-Wallistestswerecarriedoutinordertoverify whethertherewerestatisticallysignificantdifferencesbetweenlevelsofagreementregardingeach variableamonggroupsofprofessorscharacterizedbygender,researchareas,andagegroup.Finally, multipleregressionswereusedtocalculatetheinfluencesandtherelationshipsamongTAMvariables.

Table 1. Items considered in the evaluation of the acceptance of the usage of the technologies

Variable Item

PerceivedEaseOf

Use(PEOU) PEOU1-LearninghowtouseTECHNOLOGYXiseasy._{PEOU2-Itisoftennecessarytoconsultthesupport/helptutorialstouseTECHNOLOGYX.} PEOU3-TheTECHNOLOGYXmenusandfeaturesareeasytounderstand. PEOU4-IgetconfusedwhenIusetheresources/activitiesofTECHNOLOGYX. PEOU5-IoftenmakemistakeswhenIuseTECHNOLOGYX. PEOU6-It’seasytorememberhowtoperformthetasksrelatedtothecreation/editingof resources/activitiesinTECHNOLOGYX. PEOU7-Overall,IfindTECHNOLOGYXiseasytouse. Perceived

Usefulness(PU) PU1-UsingTECHNOLOGYXallowsmetobetterorganizeandtracktasksrelatedtotheTeaching-Learningprocess. PU2-TECHNOLOGYXallowsmetoperformtaskswithoutbeingdependentonschedules. PU3-UsingTECHNOLOGYXallowsmetosavetime.

PU4-UsingTECHNOLOGYXimprovestheoutcomeoftheTeaching-Learningprocess. PU5-Overall,IfindTECHNOLOGYXusefulfortheTeaching-Learningprocess. AttitudeToward

Using(ATU) ATU1-IlikeusingTECHNOLOGYXinTeaching-Learningcontext._{ATU2-IrecommendtheuseofTECHNOLOGYXtosupporttheTeaching-Learningprocess.} ATU3-Overall,IhaveafavourableattitudetowardsusingTECHNOLOGYXinTeaching-Learningcontext.

SocialInfluence

(SI) SI1-IuseTECHNOLOGYXbecauseitisprovidedbytheUniversityofAveiro.*_{SI2-IuseTECHNOLOGYXbecauseIwasinfluencedbycolleagues.} SI3-IuseTECHNOLOGYXbecauseIwasdirectlyorindirectlyinfluencedbystudents. SI4-Theeditingfeatures/activitiesinTECHNOLOGYXallowmetocommunicate/collaborate withstudents.

SI5-IconsiderthatthereisatendencytodevelopmoreactivitiesusingTECHNOLOGYXinthe future.

Legend: TECHNOLOGY X- Technology under evaluation (Facebook, Linkedln, YouTube, Flickr, Instagram, iTunes, MediaWiki, Blogger, Twitter, Moodle, Educast, Video Sharing–Sapo campus, Photo Sharing–Sapo campus, Wiki–Sapo campus, Blog–Sapo campus); *- item used in the technologies provided by the UA.

RESULTS AND DISCUSSION

Theresultsfromthequestionnairearepresentedinthefollowingfivesub-sections:(i)characterisation oftheparticipants;(ii)characterisationoftheuseandacceptanceoftechnologies;(iii)comparison oftheacceptancebetweensomegroupsofprofessors;(iv)useofTAMforevaluatingtheacceptance ofthemoreusedtechnologies;and(v)characterisationoftheuseofMOOCs.

Characterization of the Participants

Participantswere62femalesand35malesandtheaverageageofrespondentswas44.5yearsold(s =8.42).Themajorityoftheprofessorswerefromtheuniversitysubsystem(76;79.2%),fromwhich 50(52.1%)wereAssistantProfessors,asillustratedinTable2. Table3presentsthedistributionoftherespondentsbytheresearchareas.Itcanbeobservedthat themajorityofthemwerefromSocialSciencesandHumanities(54;55.7%)andExactSciencesand Engineering(36;37.1%).

Characterization of the use and Acceptance of Technologies

Themostusedplatformsbytherespondentswere:Moodle(96),Facebook(40),andYouTube(32). Thisresultisinlinewiththeresultsreportedintheliterature(Campanellaetal.,2008;Escobar-Rodriguez,Carvajal-Trujillo,&Monge-Lozano,2014;Danyaro,Jaafar,DeLara,&Downe,2010; Galan,Lawley,&Clements,2015;Manca&Ranieri,2016).Professors,whenfacedwiththeuseof twoormoreplatformsofthesametechnology,indicatedwhichonetheyusemore,inordertoproceed withthequestionnaireregardingonlythatone.

Table 2. Professional category of academics

Education subsystem Professional category n %

University FullProfessor 6 6.3 AssociateProfessor 14 14.6 AssistantProfessor 50 52.1 Assistant 6 6.3 Polytechnicschool CoordinatorProfessor 3 3.1 AdjunctProfessor 14 14.6 Other 3 3.1 Total 96 100.0

Table 3. Research areas of the academics

Research areas n % LifeandHealthSciences 4 4.1 NaturalandEnvironmentalSciences 3 3.1 ExactSciencesandEngineering 36 37.1 SocialSciencesandHumanities 54 55.7 Total 97 100.0

Table4presents,foreachplatform,thenumberofanswersgiveninthesectionrelatedtothe acceptanceofthetechnologiesused. Regardingtechnologyacceptance,thetechnologieswithmoreanswerswereMoodle(96), Facebook(36)andYouTube(29)andtheiracceptancewasevaluatedbythevariablesdescribedinTable 1.Table5presentsadescriptiveanalysisoftheanswerstotheitemsrelatedtothereferredvariables. Ingeneral,academicsexpressedapositiveattitudeconcerningthevariousitems.Regardingthe itemsPEOU2–“Itisoftennecessarytoconsultthesupport/helptutorialstouseTECHNOLOGYX”, PEOU4–“IgetconfusedwhenIusetheresources/activitiesofTECHNOLOGYX”,andPEOU5–“I oftenmakemistakeswhenIusetheTECHNOLOGYX”,itshouldbenoticedthatthequestionswere askedusingthescalewithaninvertedorder,whencomparedwiththeotheritems.Asaconsequence, theseitemspresentlowlevelsofagreement. ThevaluescomputedforthevariablesPEOU,PU,ATUandSIcorrespondedtotheaverage valuesoftherespectiveitems,calculatedforeachrespondent.Thisprocedureledtodifferentsample sizes,asthemissingvalueshadahigherimpactinthevariablesconsidered(PEOU,PU,ATUandSI) thanintherespectiveitems.ThevaluesofthescaleoftheitemsPEOU2,PEOU4andPEOU5were changed,convertingthelevel1ofthescaleto5,thelevel2to4,thelevel4to2,andthelevel5to1. RegardingMoodle,themeanvalueofPEOUwas3.98(s=0.634),withtheitemsPEOU1– “LearninghowtouseMoodleiseasy”,andPEOU7–“Overall,IfindtheMoodleiseasytouse” havingahigherlevelofagreement.ThisresultisconsistentwiththestudiesofNorth-Samardzicand Jiang(2015)andWingo,Ivankova,andMoss(2017),wheretheeaseofuseofthetechnologyisthe mostimportantfactorthatinfluencesintentiontouseMoodle.ThePUvariablehasameanvalueof 3.89(s=0.756),withtheitemPU3-“UsingMoodleallowsmetosavetime”having,onaverage, lowervaluethantheotheritems.ThisresultwaspartiallyalignedwiththestudyfromIslamand Azad(2015)whichindicatedprofessorsconsideredthatMoodle“addanextraloadtotheirteaching tasksandreducetheirautonomyandcontrolintheclassroom”.ThemeanvalueofATUwas4.01(s =0.843),withitemsrangingfrom3.94to4.08.ThemeanvalueofSIwas2.88(s=0.659),having theitemsSI2–“IuseMoodlebecauseIwasinfluencedbycolleagues”andSI3–“IuseMoodle becauseIwasinfluenceddirectlyorindirectlybythestudents”,onaverage,lowervaluesthanthe otheritems.ItshouldbenoticedthattheitemSI1–“IuseMoodlebecauseitistheLMSprovided bytheUniversityofAveiro”presentedthehighestaveragevalue(4.64)ofalltheitems,probably

Table 4. Number of answers to technologies’ acceptance

Technology Platform Number of answers

LMS Moodle 96 Educast 4 SocialNetworks Facebook 36 LinkedIn 8 VideoSharing YouTube 29 PhotoSharing Instagram 5 Podcasting iTunes 6

Wikis Wiki-sapo campus 3

Mediawiki 5

Blogs Blog-sapo campus 4

Blogger 4

reflectingthatprofessorsfelttheimportanceofhavingaLMSavailabletosupporttheTLprocess andusedtheoneprovidedbytheinstitutionwheretheyteach. ConcerningFacebook,themeanvalueofPEOUwas4.23(s=0.663),andtheitemsPEOU1 andPEOU7havehadahigherlevelofagreementthantheothers.ThisresultrevealsthatFacebook isrelativelyeasytouse,asthestudyofPinhoandSoares(2011)pointout.Themeanvalueofthe PUvariablewas3.04(s=0.944),andtheitemsofPUpresentaveragevaluesfrom2.61to3.29.The meanofATUis2.94(s=0.988)anditsitemspresentaveragevaluesrangingfrom2.86to3.06. Accordingthesefindings,theperceivedusefulnessandeaseofusecanhaveimpactontheintention toadoptFacebook(Rueda,Garcia,&Silva,2017;Thongmak,2014).ThemeanvalueofSIwas3.16 (s=0.570)havingtheitemSI5ahighervaluethantheotheritems. ConsideringYouTube,themeanvalueofPEOUwas3.99(s=0.630)withtheitemsPEOU1- “LearninghowtouseYouTubeiseasy”andPEOU7-“Overall,IfindtheYouTubeiseasytouse” showingahigherlevelofagreement.TheitemsthatbelongtoPUpresentaveragevaluesfrom3.24 to3.78.TheitemsonthevariableATUpresentaveragevaluesofagreementrangingfrom3.81to 4.00.RegardingSIvariableitcanbestressedthattheitemSI5presentsanaveragevalue(3.85) higherthantheotheritems.

Table 5. Descriptive statistics of the items on the of technologies’ acceptance

Moodle Facebook YouTube

Item n Mean Med Mod SD n Mean Med Mod SD n Mean Med Mod SD

PEOU1 96 4.00 4.00 4 0.781 35 4.43 5.00 5 0.739 29 4.07 4.00 4 0.704 PEOU2* 95 1.96 2.00 2 0.933 32 1.66 1.00 1 1.035 27 2.07 2.00 2 0.874 PEOU3 92 3.80 4.00 4 0.867 34 4.15 4.00 4 0.784 26 3.77 4.00 4 0.992 PEOU4* 95 2.00 2.00 2 0.911 34 1.79 1.50 1 1.008 26 1.73 2.00 2 0.667 PEOU5* 95 1.87 2.00 2 0.775 34 1.82 2.00 2 0.834 25 1.76 2.00 2 0.663 PEOU6 95 3.85 4.00 4 1.000 33 3.97 4.00 4 1.045 25 3.80 4.00 4 0.866 PEOU7 96 4.04 4.00 4 0.832 34 4.24 4.00 4 0.781 28 4.04 4.00 4 0.838 PEOU 89 3.98 4.00 4.00 0.634 32 4.23 4.14 5.00 0.663 25 3.99 4.00 4.00 0.630 PU1 95 3.77 4.00 4 0.994 33 2.61 2.00 2 1.298 25 3.28 3.00 4 1.137 PU2 93 4.19 4.00 4 0.900 33 3.27 3.00 4 1.281 24 3.33 3.50 4 1.129 PU3 93 3.69 4.00 4 1.073 33 2.85 3.00 2 1.228 25 3.24 3.00 4 1.268 PU4 96 3.74 4.00 4 1.018 34 3.29 3.00 3 0.938 27 3.70 4.00 4 1.103 PU5 96 4.11 4.00 4 0.844 35 3.20 3.00 4 0.964 27 3.78 4.00 4 0.934 PU 92 3.89 4.00 4.00 0.756 33 3.04 3.00 3.20 0.944 23 3.39 3.40 3.00 0.949 ATU1 96 4.00 4.00 4 0.846 36 2.92 3.00 3 1.079 26 3.81 4.00 4 1.021 ATU2 96 3.94 4.00 4 0.938 36 2.83 3.00 3 1.056 28 3.96 4.00 4 0.922 ATU3 96 4.08 4.00 4 0.854 36 3.06 3.00 3 0.955 27 4.00 4.00 4 0.877 ATU 96 4.01 4.00 4.00 0.843 36 2.94 3.00 3.00 0.988 26 3.94 4.00 4.00 0.885 SI1 96 4.64 5.00 5 0.651 --- --- --- --- --- --- --- --- --- ---SI2 86 2.05 2.00 1 1.283 35 2.71 3.00 4 1.363 24 2.58 3.00 3 1.248 SI3 87 1.74 1.00 1 1.051 36 2.64 3.00 1 1.397 26 2.65 2.50 2 1.263 SI4 96 4.04 4.00 4 0.905 36 3.56 3.00 3 0.843 25 3.04 3.00 3 1.136 SI5 95 3.76 4.00 4 0.964 33 3.70 4.00 4 1.045 26 3.85 4.00 4 1.008 SI 86 2.88 2.88 2.75 0.659 32 3.16 3.25 3.25 0.570 21 2.98 3.00 3.00 0.782 Legend: *- scale with an inverted order; N- number of respondents; Med- median; Mod- mode; SD- standard deviation.

Itisinterestingtonotethat,inwhatconcernsthevariablePEOU,thethreetechnologiesanalysed hadPEOU1andPEOU7astheitemsshowinghighervalues,probablymeaningthattheeaseofuse isanimportantissuefortherespondentsthatispresentinallthesetechnologies.RegardingSIitems, itshouldberemarkedthat,whilewithMoodlethehigherlevelofagreementreferstoitsusebecause itistheLMSprovidedbytheUA,withFacebookandYouTubethetendencyofusingtheminthe futureisthehighestvalueditem.Comparingthemainvariablesamongthethreetechnologies,the PEOUhasahighermeanvalueinFacebookthaninothertechnologies,suggestingthatitisaneasy-to-usetechnology.ThePUhasahighermeanvalueinMoodlethanYouTubeandFacebook,probably meaningthatMoodleismoreusefulintheTLcontext.TheATUhadahighermeanvalueinMoodle thanintheothertechnologies,showingthatprofessorshaveafavourableattitudeinusingthistoolin theTLcontext.TheSIshowedahighermeanvalueforFacebook,confirmingitsgreatersocialnature. Comparison of the Acceptance of the More used

Technologies Between Groups of Professors

InthissectionsomecomparisonsoftheacceptanceofMoodle,FacebookandYouTubebetween groupsofprofessorsbasedongender,agegroupandscientificareawereperformed.Theagegroups consideredwere[28,39],[40,49]and[50,67]andthescientificareasconsideredwereareaAthat groupedLifeandHealthSciences,NaturalandEnvironmentalSciences,andExactSciencesand Engineering,andareaBthatwasSocialSciencesandHumanities.Thestatisticaltestsperformed wereMann-WhitneyforgenderandscientificareaandKruskal-Wallisforagegroups,andTable6 presentstheitemsforwhichthenullhypothesiswasrejectedand,therefore,thedifferencesamong groupswerestatisticallysignificant. ThecomparisonoftheMoodleacceptancebetweengender,showstatisticallysignificant differencesinitemsPEOU1,PEOU7,PU4,PU5,ATU1andATU3,wherethefemalespresent,on averagerank,highervaluesthanmales.Theseresultsaresimilartothosepresentedinthestudyof Padilla-Meléndez,Aguila-Obra,&Garrido-Moreno(2015),wherefemalesshowedhigherscoresin theitem“IlikeusingMoodle”. Concerningthescientificarea,therewerestatisticallysignificantdifferencesintheitemsATU1, ATU2,ATU3andSI2,wheretheprofessorsbelongingtoareaBpresented,onaveragerank,higher values.ThisresultispartiallyconsistentwiththestudyofMancaandRanieri(2016)wherethe professorsin“HumanitiesandArtsplusSocialSciencesaremorepronetouseSocialMediafortheir pedagogicalaffordances”(p.229). Regardingagegroups,theonlyitemforwhichtherewerestatisticallysignificantdifferences wasSI5,wherethegrouphaving28to39yearsoldpresentsahigheraveragerankthantheothers, meaningthatyoungerUAprofessorsshowedatendencyofdevelopingmoreactivitiesusingMoodle inthefuture. RelatingtoFacebookacceptanceandthetwogendergroups,therewerefoundstatistically significantdifferencesonlyinthevariableATU1(p-value=0.010),wherethemalespresented, onaveragerank,highervalues(18.90)thanfemales(17.59),showingthatmentendedtousethis technologyintheTLcontextmorethanwomen. ConcerningYouTube,nostatisticalsignificantdifferencesamongthestudiedgroupsandforall theitemswerefound.

The Application of TAM for Assessing the Most used Technologies’ Acceptance Astheresultspresentedintheprevioussectionshowedthatthereweresomeitemsforwhichthere werestatisticalsignificantdifferencesintheMoodle’sacceptanceconcerninggenderandscientific areas(6inthecaseofgenderand4inthecaseofscientificarea),itwasdecided,inthecaseof thistechnology,tostudytherelationshipspresentedintheTAMseparatelyforthereferredgroups. RegardingFacebookandYouTube,theTAMwasappliedwithoutconsideringgroupsofindividuals, duetotheinexistenceofstatisticalsignificantdifferencesamongthem.Therefore,therewereanalysed

therelationshipsamongtheconstructsforsixTAMmodels,namely,Moodle’TAMforfemale, Moodle’TAMformale,Moodle’TAMforareaA,Moodle’TAMforareaB,Facebook’TAMand YouTube’TAM. TherelationshipsweremeasuredthroughPearsoncorrelationscoefficientsandregressionmodels. TheregressionmodelwasbasedintheoriginalTAM,representedinFigure1(ASUwasnotobject ofthisstudy),andthusconsistingintheonesimpleregressionandtwomultipleones.Themethod usedwasthestepwiseregression. Theexpressionsoftheregressionsperformedcanberepresentedas: ATU=f(PEOU,PU),PU=g(PEOU,SI),andPEOU=h(SI) wheref,gandhrepresentlinearfunctionsofthevariablesbetweenparenthesis. ThevaluesofATU,PU,PEOU,andSIwerecalculatedbycomputingthemeanvaluesofthe itemsthatcorrespondtoeachofthevariables.

Table 6. Results of Mann-Whitney and Kruskal-Wallis tests

Group Moodle

n Mean Rank p-value

PEOU1 Gender F 62 53.64 0.007 M 34 39.13 PEOU7 Gender F 62 52.48 0.043 M 34 41,25 PU4 Gender F 62 52.69 0.038 M 34 40.87 PU5 Gender F 62 52.74 0.029 M 34 40.76 ATU1 Gender F 62 53.10 0.019 M 34 40.12 Area A 43 42.56 0.043 B 53 53.32 ATU2 Area A 43 41.19 0.014 B 53 54.43 ATU3 Gender F 62 52.60 0.035 M 34 41.01 Area A 43 41.49 0.016 B 53 54.19 SI2 Area A 39 37.78 0.038 B 47 48.24 SI5 Agegroup [28,39] 23 58.33 0.020 [40,49] 39 49.53 [50,67] 33 39.00

Legend: F-Female; M-Male; A-Life and Health Sciences, Natural and Environmental Sciences, and Exact Sciences and Engineering; B-Social Sci-ences and Humanities.

Moodle Acceptance by Gender Table7presentsthePearsoncorrelationscoefficientsamongthefourTAMvariablesthatresulted forMoodlebygender. TheresultsfromTable7revealthatPUandATUarestronglycorrelated(Pearsoncorrelation coefficientbetween0.7and0.9)forbothgenders.ThecorrelationbetweenPUandPEOUisonly statisticallysignificantinthecaseofthefemalegroup,whilebetweenATUandPEOUthecorrelations arestatisticallysignificantforbothgroups,withintermediatevalues.CorrelationsbetweenSIand PUandSIandATU,althoughstatisticallysignificantforbothgenders,arestrongerinthecaseof males(intermediate,versusweakcorrelationsinthecaseoffemales). ThemodelthatresultedfromthecorrelationsshowninTable7andfromtheregressions characterizednext,areshowninFigure3.Notethataboveeacharrowisthevalueofthestandardized coefficient(ß)ofthecorrespondentregression,andnexttoeachdependentvariabletheR-Square (RSq)valueispresented. IntheFemaleregressionmodelforMoodle(n=53),thePU(β=0.705)andPEOU(β=0.262) werefoundtobesignificantpredictorsoftheATU,explaining73.2%ofthetotalvariance.ThePEOU (β=0.415)andtheSI(β=0.289)werealsofoundtobesignificantpredictorsofthePU,explaining 28.4%ofthetotalvariance(n=48). IntheMaleregressionmodelforMoodle(n=32),thePU(β=0.670)andPEOU(β=0.363) werefoundtobesignificantpredictorsofATU,asinthefemalecase,butexplaininglessofthetotal variance-68.4%.InwhatrespectstoPU,itwasfound,thatunlikewhathappenswithwomen,SI

Table 7. Pearson correlations coefficients among the four constructs of Moodle acceptance for females and for males

Females Males

PEOU PU ATU SI PEOU PU ATU SI

PEOU --- --- --- --- --- --- --- ---PU 0.451** (n=53) --- --- --- (n=32)0.213 --- --- ---ATU 0.474** (n=57) (n=58)0.821** --- --- (n=32)0.506** 0.736**(n=34) --- ---SI 0.108 (n=52) (n=53)0.342* (n=57)0.318* --- (n=28)0.166 0.558**(n=29) (n=29)0.589** ---Legend: *Correlation is significant at the 0.05 level (2-tailed); **Correlation is significant at the 0.01 level (2-tailed).

(β=0.575)wastheonlyvariablethatwasconsideredintheregression(n=28),whichexplained 33.1%ofthetotalvariance.

Inthemodelsforbothgenders,thecoefficientsoftheregressionsPEOU=h(SI)arenot statisticallysignificant,thusindicatingalackofalinearrelationshipbetweenthosevariables.

Moodle Acceptance by Scientific area

Table8presentsthePearsoncorrelationscoefficientsamongthefourTAMvariablesofMoodle byscientificarea,usingthesamenotationasabove:AreaA-LifeandHealthScience,Naturaland EnvironmentalScience,andExactSciencesandEngineeringandAreaB-SocialSciencesand Humanities. TheresultsofcorrelationsforAreaAandforAreaBarenotverydifferent.Infact,ashappened withthepreviousanalysis,thelargerandmostsignificantcorrelationsarebetweenthevariablesPU andATU.ThecorrelationsbetweenPUandPEOUareweakforbothareasandbetweenSIandPU areintermediatealsoforbothareas.Forthepairs‘ATUandPEOU’and‘SIandATU’,AreaAshows highercorrelationsthanAreaB. RegardingSIandPEOU,ashappenedwithbothgenders,thecorrelationswerenotstatistically significantlydifferent.ThemodelthatresultsfromthecorrelationsshowninTable8andfromthe regressionscharacterizednext,areshowninFigure4. IntheScientificAreaAregressionmodelforMoodle(n=37),thePU(β=0.612)andPEOU (β=0.384)werefoundtobesignificantpredictorsofATU,explaining69.7%ofthetotalvariance. Intheconsideredmodel(n=34),PUisonlyexplainedbySI(β=0.489),withatotalvariance explainedof23.9%. ConsideringtheScientificAreaBregressionmodelforMoodle(n=48),thePU(β=0.794) andPEOU(β=0.177)weresignificantpredictorsofATU,explaining75.4%ofthetotalvariance. ThePEOU(β=0.316)andSI(β=0.431)weresignificantpredictorsofPUexplaining29.3%of thetotalvariance(n=42). Aswiththemodelsforbothgenders,thecoefficientsoftheregressionsPEOU=h(SI)arenot statisticallysignificant,thusindicatingalackofalinearrelationshipbetweenthosevariables. InthecaseoftheMoodle,inallthefourmodelsstudied(Female,Male,AreaA,andArea B),thecorrelationsbetweenPUandATUarestrong,positiveandsignificant.Thisresultisinline withthestudyfromEscobar-RodriguezandMonge-Lazano(2012),whichindicatesthathavingthe perceptionthatMoodleincreasestheworkperformance,hasapositiveinfluenceontheintentionto useit.ConcerningthePEOU,ithasapositivecorrelationwithATU,againagreeingwiththeresults ofthestudyEscobar-RodriguezandMonge-Lazano(2012).ThecorrelationbetweenPUandPEOU

Table 8. Pearson correlations coefficients among the four constructs of Moodle acceptance for the two groups of scientific areas

Area A Area B

PEOU PU ATU SI PEOU PU ATU SI

PEOU --- --- --- --- --- --- --- ---PU 0.373* (n=37) --- --- --- (n=48)0.331* --- --- ---ATU 0.559** (n=40) 0.717**(n=40) --- --- (n=49)0.397** (n=52)0.858** --- ---SI 0.162 (n=37) 0.459**(n=36) (N=39)0.463** --- (n=43)0.074 (n=52)0.437** (n=46)0.356* ---Legend: *Correlation is significant at the 0.05 level (2-tailed); **Correlation is significant at the 0.01 level (2-tailed).

ispositiveandstatisticallysignificant(exceptinthecaseofmales).Thisresultisonlypartiallyin linewiththesamestudy,wherethisrelationshipisnotstatisticallysignificant.

Facebook and YouTube Acceptance

TheresultsobtainedforFacebookandYouTubearepresentedinthissubsection,inthesamewayas werepresentedforMoodlebut,aswasalreadymentioned,withoutsubdividingtheoriginalsample. Table9presentsthePearsoncorrelationscoefficientsamongthefourTAMvariablesthatresulted forFacebookandYouTube. TheresultsfromTable9revealthatPUandATUhavestrongstatisticallysignificantcorrelation values,bothforFacebookandYouTube,whichareverysimilar.ConcerningYouTube,thereisanother statisticallysignificantcorrelationvalue(moderate)andisbetweenPUandPEOU.Thelownumber ofvariablescorrelatedforthesetechnologiescanbeexplainedbythelowernumberofrespondents tothequestionsrelatedtothem.ThemodelsthatresultedfromthecorrelationsshowninTable9and fromtheregressionscharacterizednext,areshowninFigure5. AccordingtothemodelsobtainedforFacebookandYouTube,thePUwastheonlyindependent variableincludedintheregressionsthatexplainedATU,thatturnedouttobesimplelinearones. TheregressionforFacebook(n=30)presentsaβ_PU-Facebookof0.658andatotalvarianceexplained of43.2%(R2

Facebook)whiletheoneforYouTube(n=22)presentsaβPU-YouTubeof0.668andatotal varianceexplainedof44.6%(R2

YouTube).

Figure 4. Models obtained for Moodle in the UA, by scientific area

Table 9. Pearson correlations coefficients among the four constructs of Facebook and YouTube acceptance

Facebook YouTube

PEOU PU ATU SI PEOU PU ATU SI

PEOU --- --- --- --- --- --- --- ---PU 0.172 (n=30) --- --- --- (n=23)0.461* --- --- ---ATU 0.153 (n=32) (n=33)0.673** --- --- (n=23)0.316 (n=22)0.668** --- ---SI -0.211 (n=28) (n=29)0.005 (n=32)-0.100 --- (n=19)-0.069 (n=19)0.415 (n=20)0.118 ---Legend: *Correlation is significant at the 0.05 level (2-tailed); **Correlation is significant at the 0.01 level (2-tailed).

Accordingtothemultipleregressionsjustpresented,itwasverifiedthat,intheTLcontext, thePEOUandPUconstructsareimportantdeterminantsoftheacceptanceofMoodle.Thisresult isinaccordancewithEscobar-RodriguezandMonge-Lazano(2012,p.1086),thatreferringtothe acceptanceofMoodle,mention:“twoimportantdeterminantstoanalysewhatcausepeopletoaccept orrejectinformationtechnologyareperceivedusefulnessandperceivedeaseofuse”.Concerning FacebookandYouTube,theregressionresultsonlypointedoutPUasadeterminantofthetechnologies’ acceptance.Thiscouldbeexplainedbythefactthattherearelessrespondentsansweringtothose technologies,meaningthattheyarenotsousedandmakingmoredifficulttodrawconclusionsabout theiracceptance.Ontheotherhand,thesmallsampledimensioncanbeaffectingthesignificance ofthePEOUvariableinthemodels.

Characterization of the use of MOOCs and MOOCs Platforms

Themajorityoftherespondents(55;56.7%)didnotknowtheMOOCconcept.Fromthosewho reportedknowingtheconcept(42),42.9%havealreadyaccessedMOOCplatforms,23.8%(10) attendedtoatleastoneMOOC,and4.8%(2)collaboratedonthedevelopmentofatleastoneMOOC. Table10relatestoMOOCplatformsandpresentsthenumberofrespondentsthatreportedthey knew,consulted,attended,andusedthereferredplatforms. Courserawasthemostknownplatform,being,alsotheonemoreconsultedandattended. Itshouldbenoticedthatfromthoserespondentsthatknewtheconcept57.1%(24)wouldlike todevelopaMOOC,31.0%(13)didnothaveanopinionaboutit.Thisfactshouldbeconsidered becauseitcanrevealthatprofessorsdonotknowthecontextandtheconceptsufficiently,inorder toattendtoandcollaborateintheconceptionofMOOCs.

Figure 5. Models obtained for Facebook and YouTube in the UA

Table 10. Use of MOOC platforms

MOOC platform Know Consulted Attended Collaborated

Coursera 16 15 5 1

EdX 6 3 2 0

Others* 4 3 2 1

CONCLUSIONS Inthisstudy,theuseandacceptanceoftechnologiesbyprofessorsinHigherEducationInstitution (HEI)wereanalysed.ThetechnologiesidentifiedasmostusedintheTeachingandLearning(TL) processinHEwereMoodle,FacebookandYouTube.Thestudyonthetechnologiesacceptanceby theprofessorsintheUniversityofAveirowasimplementedthroughtheapplicationofaquestionnaire basedontheTAM. TheresultsofthequestionnairepointedoutthatingeneralMoodle,FacebookandYouTube werewellacceptedbytherespondents. Whentheacceptance’itemsappliedtoFacebookandYouTubewereanalysed,theydonotshow anystatisticalsignificantdifferencesamonggroupsofrespondentsbasedongender,scientificarea andage,probablyrevealingthattheuseofthesetechnologiesisalreadywidespreadintheTLcontext. RegardingMoodle,therewerefoundstatisticalsignificantdifferencesinsomeitems,withfemales presenting,onaveragerank,highervaluesthanmales,andtheSocialSciencesandHumanitiesarea presenting,onaveragerank,highervaluesthantheotherarea. PerceivedusefulnesspresentedastrongcorrelationwithattitudetowardusingMoodle,while concerningFacebookandYouTube,thereferredcorrelationwasmoderate. Accordingtotheresultsofmultipleregressions,perceivedusefulnessandperceivedeaseofuse aretwoimportantdeterminantsoftheMoodle’sacceptance,whileregardingFacebookandYouTube, theonlydeterminantoftheiracceptanceistheperceivedusefulness. ResultsalsoshowedthatthemajorityoftheprofessorsdidnotknowtheconceptofMOOCs, buttheonesthatknowit,areawareofCourseraandEdXplatforms,andwouldliketodevelopa MOOCinthefuture. ThisstudyislimitedtoonlyoneHEI.Futureworkshouldbedoneinordertoexpandthestudyto othersHEIs,comparingtheresultsandconcludingaboutlargerpopulations.Thecomparisonofthe acceptancebetweenMoodle,FacebookandYouTube,whichcouldnotbeperformedduetothesmall numberofrespondentsusingthethreetechnologies,canhelptounderstandhowtheyarebeingused andtoexplorethedifferencesinordertocontributeforabetteruseofeachofthemintheTLprocess. Astheresultsofthisworkprovideinsightsonfactorsthatcontributetotheintentiontoadopt technologiesintheTLcontextinHigherEducation,thestudyisconsideredvaluablenotonlyfor researchersinthearea,asforprofessorsthatwanttodeveloptheimplementationoftechnologiesin theiracademicenvironment. ACKNOwLEDGEMENT ThisresearchwassupportedbythePortugueseNationalFundingAgencyforScience,Researchand Technology(FCT),withintheCenterforResearchandDevelopmentinMathematicsandApplications (CIDMA),projectUID/MAT/04106/2019.

REFERENCES

Ajjan,H.,&Hartshorne,R.(2008).InvestigatingfacultydecisionstoadoptWeb2.0technologies:Theoryand empiricaltests.The Internet and Higher Education,11(2),71–80.doi:10.1016/j.iheduc.2008.05.002

Al-Busaidi,K.A.,&Al-Shihi,H.(2012).Criticalfactorsinfluencinginstructors’acceptanceanduseofLearning ManagementSystems.InHigherEducationInstitutionsandLearningManagementSystems:Adoptionand Standardization(pp.116-140).Hershey,PA:IGIGlobal.

Al-Samarraie,H.,&Saeed,N.(2018).Asystematicreviewofcloudcomputingtoolsforcollaborativelearning: Opportunitiesandchallengestotheblended-learningenvironment.Computers & Education,124,77–91. doi:10.1016/j.compedu.2018.05.016

Anderson,P.(2007).WhatisWeb2.0?Ideas,technologiesandimplicationsforeducation.Report,1(1),1–64. doi:10.1530/jrf.0.0680001

Arkorful,V.,&Abaidoo,N.(2015).Theroleofe-learning,advantagesanddisadvantagesofitsadoptionin highereducation.International Journal of Instructional Technology and Distance Learning,12(1),29–42. Baker,P.M.A.,Bujak,K.R.,&DeMillo,R.(2012).Theevolvinguniversity:Disruptivechangeandinstitutional innovation.Procedia Computer Science,14,330–335.doi:10.1016/j.procs.2012.10.037

Bennett,S.,Bishop,A.,Dalgarno,B.,Waycott,J.,&Kennedy,G.(2012).ImplementingWeb2.0technologies in higher education: A collective case study. Computers & Education, 59(2), 524–534. doi:10.1016/j. compedu.2011.12.022

Blackmon,S.,&Major,C.(2017).WhereforeartthouMOOC:DefiningMassiveOpenOnlineCourses.Online

Learning Journal,21(4),195–221.

Campanella,S.,Dimauro,G.,Ferrante,A.,Impedovo,D.,Impedovo,S.,Lucchese,M.G.,&Trullo,C.A.et al. (2008).E-learningplatformsintheItalianUniversities:ThetechnologicalsolutionsattheUniversityofBari.

WSEAS Transactions on Advances in Engineering Education,5(1),12–19.

Chen,H.H.,Lee,M.C.,Wu,Y.L.,Qiu,J.Y.,Lin,C.H.,Tang,H.Y.,&Chen,C.H.(2012).Ananalysisof Moodleinengineeringeducation:TheTAMperspective.InPaper presented at theIEEE International Conference

on Teaching, Assessment, and Learning for Engineering, TALE 2012.doi:10.1109/TALE.2012.6360324

Coates,H.,James,R.,&Baldwin,G.(2005).Acriticalexaminationoftheeffectsoflearningmanagement systemsonuniversityteachingandlearning.Tertiary Education and Management,11(1),19–36.doi:10.1080 /13583883.2005.9967137 Costa,C.,Alvelos,H.,&Teixeira,L.(2015).TheMoodleplatform:astudyinahighereducationPortugueses Institution.InM.M.Cruz-Cunha,F.Moreira,&J.Varajão(Eds.),ImprovingOrganizationalEffectivenesswith EnterpriseInformationSystems(pp.13–30).Hershey,PA:IGIGlobal.doi:10.4018/978-1-4666-8368-6.ch002 Costa,C.,Teixeira,L.,&Alvelos,H.(2014).TheimpactofWeb2.0intheteachingandlearningprocess.InM. M.Cruz-Cunha,F.Moreira,&J.Varajão(Eds.),HandbookofResearchonEnterprise2.0:Technological,Social, andOrganizationalDimensions(pp.346–363).Hershey,PA:IGIGlobal.doi:10.4018/978-1-4666-4373-4.ch017 Daniel,J.,Cano,E.V.,&Cervera,M.G.(2015).ThefutureofMOOCs:Adaptivelearningorbusinessmodel?

International Journal of Educational Technology in Higher Education,12(1),64–73.

Danyaro,K.U.,Jaafar,J.,DeLara,R.A.A.,&Downe,A.G.(2010).AnevaluationoftheusageofWeb2.0 amongtertiarylevelstudentsinMalaysia.Paper presented at the International Symposium on Information

Technology - Visual Informatics, ITSim’10.

Davis,F.D.(1986).ATechnologyAcceptanceModelforempiricallytestingnewend-userinformationsystems: theoryandresults[Doctoraldissertation].MITSloanSchoolofManagement.

Davis,F.D.(1989).PerceivedUsefulness,PerceivedEaseofUse,andUserAcceptanceofInformation Technology.Management Information Systems Quarterly,13(3),319–340.doi:10.2307/249008

Davis,F.D.,Bagozzi,R.P.,&Warshaw,P.R.(1989).Useracceptanceofcomputertechnology:Acomparison oftwotheoreticalmodels.Management Science,35(8),982–1003.doi:10.1287/mnsc.35.8.982

Ebner,M.,Lienhardt,C.,Rohs,M.,&Meyer,I.(2010).MicroblogsinHigherEducation–Achanceto facilitateinformalandprocess-orientedlearning?Computers & Education,55(1),92–100.doi:10.1016/j. compedu.2009.12.006 Ekúndayò,O.T.,&Tuluri,F.(2011).LearnerManagementSystemsandEnvironments,implicationsforpedagogy andapplicationstoresourcepoorenvironments.InS.G.F.Lazarinis&E.Pearson(Eds.),HandbookofResearch onE-LearningStandardsandInteroperability:FrameworksandIssues(pp.499–525).Hershey,PA:IGIGlobal. doi:10.4018/978-1-61692-789-9.ch025 Epelboin,Y.(2013).MOOC in Europe.UPMC-SorbonneUniversité. Escobar-Rodriguez,T.,Carvajal-Trujillo,E.,&Monge-Lozano,P.(2014).Factorsthatinfluencetheperceived advantagesandrelevanceofFacebookasalearningtool:AnextensionoftheUTAUT.Australasian Journal of Educational Technology,30(2),136–151.doi:10.14742/ajet.585 Escobar-Rodriguez,T.,&Monge-Lozano,P.(2012).TheacceptanceofMoodletechnologybybusiness administrationstudents.Computers & Education,58(4),1085–1093.doi:10.1016/j.compedu.2011.11.012 Galan,M.,Lawley,M.,&Clements,M.(2015).Socialmedia’suseinpostgraduatestudents’decision-making journey:Anexploratorystudy.Journal of Marketing for Higher Education,25(2),287–312.doi:10.1080/088 41241.2015.1083512

Garcia,F.A.,Martinez,U.F.,Lechuga,M.L.,Hernandez,F.A.L.,Perez,M.S.,&Marin,M.R.(2015). FrequencyofuseofLearningContentManagementSystems.AMoodlevs.Sakaicomparisonforstudieswith economicprofile.Revista de Investigación Educacional,13(1),69–87.

Grosseck,G.(2009).Touseornottouseweb2.0inhighereducation?Procedia: Social and Behavioral Sciences,

1(1),478–482.doi:10.1016/j.sbspro.2009.01.087

Halic,O.,Lee,D.,Paulus,T.,&Spence,M.(2010).Toblogornottoblog:Studentperceptionsofblog effectivenessforlearninginacollege-levelcourse.The Internet and Higher Education,13(4),206–213. doi:10.1016/j.iheduc.2010.04.001

Hew,K.F.,&Cheung,W.S.(2014).Students’andinstructors’useofMassiveOpenOnlineCourses(MOOCs): Motivationsandchallenges.Educational Research Review,12,45–58.doi:10.1016/j.edurev.2014.05.001 Holmes,K.A.,&Prieto-Rodriguez,E.(2018).StudentandstaffperceptionsofaLearningManagementSystem forblendedlearninginteachereducation.Australian Journal of Teacher Education,43(3),21–34.doi:10.14221/ ajte.2018v43n3.2

Holotescu,C.,&Grosseck,G.(2009).Usingmicrobloggingtodeliveronlinecourses.Case-study:Cirip.ro.

Procedia: Social and Behavioral Sciences,1(1),495–501.doi:10.1016/j.sbspro.2009.01.090

Hsu,Y.-C.,&Ching,Y.-H.(2012).Mobilemicroblogging:UsingTwitterandmobiledevicesinanonlinecourse topromotelearninginauthenticcontexts.International Review of Research in Open and Distance Learning,

13(4),211–227.doi:10.19173/irrodl.v13i4.1222

Jimoyiannis,A.,Tsiotakis,P.,Roussinos,D.,&Siorenta,A.(2013).PreparingteacherstointegrateWeb2.0in schoolpractice:TowardaframeworkforPedagogy2.0.Australasian Journal of Educational Technology,29(2), 248–267.doi:10.14742/ajet.157

Jung,Y.,&Lee,J.(2018).Learningengagementandpersistenceinmassiveopenonlinecourses(MOOCS).

Computers & Education,122,9–22.doi:10.1016/j.compedu.2018.02.013

Kear,K.,Woodthorpe,J.,Robertson,S.,&Hutchison,M.(2010).Fromforumstowikis:Perspectivesontools forcollaboration.The Internet and Higher Education,13(4),218–225.doi:10.1016/j.iheduc.2010.05.004 Kop,R.,Fournier,H.,&Mak,J.S.F.(2011).Apedagogyofabundanceorapedagogytosupporthumanbeings? Participantsupportonmassiveopenonlinecourses.International Review of Research in Open and Distance

Learning,12(7),74–93.doi:10.19173/irrodl.v12i7.1041

Liu,Z.,Cheng,H.N.,Liu,S.,&Sun,J.(2017).Discoveringthetwo-steplagbehavioralpatternsoflearnersin thecollegeSPOCplatform.International Journal of Information and Communication Technology Education,

Liyanagunawardena,T.R.,Adams,A.A.,&Williams,S.A.(2013).MOOCs:Asystematicstudyofthe publishedliterature2008-2012.International Review of Research in Open and Distance Learning,14(3), 202–227.doi:10.19173/irrodl.v14i3.1455

Manca,S.,&Ranieri,M.(2016).Facebookandtheothers.PotentialsandobstaclesofSocialMediaforteaching inhighereducation.Computers & Education,95,216–230.doi:10.1016/j.compedu.2016.01.012

Moore,J.L.,Dickson-Deane,C.,&Galyen,K.(2011).E-Learning,onlinelearning,anddistancelearning environments: Are they the same? The Internet and Higher Education, 14(2), 129–135. doi:10.1016/j. iheduc.2010.10.001 North-Samardzic,A.,&Jiang,B.(2015).AcceptanceanduseofMoodlebystudentsandacademics.Paper presentedattheTwenty-firstAmericasConferenceonInformationSystems. O’Reilly,T.(2005).WhatisWeb2.0.Designpatternsandbusinessmodelsforthenextgenerationofsoftware. Retrievedfromhttp://oreilly.com/web2/archive/what-is-web-20.html Ornellas,A.,&Carril(2014).Amethodologicalapproachtosupportcollaborativemediacreationinane-learning highereducationcontext,Open Learning: The Journal of Open, Distance and e-Learning, 29(1),59-71. Oum,S.,&Han,D.(2011).Anempiricalstudyofthedeterminantsoftheintentiontoparticipateinuser-createdcontents(UCC)services.Expert Systems with Applications,38(12),15110–15121.doi:10.1016/j. eswa.2011.05.098

Padilla-Meléndez,A.,Águila-Obra,A.R.,&Garrido-Moreno,A.(2013).Perceivedplayfulness,gender differencesandtechnologyacceptancemodelinablendedlearningscenario.Computers & Education,63, 306–317.doi:10.1016/j.compedu.2012.12.014

Padilla-Meléndez,A.,Águila-Obra,A.R.,&Garrido-Moreno,A.(2015).Usingmoodleinteaching-learning processesinbusinessmanagement:Thenewprofileofeheastudent.Educacion XX1, 18(1),125-146. Paulsen,M.(2003).ExperienceswithLearningManagementSystemsin113EuropeanInstitutions.Journal of

Educational Technology & Society,6(4),134–148.

Pinho,J.C.M.R.,&Soares,A.M.(2011).Examiningthetechnologyacceptancemodelintheadoptionof socialnetworks.Journal of Research in Interactive Marketing,5(2),116–129.

Rueda,R.A.S.,Garcia,J.L.L.,&Silva,H.F.R.(2017).Perspectivesofthestudentsabouttheuseofnetworks inthehighereducationalprocess.Revista de Comunicación ‘Vivat Academia’, 19(139),53-66.

Sánchez,R.A.,&Hueros,A.D.(2010).MotivationalfactorsthatinfluencetheacceptanceofMoodleusing TAM.Computers in Human Behavior,26(6),1632–1640.doi:10.1016/j.chb.2010.06.011

Thongmak,M.(2014).Factorsdetermininglearners’acceptanceofFacebookinahighereducationclassroom.

Knowledge Management and E-Learning,6(3),316–331.

Tschofen,C.,&Mackness,J.(2012).Connectivismanddimensionsofindividualexperience.International

Review of Research in Open and Distance Learning,13(1),124–143.doi:10.19173/irrodl.v13i1.1143

UA.(2018).Factos&números.Retrievedfromhttp://www.ua.pt/page/151

Venkatesh,V.,Morris,M.G.,Davis,G.B.,&Davis,F.D.(2003).Useracceptanceofinformationtechnology: Towardaunifiedview.Management Information Systems Quarterly,27(3),425–478.doi:10.2307/30036540 Wingo,N.P.,Ivankova,N.V.,&Moss,J.A.(2017).Facultyperceptionsaboutteachingonline:Exploringthe literatureusingtheTechnologyAcceptanceModelasanorganizingframework.Online Learning,21(1),15–35. doi:10.24059/olj.v21i1.761 Xu,B.,&Yang,D.(2015).StudyPartnersRecommendationforxMOOCslearners.Computational Intelligence and Neuroscience,(15):1–10.doi:10.1155/2015/832093 Zimnas,A.,Kleftouris,D.,&Valkanos,N.(2009).IDEL-AsimpleInstructionalDesigntoolforE-Learning.