Machine learning applications in Government and Education

Described below are some of the examples of machine learning applications in education and government.

Machine learning applications in Government and Education


Machine learning provides new opportunities to tackle the challenge of large-scale grading automatically by analyzing data generated while students interact with teachers and other students.  Data mining and predictive analysis are applied to offer personalized feedback, track progress and recommend steps for improvement. Content analytics organise and optimize contents and scheduling algorithms search for an optimal and adapted teaching policy to ensure help students learn more efficiently. It is also used for back-office operations by companies such as Edulog for bus scheduling, MyEdMatch and TeacherMatch for matching teachers and schools or Enterprise Resource Planning systems to predict enrollment, enhance security or boost retention.


Modern search engines and natural language processes are used to organise and explore public documents. Machine learning increases operational efficiencies by analysing data sets and finding patterns and anomalies. Additionally, identification of important trends and subtle yet complex patterns generate the most relevant and high impact information. This information detects and prevents fraudulent transactions, provide efficient, effective public services and help public officials to make informed decisions.  Analysis of data helps in detecting illegal activities such as money laundering, criminal activities, trade of counterfeit items or spread of terrorism.  Identifying trends and correlation among different groups stop these activities before it is too late. An example is to run buses according to citizens’ needs or when raining and not on fixed timetables. Also, it reduces duplicates in the database by recognising two businesses with the same address and phone number.

Machine learning provides an intelligent support to various aspects of the economy. Deducing from data is helping in improving productivity, daily working logistics, increase revenue and better learn the ambiguities of the real world. Its uses include recommending and up-selling. For instance, recommendations drive 50 percent of LinkedIn connections, 75 percent of Netflix views and 35 percent of Amazon sales. It enhances customer’s satisfaction while reducing costs and consequently helping to a business’s success. These techniques also make a government’s operations open, flexible, fast paced with less bureaucracy and more direct input from citizens.

Leave a Reply