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Analysing student behaviour when learning from video-based learning resources

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Analysing student behaviour when learning from video-based learning resources

César Córcoles

1st International Workshop on Technology-Enhanced Assessment, Analytics and Feedback (TEAAF2014)

Publicada em: Educação
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Analysing student behaviour when learning from video-based learning resources

  1. 1. Analysing student behaviour when learning from video-based learning resources César Córcoles (ccorcoles@uoc.edu) Dept of IT, Multimedia and Telecommunication Universitat Oberta de Catalunya The growingimportance of online videoasaneducational resource inonlineeducation environments,butalsoinblendedlearningscenariosandintraditional learningsituations, thanksfor example toflippedclassrooms,hasleadtothe needto analyze and measure how studentslearnfromthose videoresources. Inatraditional learningsettingagoodteacherwill be able to gathersome feedbackfromstudentactivity,both explicit(studentsasking questions) andimplicit(suchasbodylanguage andfacial expression),andadaptherteaching to that feedback,providingabetterlearningexperience.Whenstudentsare learningfrom videoresources thatfeedbackisall butlost,andwithitthe possibilitytoimprove the learning experience accordingly. There are twosidestothat necessary analysis:  Firstly,we need tounderstandparticularstudents’ itinerariesthroughasingle video resource,bothat the individualsession levelandthroughdifferentsessions. At the single sessionlevel we mayaskquestionssuchas“Is the learnerviewingit linearlyfrombeginningtoendor doesshe accesssome particularsegment?”,“Does she rewatchcertainsections?”,or“Doesshe pause the video(possiblybecauseshe is referencingotherlearningresources)?”. If we aggregate asingle student’sdifferent sessionswe mayaskquestionssuchas“Doeslearneractivityvaryfromherfirstvisitto hersecondor thirdone?”  Secondly,we alsoneedaglobal understandingof how avideoworks (answering questionssuchas“doesthe videohave sectionsthatare not as clearas expected?”). We have implementedalearninganalyticsgatheringtool forthe analysisof studentactivity whenlearningfrom onlinevideoeducational resources,thatwillbe extendedinorderto betterpresentandhelpanalyse those data.Wheneverastudentclicksthe playor pause buttonsor clicksto a differentpointinthe videotimeline (oranyeventtriggersthose behaviours) we registerthe time of the eventandanyconvenientparameters(suchasthe pointinthe videotimeline the studentjumpedto). For that purpose we use the Popcorn.js opensource JavaScriptlibrary1 .We have developeda WordPressplug-infordatagathering,allowingustoinsertaVimeovideowithlearning analyticssupportaseasilyasif we were to insertthe same videowithoutlearninganalytics support(the embeddingof YouTube videosand/orself-hostedvideos isnotcurrently supported,butitwill be inthe shortterm).The plug-inisfreelyavailable now.The plug-in should alsobe a reference implementationthatcan be easilyadaptedforotherContent ManagementSystems(CMSs) andLearningManagementSystems(LMSs),andshould alsobe 1 http://popcornjs.org/
  2. 2. easyto integrate with nomatterwhichauthenticationsolution otherlearninginstitutions employ. Currentlyreportinganddatavisualizationare verybasic.There isanefforttointegrate thissolutionwiththe currentclassroomenvironmentandimminentlearninganalytics platform at UOC. As mightbe expected,thereare otherinitiativesworkinginthe same direction.We canfindat leasttwoothereffortsinthe literature,one atthe edXMOOC platform,documentedinKimet al,2014, and anotherone at IonianUniversity(Chorianopoulos,2013). Our solutionprovides betterplatformindependence withregardto videohostingsolutions,LMSand/orCMS and otherauthentication platforms. Also,thesesolutionsfocusonpainting"attentionmaps"for the videos:whichsegmentswere viewedmost.Ourpresentsolutionallowsusto provide that same information,butalsolooksatstudentpausesandjumpsfromone pointinthe timeline to anotherone as importantaspectsinusage fromwhichimportantinformationcanbe extracted. The current implementationof the tool,as explainedabove,providesadatagathering solution,butlearninganalyticsmakessense onlyasameansto improve the teachingand learningprocess.Inthatline,ourfuture workincludesthe followingpaths:  We intendtoprovide aquantitative/qualitative mixedmethodology.Quantitative analysisof the data revealsusage patterns thatwouldbe hardto detectwith traditional qualitativeapproaches (ifonlybecause thoseapproachesdonotprovide goodcoverage for big,diverse populations).Teachersmayhave explaininghypothesis for those patterns,needingvalidation,ortheymayhave noexplanation.Once ausage patternhas beendetected(say,itisseenthat1% of studentspausesforasignificant amountof time aftera certainsegmentinthe videoandthenrewatchesit) our solutionallowsusto insertsome logicintothe playerthat asksstudents followingthat patterninthe future the purpose of those actions.  Our initial motivationwastouse the multimedialearningprincipleof self-explanation (see,forexample,Renkl etal,1998), statingthatstudentslearnbetterfrom multimediaresourceswhentheyare stimulatedtogenerate self explanationsfromthe conceptsor skillstheyare beingtaught.There isevidence thatexposingnon-self- explainingstudentstootherstudents'effective self explanationsturnsasignificant numberof themintoeffectiveself explainers,thusimprovingtheirlearning. Finally, there isalsoevidence that interrogatingstudentsimprovestheirlearning (Meanetal, 2009). Our solutionwill allowusinthe future topose the appropriate questionsto studentsaccordingtotheirlearnerprofile andusage patterns,be itinthe formof promptsfor self explanationoras formative orsummative assessment.  Finally,we intendto provide apersonalized learningexperience forstudents.We will be able to react to usage patterns andofferexercises andadditional learning resourcestailoredtothe particularstudentandlearningsituation. Bibliography Chorianopoulos,K.(2013). Collective intelligence withinwebvideo. Human-centricComputing and Information Sciences,3(1),1-16.
  3. 3. Kim,J.,Guo, P.J., Seaton,D.T., Mitros,P., Gajos,K. Z.,& Miller,R.C. (2014, March). Understandingin-videodropoutsandinteractionpeaksinonline lecture videos.In Proceedings of the first ACMconferenceon Learning@scale conference (pp.31-40). ACM. Means,B., Toyama,Y., Murphy, R.,Bakia,M., & Jones,K.(2009). Evaluationof Evidence-Based PracticesinOnline Learning:A Meta-AnalysisandReview of Online LearningStudies. US Departmentof Education. Renkl,A.,Stark,R.,Gruber,H., & Mandl,H. (1998). Learningfromworked-outexamples:The effectsof examplevariabilityandelicitedself-explanations. Contemporary educational psychology,23(1), 90-108.

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