Predicting student performance in a blended learning environment using learning management system interaction data
Purpose Student attritions in tertiary educational institutes may play a significant role to
achieve core values leading towards strategic mission and financial well-being. Analysis of
data generated from student interaction with learning management systems (LMSs) in
blended learning (BL) environments may assist with the identification of students at risk of
failing, but to what extent this may be possible is unknown. However, existing studies are
limited to address the issues at a significant scale.
achieve core values leading towards strategic mission and financial well-being. Analysis of
data generated from student interaction with learning management systems (LMSs) in
blended learning (BL) environments may assist with the identification of students at risk of
failing, but to what extent this may be possible is unknown. However, existing studies are
limited to address the issues at a significant scale.
Abstract
Purpose
Student attritions in tertiary educational institutes may play a significant role to achieve core values leading towards strategic mission and financial well-being. Analysis of data generated from student interaction with learning management systems (LMSs) in blended learning (BL) environments may assist with the identification of students at risk of failing, but to what extent this may be possible is unknown. However, existing studies are limited to address the issues at a significant scale.
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