Big Data in Education

Title of the Article

Big Data in Education

Article Subject Area

Due to its importance in modern society, the academic industry or sector is the focus of this investigation.

Type of Data Set Used/Evaluated

The analysis presented in this article is based on research that examined categorical data. Data sets that are categorical are those that are used to represent groupings of people, locations, or objects. The qualitative variable gives two categories, sometimes referred to as values, which are used to construct the categorical data that is formed on top of them (Williamson, 2017). A variable is said to be dichotomous if there are two possible values for it, but it can never take both of them at the same time. Polytomous variables, on the other hand, may take on more than two distinct values, in contrast to dichotomous variables. Until there is proof to the contrary, it is usual practice to operate on the assumption that variables used for classification or quantitative analysis are polytomous.

Synopsis of Article

The volume of information stored in data warehouses is increasing rapidly, and the term “big data” has been used to characterize the vast quantities of data accumulated by many businesses (Williamson, 2017). Big data has the potential to improve college and university operations, student outcomes, and staff productivity. It changes how schools evaluate the effectiveness of technology on learning, teacher efficiency, student engagement, and community involvement. Teachers evaluate students’ overall progress in class based on data they collect.

Data analysis may reveal previously unknown avenues for expanding participation. Teachers may use the insights gained from analyzing massive datasets to improve classroom activities and performance, as well as to more accurately assess their students’ development over time. Universities use CRM and associated recruiting systems to monitor the digital footprints of potential students across several platforms, including the web, social networks, and mobile devices (Williamson, 2017). Leaving a digital footprint whenever you use the web, social media, or mobile devices means that you may be tracked wherever. Prospects who have visited an educational institution’s website, either via an ad for the site or by exploring it themselves, may be retargeted by the CRM system using “cookies,” a form of computer information. It is possible that the school’s tuition income will increase if enrollment rates increase.

The college’s campus finances and economic activities may benefit from the use of big data, thus doing so is a worthy endeavor. However, whether or not this will lead to greater efficiency and output remains unclear. Since the introduction of big data, there has been a shift in the way in which teachers, students, and administrators communicate. This shift has been brought about by the emergence of “big data.” All parts of academic support for currently enrolled students, including recruitment and evaluation, are affected (Pardos, 2017). Teachers may benefit from big data analysis by learning more about their students’ strengths and areas for improvement, allowing them to better tailor their lessons to the needs of their pupils… Consequently, students have more control over how they use their time in class. Educators may benefit from big data analytics by learning more about the causes of student attrition. By studying what causes students to drop out and what keeps them in school, authorities may be able to create programs and initiatives that reduce the number of students who depart school before finishing their degrees.

Three business takeaways on how data impacts the industry concerning usability

The use of large amounts of data in educational settings will result in improved digital learning competence among educators. Big data, when paired with more traditional educational resources such as the internet, books, and electronic textbooks, may make it possible for teachers to give more customized teaching for the students in their classrooms (Pardos, 2017). The automation of tasks will make it easier for teachers to plan lessons and examinations by using resources such as learning management systems, which is another area that will benefit from automation (LMS). As a direct consequence of this adjustment, instructors now have more time to spend one-on-one with their classes.

Second, as the techniques of collecting and evaluating data grow more advanced, academic establishments will be better prepared to plan for the makeup of their student bodies and pick the applicants who are best qualified for the positions available. There is the potential for promoting institutional development, as well as degree software package coordination of resource usage, as possible advantages (Pardos, 2017). Third, technological developments such as the introduction of virtual, augmented, and mixed intricacies in instructional content, the development of artificial intelligence, and, yes, the ability to connect to internet of things (IoT) gadgets are all examples of developments that are currently taking place and are anticipated to become more prevalent in the near future. There is hope that the current wave of technical advances will allow for more efficient individualized teaching, improving both students’ chances of success and the quality of their academic experience. Thanks to this, and the hard work of everyone involved, the big data industry is poised for rapid growth in the near future.

References

Pardos, Z. A. (2017). Big data in education and the models that love them. Current opinion in behavioral sciences, 18, 107-113.

Williamson, B. (2017). Big data in education: The digital future of learning, policy, and practice. Sage.