Edited by Tabitha Naisiko, Wednesday, 30 Oct 2019, 14:31
Precisely, learning design
is theoretical while analytics are practical and deals with a) data, b) technology,
and c) pedagogy. Learning analytics, therefore, reflects the achievement of the
learning design and thus informs points and process of designing and
redesigning. The two broad categories of
analytic presented by Lockyer et al. (2013), are checkpoint and process analytics.
Having read the article, I realise that process analytic are student-oriented and
deals with teachers assessing how students are understanding the pedagogical
and technical contexts in which data is generated. Teacher bases on tasks given
to evaluate student understanding. On the other hand, Checkpoint analytic is
teacher-oriented, whereby the teacher evaluates the learning design if it is giving
the planned learning outcome.
As a lecturer, I find these classifications more useful than
the previous ones because these are very contextual in academia. The former to
me seemed more business-like and was dwelling more on data than the factors underlying the data.
Learning analytics and learning design
Precisely, learning design is theoretical while analytics are practical and deals with a) data, b) technology, and c) pedagogy. Learning analytics, therefore, reflects the achievement of the learning design and thus informs points and process of designing and redesigning. The two broad categories of analytic presented by Lockyer et al. (2013), are checkpoint and process analytics. Having read the article, I realise that process analytic are student-oriented and deals with teachers assessing how students are understanding the pedagogical and technical contexts in which data is generated. Teacher bases on tasks given to evaluate student understanding. On the other hand, Checkpoint analytic is teacher-oriented, whereby the teacher evaluates the learning design if it is giving the planned learning outcome.
As a lecturer, I find these classifications more useful than the previous ones because these are very contextual in academia. The former to me seemed more business-like and was dwelling more on data than the factors underlying the data.