Ricerc@Sapienza

The paper presents a study of the performance
variationsoftheBayesianmodelofpeerassessmentimplementedin
OpenAnswer, in terms of the grades prediction accuracy.
OpenAnswer (OA)modelsapeerassessmentsessionasaBayesian
network. For each student, a subnetwork contains variables
describingrelevantaspectsofboththeindividualcognitivestateand
the state of the current assessment session. Subnetworks are
interconnected to each other to obtain the final one. Evidence
propagated through the global network is represented by all the
gradesgivenbystudentstotheirpeers,togetherwithasubsetofthe
teacher’scorrections.Amongthepossibleaffectingfactors,thepaper
reportsabouttheinvestigationofthedependenceofgradesprediction
performance on the quality of the class, i.e., the average level of
proficiency of itsstudents,andon thenumberofpeersassessedby
eachstudent.Theresultsshowthatbothfactorsaffecttheaccuracyof
the inferred marks produced by the Bayesian network, when
comparedwiththeavailablegroundtruthproducedbyteachers.