Automatic identification and extraction of adaptive learning objects from traditional courseware

Abstract EN

e-Learning is becoming the learning system of the future.

However, many recent researches suggested replacing the "one size fits all" teaching model with the smarter model of adaptive learning in which the interpersonal differences between learners are considered the main shapers of lesson for a particular student. 
However, the main hurdle for the adaptive model is building the repository of micro learning objects (MLOs) with reasonable sizes suitable for reassembling into lessons in a way that is more suitable for a particular student.

The process of designing such MLOs is expensive and time-consuming, reasons with the least effect of dampening the model to the point that it may collapse.

Noteworthy, there is a wealth of digital learning contents and open-source educational curricula available online, but in the large granular traditional format.

Therefore, this research proposes a Text processing intelligent framework for automatically identifying, extracting, and, annotating, those MLOs out of open digital video contents and PowerPoint courses, which are the most popular media types.