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The recommendation approach is based on learning objects repositories, linked data and video repositories.
We present three recommendation approaches: based on learning objects repositories; based on linked data sets; and video recommendation based on YouTube.
So, we present three recommendation approaches: based on learning objects repositories; based on linked data sets; and video recommendation based on YouTube.
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Other approaches include those which analyze 3D objects using analytical functions/transforms [24, 42] and also those based on learning [29].
We followed (V) methodological rigor during its development and evaluation, (VI) analyzing existing solutions and basing the proposal on learning objects repositories, linked data and video repositories and (VII) ensuring results publication.
Our approach is based on learning by demonstration.
In the field of Technology-enhanced Learning (TeL), social tagging has been applied to Learning Object Repositories (LORs) mainly as a means:(a) to offer an alternative way of classifying the Learning Objects (LOs) based on the tag vocabulary created by the end-users of the LOs, and (b) to facilitate the enhancement of LOs' descriptions via collaborative tagging.
For example, in our case, learning objects are created based on the historical relative value of certain objects during periods of trade between communities and the HBC.
It forms a neighborhood of learners based on their profile and discovers associations among learning objects (through association rule mining) that led to identifying the useful learning objects visited by other similar learners.
In [23] is proposed BROAD-RS architecture that enables automation of Learning Objects recommendation process based on students interests and according to the class goals specified by the teacher.
As a future work, we will extend the system to additionally provide recommendations of learning objects from the web based on other similar learners' profiles.
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