Machine Learning Based Improved Recommendation Model for E-learning
Hadi Ezaldeen, Rachita Misra, Rawaa Alatrash, Rojalina Priyadarshini · 2019
E-learning has gained importance due to the need of re-skilling, up-skilling, augmenting normal education system by providing knowledge delivery in virtual environment. A good E-learning system needs to have a customizable process, initiated by learner profile and dependent on training requirements. It can deliver desired results when it is integrated into day-to-day learning patterns providing a clear competitive edge for e-learning platforms. Learning needs to be relevant to the context of the concept. Learners in any learning environment differ in their learning style, level of knowledge, preferences, and attempts in solving and addressing problems when their expectations are not met. The current study illustrates and discusses a framework how machine learning (ML) technologies can be applied to e-learning systems to help the learner in selecting an appropriate learning course. Courses that require special privileges to be accessed can be handled according to the learner profile and learner's categories. In this paper, we present a comprehensive survey of current e-learning systems. Further an intelligent e-learning framework has been presented. The authors have applied 2 machine learning methods to the proposed framework and outcomes are discussed.