How Will Learning Analytics Change Education in 2025? Essay Example

Just like many other sectors in life, education is undergoing tremendous technological changes thus reshaping the mode of teaching. Classes have been connected and the fundamental model of business has been shaken up in the education industry (Dawson, 2010). The glamour that is associated with technology is what is required for the transformation of education. Technology p0lays a significant role of an amplifier that must be incorporated in a vision and a plan of what must be amplified. Technology however must not replace the constructive pedagogy in the education industry. Learning analytics play a crucial role in redefining the education industry through supply of a road map that is seemingly scalable in achieving success. The education industry must adopt the distinct professional specialization offered by data science that is associated with newer specialization in to ‘education data science’. Technology has gone beyond human capital in many fields including education and demand surpasses supply (Romero, et al., 2010).

Enhanced utilization of the learning data available to the students towards analytics is anticipated to achieve specialization redefining in the education sector that enhances the quality of teaching. The contemporary education faculty must be incorporated in to learning faculty especially the faculties dealing with education measurements as well as psychometrics. This is an achievement that can be a reality in many learning institutions in 2025. This will also engross accessing the faculties that deal in advanced work that is characterized by enormous data sets such as computer science, business intelligence, statistics, particle physics and bioinformatics among others.

The achievement of data science in education and learning analytics by 2025 would necessitate the leveraging of the much desired expertise offered by the experts in cyberlearning as well as cyberinfrastructure (Baepler & Cynthia, 2010).  In this scenario, cyberlearning can be considered as the type of learning which is mediated by means of communication technologies as well as networked computers. On the other hand, cyberinfrastructure implies to the distributed information, communication as well as computer technologies working in conjunction with the relevant personnel with the ultimate objective of integrating the suitable components which offer a permanent platform in open learning.

Learning analytics is anticipated to change education in 2025 through the ability to address the challenges that are associated with personalized learning by application of cyberlearning infrastructure and hence facilitate leveraging of all the skills, the talents as well as resources that are available in the government agencies, academic institutions, nonprofit organizations, the private sector as well as all private foundations. The resources will play a crucial role in field building as well as facilitating for the training programs. Learning analytics is perceived as a good avenue of fueling innovation that will be a source of data on content, individual performance as well as trend performance. Learning analytics by 2025 will be in a position of enabling the sharing of data that is associated with resources applicable in digital learning. Useful resources will be achieved through sharing the resources among individuals through the application of computer systems. Students will be empowered to poses readable format of data in their machines that will facilitate in the creation of personal profile for learning (Arnold, 2010). The vendors who are willing to implement Learning analytics must be encouraged and permitted by the relevant institutions in anticipation of implementing successful learning analytics by 2025. This will be beneficial to many students as they can pay for the only courses that they need in their institutions. The classroom environment will be marked by tablets to facilitate communication with interactive whiteboards.

References

Arnold, K. (2010). Signals: Applying academic analytics. EDUCAUSE Quarterly, 33(1).

Baepler, P. M. & Cynthia, J., (2010). Academic Analytics and Data Mining in Higher Education. International Journal for the Scholarship of Teaching and Learning, 4(2).

Dawson, S. (2010). ‘Seeing’ the learning community: An exploration of the development of a resource for monitoring online student networking. British Journal of Educational Technology, 41(5), 736-752.

Romero, C., Ventura, S., Pchenizky, M., & Baker, S.J.d. (Eds.). (2010). Handbook of educational data mining. Boca Raton, FL: Taylor and Francis.