Prediction of Student's Adaptivity Level of Online Learning

Online Education has become a buzzword since the COVID-19 pandamic hit the world unexpectedly. Many educational institutions went online to continue educational activities for students of all profiles.

Students of several levels also faced many difficulties when they got introduced to online education. It is hence important for the decision-makers of educational institutions to be informed about the effectiveness of online education so that they can take further steps to make it more beneficial for the students.

This online tool serves as a simple way to predict how well a student can adapt to online learning based on a set of parameters. Educators can then pay additional attention to these students to ensure they are not left behind.

Gender Male
Female

Instituition Type

Government institutions offer subsidised programs while Non-government instituitions are not subsidised

Government
Non-government

IT Student Status Yes
No

Student Location in Town Yes
No

Load shedding in Student's Country

Load shedding (loadshedding) is a way to distribute demand for electrical power across multiple power sources. Load shedding is used to relieve stress on a primary energy source when demand for electricity is greater than the primary power source can supply.

During load shedding events, the building draws power from its secondary source(s) rather than from the utility. A typical secondary source is on-site diesel generators, or on-site or contracted solar photovoltaics or wind-based renewable power.

If unsure, select "Low".

High
Low

Internet Type used by student Wifi
Mobile Data

Does instituition use a Learning Management System (LMS)? Yes
No

Education Level of student

School refers to students at the pre-tertiary education level, college refers to students at the bachelor level & university refers to post-bacherlor level

Education level:

Financial Condition

33.3 percentile = Low, 33.3 to 66.6 percentile = Mid, above 66.6 percentage = High

Financial condition:

Class Duration Class duration (in hours):

Device used by student Device used:

Mobile Network used by student Mobile network type:

Student's Age