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Charlotte Van Petegem 2024-02-13 16:55:37 +01:00
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@ -290,7 +290,20 @@ The FPGE platform by\nbsp{}[cite/t:@paivaManagingGamifiedProgramming2022] offers
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Learning analytis and educational data mining stand at the intersection of computer science, data analytics and education.
Learning analytics and educational data mining stand at the intersection of computer science, data analytics and the social sciences, and focuses on understanding and improving learning.
They are made possible by the increased availability of data about students who are learning, due to the increasing move of education to digital platforms\nbsp{}[cite:@romeroDataMiningCourse2008].
They can also serve different actors in the educational landscape: they can help learners directly, help teachers to evaluate their own teaching, allow educational institutions to guide their decisions, or even allow governments to take on data-driven policies\nbsp{}[cite:@fergusonLearningAnalyticsDrivers2012].
Learning analytics and educational data mining are overlapping fields, but in general, learning analytics is seen as focusing on the educational challenge, while educational data mining is more focused on the technical challenge\nbsp{}[cite:@fergusonLearningAnalyticsDrivers2012].[fn::
The analytics focusing on governments/educational instutions is also called academic analytics.
]
[cite/t:@chattiReferenceModelLearning2012] defined a reference model for learning analytics based on four dimensions:
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- What data is gathered and used?
- Who is targeted by the analsis?
- Why is the data analysed?
- How is the data analysed?
** Structure of this dissertation
:PROPERTIES: