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- W2807246871 abstract "Learning analytics has attracted a remarkable attention in the last years due to its potential to support and improve the learning experience. The first step of the learning analytics process is the educational data collection, therefore it is highly significant to track and collect the big educational data generated (learning data, assessment data, communication data etc). Assessment data represent an important proportion of the educational data generated. When we focus on assessment, we can launch a new source of data that can be analyzed and hence contribute to the improvement of the field of learning analytics. In this paper, we will focus on assessment data by proposing an assessment analytics architecture. This architecture uses the semantic web technologies and focus essentially on analyzing the set of assessment activities, the assessment result and the assessment context. These assessment data are conceived and annotated via an ontological model, inspired from the xAPI data model, which enable performing logic rules for supporting inference mechanisms related to assessment analytics and especially concerning the concrete level of the assessment actor and the level of difficulty of the assessment object." @default.
- W2807246871 created "2018-06-13" @default.
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- W2807246871 date "2017-11-01" @default.
- W2807246871 modified "2023-10-16" @default.
- W2807246871 title "A Semantic Web Based Architecture for Assessment Analytics" @default.
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- W2807246871 doi "https://doi.org/10.1109/ictai.2017.00181" @default.
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