Data science has a massive impact across many facets of education — from customized learning experiences and more efficient administrative programs to augmenting educators in their daily teaching practices.
Data, analytics, and artificial intelligence (AI) have become increasingly linked to the impact of the pandemic on the educational experience. Demand for virtual learning platforms and more personalized, interactive, AI-fueled learning tools are shaping the education market and contributing to innovative learning and tutoring techniques, and elearning platform development. The AI in education market already surpassed $1 billion in 2020 and is anticipated to grow at a CAGR of more than 40 percent between 2021 and 2027, reaching approximately $20 billion.
Interestingly, the changing educational landscape is also being shaped by external demand for highly specific skill sets in the workforce as companies respond to digital disruption. As a result, the future of education and the future of the workforce are becoming intrinsically connected.
Practical Applications of Data Science in Education
Enhance Knowledge Retention
IoT is one of the most adaptable technologies for the modern learning experience because IoT-enabled devices provide an accessible vehicle for communication, educational materials and resources, and supportive visualizations that improve knowledge retention and understanding. For example, a speech-to-text based system on such a device enables automated note-taking, which allows the student to simply absorb a lecture rather than divide their attention between listening and writing.
Anticipate Student Graduation and Dropout Rates
Higher education institutions are increasingly leveraging data science in education and machine learning solutions to predict scenarios such as which students are most likely to enroll, graduate, and be ready for a career in their chosen area of study. These capabilities also help educational providers track patterns in student dropout rates and the corresponding demographic and educational factors to predict potential future dropouts so they can proactively intervene and allocate resources to prevent it.
Deeper Understanding of Student Progress
Advanced analytics, including AI, is also being used to gain insights into academic performance so that teachers, faculty, parents, and students can better understand how a student is responding to certain tests, for example. This information can then be applied to modify the corresponding learning or teaching path to improve academic outcomes.
Accessible Education for Dispersed Student Base
Additionally, many students are working remotely during the pandemic and virtual classrooms and cloud based e-learning platforms, along with customized applications, are becoming the norm as educators strive to deliver high quality learning programs for a dispersed student base. Augmented and virtual reality are similarly providing exciting simulations and gamification to promote a more immersive remote learning experience.
Intelligent chatbots are being used by schools to help address pervasive absenteeism. For example, an AI-driven two-way text messaging system was developed to help children who frequently miss classes by enabling teachers to touch base with the student’s family. It also offers tailored, around the clock support for students who are experiencing difficulties learning.
Molding the Future Workforce
While education institutions are investing in new technologies and data science in education to address current challenges and objectives, they’re also keeping the future in mind, particularly the future of the workforce. Data science disciplines, including data analytics, are vital to these institutions as they respond to changing economic and social conditions, evolving technologies and disruption, and a new era of work.
Academic institutions are increasingly dependent on analytics to inform the development of their academic programs, degrees, training, and certifications so that they better align with fluctuations in job demand and skill gaps. Many higher education institutions are turning to predictive analytics to assess future demand for employees according to various education levels, business domain, and specific roles. This enables them to prepare for emerging workforce trends and make insights-driven decisions regarding courses that are able to effectively address the changes and developments expected to occur in the labor market.
A data-centric perspective of evolving workforce demands further allows academic institutions to help students become more career-ready and close the disparity between education and employability, which is a growing concern amongst employers.
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Inspiring the Future with Data Science in Education
Data science is responsible for numerous opportunities and innovations in the education industry recently as the events and disruptions of the past 18 months capitulated the world into a new era of academic and workforce demands. To learn how to shape your own career path that could impact the future of education or the workforce, explore Simplilearn. If you are looking forward to advancing your career in data science then enroll in Simplilearn’s Data Science course.