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DEFINITION ARGUMENT: ONLINE LEARNING

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DEFINITION ARGUMENT: ONLINE LEARNING

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Online learning refers to the kind of education where there is no close contact or face to face contact between the lecturer or the teacher and the students. Instead, there is an application of technology through application gadgets such as laptops and tablets to access the learning materials.

Key terms: online; it is using the internet to interact with one another.

Learning; refers to gaining knowledge that is being passed from one person to another.

Online learning enhances quick access to the learning material to the students as they open their learning machines with an internet connection and read from there. That means that it is an easy way of learning compared to the face to face form of education (Shalev-Shwartz pg. 107-120). The other concept of online learning is that it saves time of movement as one travels to a lecture room and access the knowledge. This is because one is in a position to access all that she needs for learning just by a click of a button. The other concept of online education is that it saves time. There is a lot of time wasted when moving to the lecture rooms and back, which are held in the case of online learning as one can learn from anywhere (Huda pg. 23-30).

On a negative note, there is a tremendous challenge to online learning as it depends much on the network and having digital gadgets. Therefore lack of those requirements may be a great challenge to online learning, and that has been the major problem affecting online education in this season of the coronavirus. In conclusion, it is evident that online learning is of great importance, and that is what is making it to be widely applied across the world.

Work cited

Huda, Miftachul, et al. “Big data emerging technology: insights into innovative environment for online learning resources.” International Journal of Emerging Technologies in Learning (iJET) 13.1 (2018): 23-36.

Shalev-Shwartz, Shai. “Online learning and online convex optimization.” Foundations and Trends in Machine Learning 4.2 (2011): 107-194.

 

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