Machine learning in education focuses on student behavior to provide the ideal, personalized learning experience. ML programs record student engagement in real time by measuring their speed of recall, learning styles, interests, time spent studying, and more.
Edtech, which fuses the words “education” and “technology,” as a field encompasses ML software programs but also includes hardware and educational theory.
By tracking every moment a student engages with apps or spends on distance learning programs, machine learning programs identify individual students and adjust daily lesson plans to their needs. They also flag at-risk students to teachers and administrators.
These programs also help students with disabilities and students whose first language isn’t English by offering text-to-speech programs or using NLP to translate material.
Finally, educators and administrators use machine learning in their work. For example, educators use it to identify plagiarism while administrators track enrollment and budgets.
Jellynote uses machine learning to provide a gamified musical education. Anyone, anywhere, can learn to play the piano, the guitar, the ukelele, and more. Further, with the machine learning program constantly adjusting to their rate of progress, users won’t lose interest.
Already one of the biggest names in K-12 and higher education, Pearson uses machine learning to support students, teachers, and associated professionals. They also provide a large range of assessments and tests through their website.
Microsoft leverages its current services to improve education for teachers, students, and administrators. They also offer security services, computers, and personalized student analyses.
PipeCandy eCommerce Leads & Insights for Fulfillment tracks company data and company shipping details: shipping volume, which companies they use to ship products, whether they ship internationally or not, and so on. You can find company leads easily with this dataset with filter capabilities.
Wikiroutes Transit Data provides public transport information—routes, stop points, and more—via crowd-sourcing. The data is constantly updated and can be easily converted and integrated into your own software system.
Wikiroute’s Transit Data is used by individuals, private companies, and government agencies of all types and sizes.
TrackStar’s Predictive Credit Technology uses fifteen years of financial dispute data to create predictive models of future borrowing potential. With this data and AI technology, your bank or other lending company can mitigate the risk of fraud, improve existing customer relations, and reduce your operating costs.