Ethical Issues in the Use of Big Data in Education

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Table of Contents

Introduction
Big Data in Education
Key Terms Definition
Ways to Ensure the Use of Big Data in Education is upheld
Conclusion
References

Introduction

An ethical issue is a problem or situation that has moral implications. Ethical issues can be personal, professional, or global. Personal ethical issues might include things like lying or cheating. Professional ethical issues might involve things like bribery or conflicts of interest. Global ethical issues might involve things like human rights or environmental protection. Under professional ethical issues, several things can arise within an institution such as the use of big data in education whereby its violation can result in diverse consequences.

Big Data in Education

In the 21st century, the term “Big Data” has become increasingly popular in a variety of industries and fields, including education. While the term is often used in a general sense, it typically refers to data sets that are too large or complex to be processed using traditional data processing methods (“National Academy of Education,” 2016). In education, Big Data can be used to track student performance, identify areas of improvement, and tailor instruction to meet the needs of individual students. There are several ways in which Big Data can be collected and used in education. For example, data sets can be created through the use of learning analytics, which is the process of collecting, analyzing, and interpreting data related to student learning (Reidenberg & Schaub, 2018). Data sets can also be created through the use of educational assessment data, which is data that is collected to assess student learning.

Key Terms Definition

Several terms can be used in the description of big data in education which include:

Data privacy: Having control over how much, when, and under what conditions one shares with others (physically, behaviorally, or cognitively) is known as privacy (“National Academy of Education,” 2016). Therefore, data privacy often referred to as information privacy, deals with an organization’s or person’s ability to control what information about them in a computer network can be shared.
Confidentiality: Once data has been disclosed, confidentiality refers to how that information is handled in a trust relationship and with the anticipation that it won’t be disclosed to others without consent in ways that are incompatible with the initial disclosure.
Data security: It involves the prevention of unpermitted data access and includes standards that can be followed to ensure correct data access.
Security breach: This occurs when data containing sensitive personal information is lost, stolen, or otherwise accessed without authorization, potentially compromising the confidentiality of the data.

Issues Caused by Violation of Big Data in Education

There are a few issues that can arise from the violation of big data in education. First, if student data is not properly secured, it could be leaked to third-party organizations hence, leading to identity theft or fraud. Additionally, if student data is not properly managed, it could be used to discriminate against certain groups of students (Hosseini et al., 2022). For example, if a school district uses big data to track which students receive free or reduced lunch, they could use that information to determine which students to provide additional resources to. This could lead to a widening of the achievement gap between rich and poor students. Additionally, if big data is not used properly, it could lead to educators making decisions based on inaccurate or incomplete information (Regan & Jesse, 2019). This could have some negative consequences, such as students being placed in the wrong classes or not receiving the services they need.

Ways to Ensure the Use of Big Data in Education is upheld

There are a few ways to ensure that the use of big data in education is upheld. First, it is important to have a clear and concise policy in place that outlines how data will be collected and used (Calvard & Jeske, 2018). This policy should be reviewed and updated regularly to ensure that it meets the needs of the ever-changing landscape of big data. Second, it is important to have a dedicated team in place that is responsible for managing and using the data. This team should have the necessary skills and knowledge to effectively use big data to improve the quality of education (Calvard & Jeske, 2018). Lastly, it is important to keep the lines of communication open between the team responsible for big data and the educators who will be using it. This communication should be ongoing and should include both positive and negative feedback to ensure that the use of big data in education is upheld.

Conclusion

Big data is becoming increasingly important in the field of education. Schools and universities are using data to track student performance, identify areas where students need improvement, and customize learning experiences to better meet the needs of individual students. However, violation of the use of big data in education can lead to negative impacts such as identity theft. Data is also being used to assess the effectiveness of educational programs and to inform decision-making about how to allocate resources. In the future, big data may be used to personalize learning experiences for each student, based on that student’s strengths and weaknesses.

References

Calvard, T. S., & Jeske, D. (2018). Developing human resource data risk management in the age of big data. International Journal of Information Management, 43, 159-164. Web.

Hosseini, M., Wieczorek, M., & Gordijn, B. (2022). Ethical issues in social science research employing Big Data. Science and Engineering Ethics, 28(3), 1-21. Web.

National Academy of Education. (2016). Big Data in education: Balancing the benefits of educational research and student privacy. A workshop summary. National Academy of Education. Web.

Regan, P. M., & Jesse, J. (2019). Ethical challenges of edtech, big data and personalized learning: Twenty-first century student sorting and tracking. Ethics and Information Technology, 21(3), 167-179. Web.

Reidenberg, J. R., & Schaub, F. (2018). Achieving big data privacy in education. Theory and Research in Education, 16(3), 263-279. Web.

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