Learning analytics for student modeling in virtual reality training systems: Lineworkers case

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dc.contributor.author Santamaría Bonfil, Guillermo
dc.contributor.author Ibáñez Espiga, María Blanca
dc.contributor.author Pérez Ramírez, Miguel
dc.contributor.author Arroyo Figueroa, Gustavo
dc.contributor.author Martínez Álvarez, Francisco
dc.date.accessioned 2020-10-09T11:25:44Z
dc.date.available 2023-01-01T00:00:05Z
dc.date.issued 2020-03-01
dc.identifier.bibliographicCitation Computers & Education (2020), 151
dc.identifier.issn 0360-1315
dc.identifier.uri http://hdl.handle.net/10016/31175
dc.description.abstract Live-line maintenance is a high risk activity. Hence, lineworkers require effective and safe training. Virtual Reality Training Systems (VRTS) provide an affordable and safe alternative for training in such high risk environments. However, their effectiveness relies mainly on having meaningful activities for supporting learning and on their ability to detect untrained students. This study builds a student model based on Learning Analytics (LA), using data collected from 1399 students that used a VRTS for the maintenance training of lineworkers in 329 courses carried out from 2008 to 2016. By employing several classifiers, the model allows discriminating between trained and untrained students in different maneuvers using three minimum evaluation proficiency scores. Using the best classifier, a Feature Importance Analysis is carried out to understand the impact of the variables regarding the trainees' final performances. The model also involves the exploration of the trainees' trace data through a visualization tool to pose nonobservable behavioral variables related to displayed errors. The results show that the model can discriminate between trained and untrained students, the Random Forest algorithm standing out. The feature importance analysis revealed that the most relevant features regarding the trainees' final performance were profile and course variables along with specific maneuver steps. Finally, using the visual tool, and with human expert aid, several error patterns in trace data associated with misconceptions and confusion were identified. In the light of these, LA enables disassembling the data jigsaw quandary from VRTS to enhance the human-in-the-loop evaluation.
dc.description.sponsorship First author thanks the program Catedras-CONACYT, Mexico for funding his research. Authors also thank Lucia Barrón for hervaluable commentaries for improving the manuscript and to L.A. Domínguez for designing Fig. 1 a. Icons employed in Fig. 2 weremade by Freepik, Flat Icons, Eucalyp, and photo3idea studio which are freely available at www.flaticon.com.This work was co-founded by the Madrid Regional Government, Spain through the Project e-Madrid-CM (P2018/TCS-4307)and by the Spanish Ministry of Science, Innovation and Universities through Project Smartlet (TIN2017-85179-C3-1-R). These twoprojects have also been co-founded by the Structural Funds (FSE and FEDER), Spain.
dc.language.iso eng
dc.publisher Elsevier
dc.rights © Elsevier, 2020
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Learning analytics
dc.subject.other Performance prediction
dc.subject.other Feature importance analysis
dc.subject.other Exploratory data analysis
dc.subject.other Virtual reality
dc.subject.other Academic-performance
dc.subject.other Simulator
dc.subject.other Affordances
dc.subject.other Prediction
dc.subject.other Displays
dc.title Learning analytics for student modeling in virtual reality training systems: Lineworkers case
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1016/j.compedu.2020.103871
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN2017-85179-C3-1-R
dc.relation.projectID Comunidad de Madrid. P2018/TCS-4307
dc.type.version acceptedVersion
dc.identifier.publicationissue 103871
dc.identifier.publicationtitle Computers & Education
dc.identifier.publicationvolume 151
dc.identifier.uxxi AR/0000026034
dc.contributor.funder Ministerio de Economía y Competitividad (España)
dc.contributor.funder Comunidad de Madrid
dc.affiliation.dpto UC3M. Departamento de Ingeniería Telemática
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Aplicaciones y Servicios Telemáticos (GAST)
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