Search results
Results from the Think 24/7 Content Network
Practical Implication – E-Learning could potentially shape the future of education by advancing the traditional classroom setting into the web. There is a need for the entire academic community to ensure that the factors of e-learning effectiveness are delivered adequately and the utilization of e-learning must be evaluated regularly.
In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP.
concerns involve e-learning course structure and its relations to students’ autonomy during e-learning courses, their prior knowledge and experience towards e-learning environments, and the communication process during e-learning environments, for instance, students- students dialogue and students- instructor dialogue.
for e-learning; and evaluation of teaching effectiveness using e-learning. Based on the findings, periodic updates and training on the new changes should be made to the university’s e-learning platforms, provision of timely technical support to academics in order to sustain positive user experiences of e-learning were recommended.
Salmon developed a five-stage model of e-learning and e-moderating that for some time has had a major influence where online courses and online discussion forums have been used. In her five-stage model, individual access and the ability of students to use the technology are the first steps to involvement and achievement.
This paper will connect e-learning educational/training courses delivery with lifelong learning (LLL). It will further analyze certain factors from the professional and educational point of view and provide recommendations on how to accelerate the implementation of LLL supported by e-learning.