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Challenges of Big Data Analytics in Education - Suzanne Vermeulen

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Challenges of Big Data Analytics in Education - Suzanne Vermeulen

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There are challenges of using big data in education that need to be considered. One study (Veldkamp, Schildkamp, Keijsers, Visscher, & de Jong, 2021) assessed the importance of the quality of the data that is used for the analytics to protect students from unfair evaluations. Furthermore, educators need to take care to not depend on data alone and not follow their own observations. Additionally, who would evaluate the data? A data scientist or the educator, as this could create a gap between the data and the student.

When depending on data care needs to be taken to not disadvantage students that do not have access to technology such as internet as data cannot be collected from these students.

Care needs to be taken with the data ownership and the privacy laws that may be adhered to when collecting data.

Cyber security needs to be considered when using and storing data in education to ensure students are not exposed to inappropriate content or the data is hacked and used or sold to third parties (Hariri, Fredericks, & Bowers, 2019)

Hariri, R. H., Fredericks, E. M., & Bowers, K. M. (2019). Uncertainty in big data analytics: survey, opportunities, and challenges. Journal of Big Data, 6(1), 1-16.

Veldkamp, B., Schildkamp, K., Keijsers, M., Visscher, A., & de Jong, T. (2021). Big Data Analytics in Education: Big Challenges and Big Opportunities. International Perspectives on School Settings, Education Policy and Digital Strategies: A Transatlantic Discourse in Education Research, 266.

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NEGATIVE SIDE OF BIG DATA ANALYTICS IN HIGHER EDUCATION- GRACE POLOA

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NEGATIVE SIDE OF BIG DATA ANALYTICS IN HIGHER EDUCATION- GRACE POLOA

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NEGATIVE SIDE

Cost is a significant obstacle to data analytics in higher education. Many organizations regard analytics as a costly activity rather than an investment. The apparent requirement for expensive instruments or data collection methods is at the root of most of the anxiety about affordability (Bichsel, 2012).

Bichsel, J. (2012). Cost is a significant obstacle to data analytics in higher education. Many organizations regard analytics as a costly activity rather than an investment. The apparent requirement for expensive instruments or data collection methods is at the root of most . Louisville : EDUCAUSE Centre For Applied Research .

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While big data offers significant potential for transforming higher education, it also presents several downsides and challenges that need to be carefully considered and addressed:

1. Privacy Concerns:
The collection, storage, and analysis of vast amounts of student and faculty data can raise serious privacy concerns. Safeguarding sensitive personal information and ensuring compliance with privacy regulations (e.g., GDPR, FERPA) is critical to maintain trust and legal compliance.

2. Ethical Use of Data:
Big data analytics can inadvertently lead to the misuse or bias in decision-making processes, such as admissions, financial aid, or academic performance evaluation. Ensuring ethical use of data to avoid discrimination or unfair treatment is a significant challenge.

3. Data Security Risks:
Storing and managing large volumes of sensitive data make higher education institutions attractive targets for cyber-attacks. Safeguarding data against breaches and ensuring robust cybersecurity measures are in place is crucial to mitigate risks.

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One of the most highlighted benefits of the plus group is personalized education, but it’s possible to disagree. The issue can be reflected from a different perspective.

It's important to keep in mind the meaning of Big Data. This means that even customized options for students are based largely on information from other students' experiences. As a result, there is a lack of personal touch in this personal educational package, making it difficult for algorithms to cater to individual educational needs. Big data puts limits on students' choices. Personalized guidance, mentorship, and development of critical thinking skills are core strengths of traditional education due to personal interactions between faculty and students.

In summary, while Big Data can provide personalized education, it may also diminish the personal touch of traditional education. Therefore, it is important to balance data-driven insights with human interactions for a well-rounded educational experience.

Bai, X., Zhang, F., Li, J., Guo, T., Aziz, A., Jin, A., & Xia, F. (2021). Educational Big Data: Predictions, Applications and Challenges. Big Data Research, 26, 100270–. https://doi.org/10.1016/j.bdr.2021.100270

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Big Data could negatively influence the recommendations for students who want to pursue higher education. The data from racial, cultural, and social backgrounds could generate “stereotype” recommendations for students. It could have a negative impact, as it would wrongly lead the students to a different field of study as he/she would have no interest or passion for it. The Big Data would need a lot of fixes and modifications for a better recommendation, most to be more adaptive to culture and personal desire.

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Disadvantages, Challenges, and Risks of Using Big Data Analytics in Higher Education

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Disadvantages, Challenges, and Risks of Using Big Data Analytics in Higher Education

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In the Pluses, many have mentioned Personalised learning. Although it is a valid point, we need to cognizant of some of the disadvantages.

Disadvantages, Challenges, and Risks of Using Big Data Analytics in Higher Education for Personalized Learning:

1. Data Privacy and Security: Collecting and storing vast amounts of sensitive student data can raise privacy concerns. Institutions must implement robust security measures to protect against data breaches and unauthorized access.

2. Data Accuracy and Bias: Inaccurate or biased data can lead to flawed personalization efforts. Institutions must ensure that data quality and diversity are appropriately managed to avoid reinforcing existing inequalities.

3. Ethical Considerations: The use of data analytics for personalized learning raises ethical questions about informed consent, transparency, and how data is utilized. Balancing personalization with privacy and ethical considerations can be challenging.

4. Infrastructure and Resource Requirements: Implementing a robust big data analytics infrastructure requires significant investments in technology, software, and expertise, which may strain the resources of some educational institutions.

5. Faculty and Student Adoption: Faculty and students may be resistant to the adoption of data-driven personalized learning, viewing it as intrusive or detracting from the traditional educational experience. Overcoming resistance and ensuring buy-in can be a substantial challenge.

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Challenges of using big data analytics in higher education

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Challenges of using big data analytics in higher education

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Mohini Karnik

Big data has many benefits in higher education like personalized learning and financial aid. However, it is interesting to think about whether these benefits outweigh the risks:

1. Data privacy: Collecting, maintaining, and using the collected data is risky. It is very important to be careful not to misuse or expose the sensitive data.

2. Data leaks: With storing large amounts of personal data of students, the risk of a data leak is very high.

3. Biases: It has been noted that there are inherent biases in the way data is analyzed and processed. These biases might negatively impact students’ future.

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Challenges of using big data analytics in higher education

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Challenges of using big data analytics in higher education

# data[].attributes.body
Mohini Karnik
Big data has many benefits in higher education like personalized learning and financial aid. However, it is interesting to think about whether these benefits outweigh the risks:

1. Data privacy: Collecting, maintaining, and using the collected data is risky. It is very important to be careful not to misuse or expose the sensitive data.

2. Data leaks: With storing large amounts of personal data of students, the risk of a data leak is very high.

3. Biases: It has been noted that there are inherent biases in the way data is analyzed and processed. These biases might negatively impact students’ future.

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Challenges of using big data analytics in higher education

# data[].attributes.subject
Challenges of using big data analytics in higher education

# data[].attributes.body
Mohini Karnik
Big data has many benefits in higher education like personalized learning and financial aid. However, it is interesting to think about whether these benefits outweigh the risks:

1. Data privacy: Collecting, maintaining, and using the collected data is risky. It is very important to be careful not to misuse or expose the sensitive data.

2. Data leaks: With storing large amounts of personal data of students, the risk of a data leak is very high.

3. Biases: It has been noted that there are inherent biases in the way data is analyzed and processed. These biases might negatively impact students’ future.

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Challenges of using big data analytics in higher education

# data[].attributes.subject
Challenges of using big data analytics in higher education

# data[].attributes.body
Mohini Karnik
Big data has many benefits in higher education like personalized learning and financial aid. However, it is interesting to think about whether these benefits outweigh the risks:

1. Data privacy: Collecting, maintaining, and using the collected data is risky. It is very important to be careful not to misuse or expose the sensitive data.

2. Data leaks: With storing large amounts of personal data of students, the risk of a data leak is very high.

3. Biases: It has been noted that there are inherent biases in the way data is analyzed and processed. These biases might negatively impact students’ future.