With the evolution of the Internet, modern
learning patterns have improved significantly, helping
to bring knowl-edge wherever the learner requires it.
When considering the knowledge available in public
domains, the speed of information filtering, and
information exchangeability, traditional educational
frameworks do have not sufficient powers to move with
today’s world. As a result, emerging trends indicate that
internet-based E-Learning technologies will be used to
acquire vast amounts of knowledge. Also with this
evolution, self-learning is highly encouraged in the
present era. Since there are a lot of internet resources,
when someone looks for educational content there could
be many similar responses, and this is where we may
need personalized recommendations to make life easy.
Compared to the existing research of personalized
recommendations in theeducation domain, there is a gap
in providing plenty of relevant personalized educational
resources to a student for self-learning. Thereby, the
research, Goal-driven Personalized Educational Content
Recommendation System for Self-learning is based on
the personal competency level and the areas that need to
beimproved of a student. The outcome of the research
will provide insight into the perspectives and challenges
of personalized recommendation-based self-learning
based, primarily web-based learning, and provides a
path to identifying appropriate solutions using Topic
Modeling for key challenges in the education domain,
with personalized recommendations for future
research.
Keywords :
Machine Learning, Topic Modeling, Text Extraction