Big Data Analytics Techniques in High Education System

Subject: Tech & Engineering
Pages: 5
Words: 1407
Reading time:
6 min
Study level: PhD

As higher education enters the era of data analytics and big data, interests have grown for the application of big data analytics techniques in the system. The goal of higher education institutions is to increase enrollment, access to education, control costs and ensure high quality of education demonstrated through student performance. Big data analytics offers new opportunities for institutions of higher learning to improve various aspects of learning. It can enhance student performance and reduce administrative workload. Institutions of higher education continue to collect huge volumes of data concerning their students and faculty members. They can manipulate or augment these data with other related data in several ways to provide the required insights for enhancing learning experiences and outcomes. Moreover, open data sources and other public data offer massive opportunities for data mining for such institutions to yield new opportunities for improvement.

In only 3 hours we’ll deliver a custom Big Data Analytics Techniques in High Education System essay written 100% from scratch Get help

As institutions of higher learning focus on the ranking of the best colleges, admission, and completion rates, they are under pressure to improve their rates by the year 2020. Higher education, however, faces myriads of problems concerning student success, costs, and access. Stakeholders must address these challenges within the next few years for the US to make significant improvements in the national completion rates and academic advancement. These multiple challenges need deeper analysis and comprehension of available data on higher education to guide decision-making and policy formulation for effective responses. Institutions of higher learning must ask themselves what they can do with new insights derived from data analytics.

On this note, it is imperative to conduct a study that can explore new options for solving higher education problems by focusing on the use of data analytics to support and solve some of the current critical challenges experienced in higher education. To gain the necessary insights, a study must be conducted across various universities that have adopted analytics in their operations to drive various aspects of higher learning. At the same time, a survey will be conducted among vendors to understand how they collaborate with higher education institutions to promote the use of analytics in higher education.

Analytics has been defined as the “use of data, statistical analysis and explanatory and predictive models to gain insights and act on complex issues” (Grajek, 2013). There are specific functional areas in higher education that analytics and prediction can be applied to support outcomes. Analytics, for instance, can be applied in critical areas such as finance and budgeting, enrollment, instructional and student progress management among others (Mattingly, Rice, & Berge, 2012).

It is imperative to recognize that analytics in higher education is relatively new and the sector is a late adopter in the use of analytics as decision support and management tool compared to other sectors. Predictive analytics has been applied in private sectors for several years to evaluate consumer behaviors, predict product performances and sales among others in online companies like Amazon, Netflix, and airline and manufacturing companies among others (McAfee & Brynjolfsson, 2012). It generally leads to effective decision-making because of the prediction capabilities.

Education has become a big investment right from lower levels to higher education. However, in the recent past, President Obama has put pressure on institutions of higher learning to increase the rate of completion. Unfortunately, it has not been clearly shown how different factors influence higher education. For instance, many colleges cannot adequately demonstrate academic activities that need to be eliminated, reviewed, or facilitated to achieve the desired admission and completion rates. Therefore, it remains difficult to assess education practices that inhibit learning or cause more damages.

Applying analytics is a fundamental step that can result in informed decision-making in higher education. In the past few years, there have been growing calls for education reforms. However, it remains unclear how such proposed changes will affect the education system. Perhaps such proposed solutions could further implicate higher education. It is, therefore, necessary to establish a strong foundation for reforms in education. In the corporate world, for instance, business intelligence has been a critical tool for decision support. Similarly, for higher education, analytics can provide the necessary intelligence for decision-making. In this regard, critical factors related to inputs, outputs, and outcomes or learning success must be evaluated to support informed decision-making. Data will provide the required evidence to support these processes.

Academic experts
available
We will write a custom Tech & Engineering essay specifically for you for only $16.00 $11/page Learn more

Higher education must however recognize that using analytics requires some caution. Generally, it can offer valuable insights on factors that may affect graduation rates or students’ success, as well as relevant activities at the institution. These may include enrollment, time spent on learning, engagement with faculty members, learners’ attendance, attrition, and social activities among others. Further, institutions of higher learning can also evaluate other support services such as the use of facilities like library resources and learning aid services. On the other hand, data analytics may not account for other soft factors in learning such as effects of motivation and informal engagements among others.

Nevertheless, higher education can use analytics to effect structural changes in learning to support favorable outcomes. For instance, today, it remains unclear how institutions of higher learning leverage student performance, usage, behaviors, faculty performance, and social insights such as tendencies, propensities, and trends to maximize learner engagement processes, reduce rates of attrition, enhance lifetime value and promote advocacy (Schmarzo, 2014). A focus could be on the learning content, for instance. For a long time, studies have continuously demonstrated that learners are unique, have diverse knowledge levels, learn at different paces, face different socioeconomic challenges and topic familiarity differs. On this note, an intelligent curriculum should account for the unique needs of every learner rather than using a standard curriculum for all. Learning contents should be flexible, adaptive, and regularly reviewed. This calls for analytics so that significant insights can be discovered and applied to shape the content for different learners and improve learning experiences (Wagner & Ice, 2012). Learning evaluation should be a continuous process and should not only be conducted at the end of a course using tests. Insights from knowledge profiles could be used for comparison to determine the chances of learners graduating in their majors.

The major objective of adapting learning content or any other activities is to enhance both general and individualized educational experiences for students and faculty members. It would ensure that students and faculty members adapt to the preferred course of action fast and improve outcomes. Analyzing student data to comprehend learning activities and motivational elements, in fact, may provide chances for reevaluating how to improve learning experiences that enhance and promote learner success.

It is noteworthy that institutions that apply analytics may only concentrate much on student acquisition and attrition rates. This is a good starting point, but deeper insights can be obtained by monitoring, for instance, students at greater risk of dropping a course or out of college. By identifying these issues earlier enough, higher education can develop interventions to reduce dropout rates effectively.

At the basic levels, higher education should use analytics for student acquisitions; course selection; performance effectiveness; student workgroups; retention; teacher effectiveness; and attrition (Schmarzo, 2014). All these applications show that higher education outcomes can be improved through analytics. It can act as the basis for making informed decisions, changing how the curriculum is designed and delivered, content evaluated, student learning activities, resource allocations, and outcome monitoring among others.

As previously mentioned, analytics is relatively new in higher learning and therefore challenges can be numerous. It, therefore, requires leaders to make the commitment and develop their capacity to gather and share data. This can be a major challenge. Fortunately, vendors may offer such services to colleges. As much as effective data analytics would promote data culture and guide higher education on strategic objectives that require continuous improvement, it calls for successful management and sharing of reliable data alongside supportive leadership, IT tools, talent management, changes in culture, and decision-making to support preferred outcomes.

Numerous challenges afflict higher education in the US. Nevertheless, they are under pressure to increase performance and graduation rates. Fortunately, higher education has multiple chances from big data analytics to improve education. These opportunities can be realized from a study that demonstrates how analytics solve higher education numerous challenges. It requires collaboration with other stakeholders such as vendors to show how new technologies can support learning in colleges. Universities must adopt best practices once revealed from data analytics, and they have opportunities to transform learning experiences and outcomes for students, teachers, and the community in general.

15% OFF Get your very first custom-written academic paper with 15% off Get discount

References

Grajek, S. (2013). Top-Ten IT Issues, 2013: Welcome to the Connected Age. EDUCAUSE Review, 48(3).

Mattingly, K. D., Rice, M. C., & Berge, Z. L. (2012). Learning analytics as a tool for closing the assessment loop in higher education. Knowledge Management & E-Learning: An International Journal, 4(3), 236-247.

McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review. Web.

Schmarzo, B. (2014). What Universities Can Learn from Big Data – Higher Education Analytics. Web.

Wagner, E., & Ice, P. (2012). Data Changes Everything: Delivering on the Promise of Learning Analytics in Higher Education. EDUCAUSE Review, 47(4).