Solutions - Education

Elevate education by grading students work in MOOC courses using AI, summarizing a long whitepaper for easier comprehension, and detecting plagiarism in students’ work are some ways this sector is getting disrupted

Technology has always played an important role in the classroom for educators. Many schools are adopting “smart classrooms” with access to technology like Smart Boards and providing each student with their own tablet or access to a computer. Vast amounts of data are available to educational institutes, and Numtra helps school utilize the information available to shape education policies and practices.

More and more states are beginning to allocate funds to public higher education schools based on the number of students that graduate with a degree rather than how many are enrolled. This makes it more important for universities to ensure that their students complete their degree and graduate so that the school can continue to receive funding to support high expenses. Admission officers are starting to look towards predictive analysis to help determine which students are most likely to succeed, and also identify when students might require additional counseling services to help them complete their coursework and graduate. Analyzing data collected from students allows university to better understand their enrollment goals and better plan for available funding. Once students are enrolled in the university, predictive analysis personalizes learning and advising services to increase the chance that a student will successfully graduate.

In the past, teachers have mainly relied on standardized testing to assess a student’s progress and knowledge of a subject. Results can be skewed if a student is not a good test taker, and these performance tests are often only completed once or twice a year. With a shift to data analysis, teachers can continuously keep track of students’ learning progress, and adjust their lesson plans and methods to tailor education to the pupils’ needs.

Early education teachers have long struggled with large class sizes for students of varying academic levels. Not only is it difficult to appropriately allocate classroom time to students of all reading levels, but it is also challenging to find enough reading material to accommodate all students. Although there exists a loose classification system that labels some literature by grade-level, it is not uncommon that many students in a class have reading abilities that fall above or below their designated grade-level. To unload the teacher’s burden to provide individually matched reading materials to students, a machine learning algorithm can be used to classify various texts appropriately.

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