Analysing student behaviour when learning from video-based learning resources César Córcoles 1st International Workshop on Technology-Enhanced Assessment, Analytics and Feedback (TEAAF2014)

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. 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. Kim,J.,Guo, P.J., Seaton,D.T., Mitros,P., Gajos,K. Z.,& Miller,R.C. (2014, March).
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