{"title":"Evaluation of Learner Performance During Practical Activities: An Experimentation in Computer Education","authors":"Rémi Venant, Philippe Vidal, J. Broisin","doi":"10.1109/ICALT.2016.60","DOIUrl":null,"url":null,"abstract":"This paper addresses learning analytics for evaluation of learners performance in remote laboratories. The objectives we identified to provide self-and social awareness while learners are practicing in their virtual learning environment are threefold: (1) the definition of a performance metric that requires no assessment tests, (2) the tracking of data to infer that metric in real time, and (3) the visualization of the performance metric without impacting learners' cognitive load. To support these needs, we propose (i) a metric related to the technical rightness of instructions carried out by learners, (ii) a generic learning analytics framework featuring an enriching engine able to infer indicators, and (iii) two distinct visualization tools. These proposals have been implemented in Lab4CE, our remote laboratory for computer education, and experimented in an authentic learning context. This experimentation showed that most students have significantly used both visualization tools, and that their usage decreased while the overall learners performance increased.","PeriodicalId":188900,"journal":{"name":"2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 16th International Conference on Advanced Learning Technologies (ICALT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICALT.2016.60","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
This paper addresses learning analytics for evaluation of learners performance in remote laboratories. The objectives we identified to provide self-and social awareness while learners are practicing in their virtual learning environment are threefold: (1) the definition of a performance metric that requires no assessment tests, (2) the tracking of data to infer that metric in real time, and (3) the visualization of the performance metric without impacting learners' cognitive load. To support these needs, we propose (i) a metric related to the technical rightness of instructions carried out by learners, (ii) a generic learning analytics framework featuring an enriching engine able to infer indicators, and (iii) two distinct visualization tools. These proposals have been implemented in Lab4CE, our remote laboratory for computer education, and experimented in an authentic learning context. This experimentation showed that most students have significantly used both visualization tools, and that their usage decreased while the overall learners performance increased.