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Learning analytics for the development of adapted e‐assessment workflow system
Author(s) -
Bendaly Hlaoui Yousra,
Hajjej Fahima,
Jemni Ben Ayed Leila
Publication year - 2016
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.21770
Subject(s) - workflow , computer science , cloud computing , adaptation (eye) , analytics , ontology , learning analytics , workflow management system , learning management , world wide web , data science , database , philosophy , physics , epistemology , optics , operating system
Learning analytics (LA) is a significant area of the enhanced learning technology that has been emerged during the last decade. In this paper, we present a Cloud Adapted Workflow e‐Assessment System, called Cloud‐AWAS. This system makes use of the learning analytics in order to turn learners into more effective and better learners. Hence, Cloud‐AWAS could be seamlessly integrated into any learning management system. This system provides a generic e‐assessment workflow which is adapted to learner's profiles. We have started by creating a learner profile ontology based on extraction data from e‐assessment activities, file log and personal information. Then, we have defined three adaptation actions: Add Activity, Edit Activity, and Delete Activity, applied on the workflow assessment and using information extracted from learner profile ontology instances. Each action is applied according to a number of conditions. Finally, we present some results of the empirical evaluation of Cloud_AWAS. © 2016 Wiley Periodicals, Inc. Comput Appl Eng Educ 24:951–966, 2016; View this article online at wileyonlinelibrary.com/journal/cae ; DOI 10.1002/cae.21770