I have noticed a trend in the five years that I have been part of the big data community. Most of the experts that I speak with almost exclusively discuss the benefits of using big data in the private sector. Far fewer data scientists emphasize its role in academia.
While its contributions in educational institutions have been frequently overlooked, big data is changing the profession in numerous ways. Administrators, students and faculty members are all benefiting from the emergence of big data and its potential in the halls of higher learning. Here are some of the applications of big data in academia in 2018 and beyond.
Big data has facilitated e-learning
Electronic University Network was the first virtual education center in 1984. This was over a decade before the World Wide Web was a mainstream technology, so students needed to use dial-up modem to connect to their online courses.
Online learning has evolved by leaps and bounds over the past 35 years. Many of these changes can be attributed to advances in data science. Big data is helping online learning programs develop more sophisticated learning platforms. The Institute of Electrical and Electronics Engineers recently published a very insightful paper on the role data is playing in online education.
Helping instructors mold their curriculum to their students
Big data has helped faculty members in a number of ways. It enables instructors to gather data on their students to customize their lesson plans. Banica Logica and Radulescu Magdalena discussed this in their Science Direct paper, titled “Using Big Data in the Academic Environment”.
“Big data, in the context of e-Learning systems (also called Big Learning Data), consists in the information sources (courses, modules, experiments etc.) created by the teachers, but especially in data coming from the learners (students) throughout the education process, collected by the Learning Management Systems, social networks, multimedia, as they were defined by the organization or the professionals,” the authors write.
Improving research models for every discipline in academia
Almost every academic field places a strong emphasis on research. Since the earliest days of academia, researchers have faced a myriad of challenges.
Trying to find sample populations for their studies.
Scientific studies require focus and control groups. Understanding the importance of each of these groups is not the biggest challenge that researchers face. Finding the right candidates for these groups is the hard part.
If a study is properly constructed, the two groups will be relatively homogenous and symmetrical to each other. Finding the right participants can be hard.
Big data has made it easier for researchers to find a sample population. They can mine data from social media, government data bases and a variety of other sources to discover people that may need the right criteria. As representatives of distinguished educational institutions, they can reach out to these people and offer them the opportunity to participate in a study.
Finding better ways to categorize and structure data
Data from academic studies needs to be carefully analyzed and formatted. According to Grade Miners essay writing service, without the right structuring, the results of the study can be wildly inaccurate.
Big data tools have helped with this immensely. A number of Hadoop solutions have enabled researchers to extract massive amounts of data and analyze them in a matter of minutes. Methodologies that rely on these algorithms can be highly effective in academic research projects ranging from particle -based physics to economics. They are being used in a variety of studies that require researchers to extract millions of data sets from third-party sources of information.
Improving the admissions process
College admissions departments have historically had very challenging jobs. They must make subjective decisions based on upwards of 10,000 candidates every single year.
Big data has made it easier for college admissions officers to vet these candidates. They can use Hadoop tools to screen every application and omit any applicants with Grade Point Average (GDP) or SAT scores below a certain threshold. These tools can also evaluate the demographic criteria of each applicant as part of the university’s affirmative action programs.
Using big data in the admissions process helps streamline the job and reduce costs for the university.