How Mobility-driven Machine Learning is Elevating eLearning in 2021

Initially struggling with the onset of the pandemic, the education industry is now set to make records with the pace of digitalization and profitable yields of popular technologies, implementable and operable on smartphones. Machine learning, as has been the case in digital marketplace, is bound to revolutionize education, opening up a myriad of opportunities for schools and institutions to level up and build stronger walls for an exhaustive learner development through mobile and web apps. In this blog, we will take a look at some of the popular machine learning use cases that schools and institutions can channel through administrative apps and make education as effortless as possible for students, parents and teachers alike. 

Mobility-based Machine Learning Use Cases in Education 

• Infrastructure management on-the-go - Machine learning has made inroads into infrastructure management in numerous industries. Educational infrastructure, that includes connectivity, mobile devices, and proprietary learning platforms, is now fast gaining prominence after the onset of pandemic. It makes up for one of the most functional use cases of ML in education. In schools and institutions, it is vital that authorities are notified of discrepancies or anomalies in functioning of their digital learning platforms or apps to make sure student studies remain slick. For example, a notification service in the app can alert authorities of the downtimes and possible reasons for the same so as to help instigate prompt action. This is again done through an ML based analysis of continuously collected data streams from networks, historical anomalies etc.
• Data analysis on learner mobile usage -  Data collected by apps through cookies on smartphones can help in understanding the web surfing behavior of the students and intervene in cases that seem serious or objectionable. Furthermore, this data can also help authorities understand the level of engagement and depth that their content offers based on data that indicates students resorting to external academic research for certain concepts and doubt clearing. This can also help authorities keep a tab on the emotional well-being of students and keep the parents informed. 
• Machine learning for adaptive learning - Adaptive learning has been present since the beginning of mankind but its popularity and effectiveness in education has been quite recent. It involves watering down on depth and difficulty levels of quizzes, exams, or content in order to help students build a robust foundation in academics. Through data technology, this principle can be implemented through a well-worked data collection and feedback framework in the app, that can self-learn and automate adaptive learning. In conceptual learning, prompt individual student feedback on difficulty level of classes can help the algorithm offer content that is relatively easy and build upon it through multiple one-on-one sessions. 
• Individual student monitoring - Machine learning can help authorities look into individual student performance and be notified if a student is consistently performing poorly even after appropriate guidance and training. It can also help analyze individual interests of a student based on the subjects he/she excels at or like and the ones they despise. This can help teachers hone in on the strong points of students and prepare them for a suitable career.
• Data analysis to deliver quality education - Improving the quality of education has been the number one use case of machine learning and data in education. Through frequent ML based exploration of indications that various data sets such as student performance, feedback and external reviews, etc. you can tweak your strategies, delivery frameworks and content to meet and surpass the industry standards. It can also help you wean off cliched content, app features and address student or parent concerns that surround your curriculum.
• Revenue generation through mobility data - Machine learning and big data has been long helping enterprises capitalize on popular as well as uncharted strategies that help maximize profits. The popular ones include cross-selling, commissions on third-party content selling and advertisements. Through a range of well placed ads and selling of inhouse educational products such as mini-courses, certificates, and diplomas on subjects a student is interested in, organizations can help offer students a personalized and resource intensive training curriculum, and gain significant monetary advantage in the process.

Conclusion

As traditional brick-and-mortar education models received a sobering blow due to the pandemic, the remote learning resort has rather turned out to be rejuvenating. These inventive technology use cases are making up for the lost times and stagnated routines of learners, encouraging stakeholders to further weave technology into education. It therefore makes great sense for enterprises in the education sector to ramp up their technology initiatives and partner with education technology companies that can help them expedite and uphold their ambitious foray into the technology driven education models. 

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