Predictive Analytics and the Digital Divide

The term "digital divide" is used to describe the disparity between those who have and those who do not have access to various forms of digital technology. This divide is evident in the availability of hardware like computers and cellphones as well as software like the internet and digital services. The spread of the coronavirus demonstrated how widespread problems with slow uploads, latency, and network congestion have become. Communities of color are disproportionately affected by these issues (Mashable, 2021).

Data, statistical algorithms, and machine learning techniques are used in predictive analytics to determine the likelihood of future outcomes given existing data (SREB, 2018). Porter and Balu’s (2016) study on predictive modeling was to predict how well K–12 students would do in school by using different kinds of data. The study looked at information about the students' backgrounds, how well they did in school, and how often they went to class (Porter & Balu, 2016).

The study also found that the predictive models were able to identify the academic outcomes of both individual students and groups of students with a high degree of accuracy. But the researchers said that while predictive models can help find kids who are likely to fail in school, they shouldn't be the only thing used to make decisions about education.

Overall, the study shows how predictive modeling could be used to find kids who are at risk and target interventions to help them do well in school. But it also shows how important it is to use predictive models in an honest and ethical way and to be aware of their possible biases and limits (Porter & Balu, 2016).

Predictive analytics could help bridge the digital divide, but it needs to be evaluated carefully. Predictive analytics may be useful in closing the digital gap since it can pinpoint specific locations that are without access to necessary infrastructure. Predictive analytics can pinpoint under-served areas in terms of digital infrastructure by examining data on internet use and device ownership. Investments in internet infrastructure or campaigns to enhance access to digital devices can then be targeted with this data.

However, a critical eye should be used while evaluating the efficacy of predictive analytics. Predictive analytics has the ability to amplify preexisting prejudices and inequities, which is cause for concern. The predictions made by predictive analytics may be flawed if the historical data used in the analysis exhibits systemic biases. Predictive models trained on data from a primarily white and wealthy population, for instance, may not adequately account for the requirements of more culturally and socioeconomically diverse communities.

To alleviate these worries, we must guarantee the ethical and transparent application of predictive analytics. Transparency regarding the data used in prediction models is essential, as is making sure that data is representative and undistorted. It also requires being open and honest about how forecasts are being used, and checking to make sure they aren't being used in a discriminatory way.

While predictive analytics shows promise in closing the digital divide, it needs to be evaluated carefully. We can make sure that predictive analytics is used in a way that promotes equity and justice if we are aware of the potential biases and ethical problems associated with it. It is ultimately up to us to make sure that technology is used in a way that helps everyone in society.

I'm curious how predictive analysis will help me in my current job as a financial aid advisor. What should I look for and how should I begin?

References

Mashable. (2021, April 26). The 'digital divide' and covid-19's impact on internet access: Mashable. YouTube. Retrieved April 18, 2023, from https://youtu.be/xkbZPAJF88k

Porter, K. E., & Balu, R. (2016). Predictive Modeling of K-12 Aademic Outcomes. mdrc Building Knowledge To Improve Social Policy. Retrieved April 18, 2023, from https://www.mdrc.org/sites/default/files/Predictive_Modeling_of_K-12_Academic_Outcomes.pdf

SREB. (2018, February). 10 issues in Educational Technology. Southern Regional Education Board. Retrieved April 1, 2023, from https://www.sreb.org/10issues