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dc.contributor.advisorTrætteberg, Hallvard
dc.contributor.authorKarlsen, Herman Myrbråten
dc.date.accessioned2019-09-11T10:55:46Z
dc.date.created2018-06-16
dc.date.issued2018
dc.identifierntnudaim:18090
dc.identifier.urihttp://hdl.handle.net/11250/2615797
dc.description.abstractIn the Object-Oriented Programming course (TDT4100) at NTNU, a lot of data is being collected about how the students work on programming assignments. The data is collected through a Learning analytics extension for the Eclipse Integrated development environment (IDE). This extension displays the collected data back to the students, with the goal of stimulating reflection and self-evaluation regarding how they work on programming assignments. In this project, we explore the collected data using Learning analytics approaches. Through an iterative process, we conduct five experiments with the goal of identifying distinct programming modes. We identify the "struggling" mode and design algorithms to automatically detect this mode. In parallel to the experiments, we design and implement a tool for visualization and analysis, which will streamline our experiment process.en
dc.languageeng
dc.publisherNTNU
dc.subjectInformatikk, Kunstig intelligensen
dc.titleSupporting learning by means of learning analyticsen
dc.typeMaster thesisen
dc.source.pagenumber75
dc.contributor.departmentNorges teknisk-naturvitenskapelige universitet, Fakultet for informasjonsteknologi og elektroteknikk,Institutt for datateknologi og informatikknb_NO
dc.date.embargoenddate10000-01-01


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