What is big data?
Now more than ever it is easy to collect massive quantities of data. Why? Well, as more people utilize and store information in chip-and-sensor technology, the internet, and cloud computing applications—more, diverse forms of information are readily accessible for companies or other institutions to analyze (Marr, 2015). Analyzing this big data helps companies or institutions predict behavior, become aware of meaningful trends, and make well-informed business decisions (Marr, 2015).
Big data tends to be large in volume, and is difficult to store, process, analyze, and present (Yang, Huang, Li, Liu, & Hu, 2016). Additionally, big data flows at a fast rate within organizations, typically contains biases and abnormalities, can be in structured or unstructured formats, and typically holds some value to help generate key insights about an organization’s policies (Yang et al., 2016).
In higher education institutions, big data is highly accessible due to the increasing use of “…ubiquitous computing devices, flexible class room design, and Massive Open Online Courses” (Daniel, 2014, p. 3). These technologies centralize large-scale programs, reduce the cost of providing education on universities, and also enable universities to reach more students.
Opportunities of big data in higher education:
- Big data analytics in higher education such as descriptive analytics encompass data collected on students, teaching, research, policies and other administrative processes to demonstrate current trends.
- Information collected may reflect enrollment rates, graduation rates, dropout rates, and rates of students that pursue higher degrees (Daniel, 2014). This may inform administrative processes and policies, which may serve as interventions to combat problematic trends.
- Learning management systems also provide a wealth of information in real time regarding students’ interactions with online course and exam content (Daniel, 2014). With descriptive analytics, researchers can determine the frequencies of videos watched, articles viewed, logins, course or exam completions, as well as students’ relative difficulty with a particular training course (Daniel, 2014). This data can trigger important dialogue regarding whether or not the technology is truly effective for students—or if other LMS options are worth exploring.
- Big data analytics in higher education such as predictive analytics encompass data that reveal the likelihood of risky or opportunistic future events by looking at trends and their related issues.
- For example, predictive analytics could be utilized to (a) allow the student to know if he or she is working effectively toward their desired learning goal, (b) allow the teacher to determine which students are at risk for course incompletions, and (c) allow institutions to better determine which courses would be advantageous to open in coming years (Boyer & Bonnin, 2016).
- Big data analytics in higher education such as prescriptive analytics encompass descriptive and predictive analytics that help higher education institutions decide on a best course of action to better achieve desirable outcomes while weighing constraints.
- For example, prescriptive analytics may reveal a better way to design content in online courses for students of different demographics (Daniel, 2014). Further, prescriptive analytics may reveal strategies for combatting dropout rate trends (Boyer & Bonnin, 2016).
Challenges of big data in higher education:
- Collecting, storing, and developing algorithms to properly process data to inform university policy and practice is time consuming and expensive.
- All data systems utilized by the different departments of higher education institutions are most likely not interoperable. Therefore, pooling data from these different systems may require costly, skilled technical support (Daniel, 2014).
- Additionally, procedures to process big data are not standardized; therefore, the generalizability of the information processed may not be sufficient to ensure better policies and practices are enacted for statewide, nationwide, or worldwide universities (Gagliardi & Wilkinson, 2017).
- Mining data for meaningful information from students may be unethical.
- Some information processed to inform policy in higher education institutions may violate student privacy and the security of student intellectual property.
- To combat this challenge, it is recommended that higher education institutions utilize “reliable data warehousing and management, flexible and transparent data mining and extraction, and accurate and responsible reporting” (Daniel, 2014, p. 918).
- Data security may be compromised when using a cloud-based software.
- To process big data efficiently, cloud-based software distributes processing tasks across many systems. Although this may mean that less data is vulnerable to be hacked through any one system, it also means that there are more distribution frameworks that house your data, which may increase security risks (Gross, 2016).
- Security tools that report in real time process a large amount of data at once, and may reveal data breaches where none of substance truly exist. It is important to learn to weed out these false positives, and learn to recognize the true positives and have proper protocols in place to address true breaches (Gross, 2016).
- Without encrypted validation to verify that users are who they say they are—breaches may occur (Gross, 2016). To prevent these instances, integrate an encrypted authentication procedure into your higher education institution’s online platform. For example, when a student enters a username and password into the system, design your system to then trigger a text message to be sent to that student. This text message can then provide a unique verification code to the student which that student can then enter into the system to access his or her account. This procedure ensures that the person entering the username and password is also the owner of, and has immediate access to, that student’s mobile phone. This thus reduces the risk of unauthorized individuals accessing your higher education institution’s platform.
Boyer, A., & Bonnin, G. (2016). Higher education and the revolution of learning analytics. Retrieved from https://icde.memberclicks.net/assets/RESOURCES/anne_la_report%20cc%20licence.pdf
Daniel, B. (2014). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46, 904–920. doi:10.1111/bjet.12230
Gigliardi, J. S., & Wilkinson, P. (2017, December 13). Big data on campus. [Blog post]. Retrieved from https://www.higheredtoday.org/2017/12/13/big-data-campus/
Gross, G. (2016, May 31). 9 key big data security issues. [Blog post]. Retrieved from https://www.alienvault.com/blogs/security-essentials/9-key-big-data-security-issues
Marr, B. (2015, March 23). Big data explained in less than 2 minutes – to absolutely anyone. [Blog post]. Retrieved from https://www.linkedin.com/pulse/big-data-explained-less-than-2-minutes-absolutely-anyone-bernard-marr
Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2016). Big data and cloud computing: Innovation opportunities and challenges. International Journal of Digital Earth, 10, 13–53. doi:10.1080/17538947.2016.1239771