Description

                    BIG DATA INFRASTRUCTURE IN HIGHER EDUCATION

INTRODUCTION

Big data infrastructure and tools gives an opportunity for higher educational (Universities) institutions to utilize informational technologies by mining resources in a strategic way in order to improve higher educational systems for a better quality and also in guiding students to a successful completion of their chosen programs (Julius Murumba, Elyjoy Micheni, 2017). Big data makes things easier as it is continuing mining smart techniques and analytics for a better version higher education learning. Teaching and lecturing tools should also motivate learning of students as they should be intelligent and effective enough to deliver a better output for students learning.

Furthermore, Big data and Analytics is a very relevant across all higher education institutions as it investigates and influence factors adopting to Big Data and Analytics in learning institutions because it can predict uncertain factors limiting or hindering utilization of Big Data Analytics in higher education learning curves. (Julius Murumba, Elyjoy Micheni, 2017).

Higher education with the use of Big Data analytics and its tools has improved decision making for administration department more efficiently as the level of accuracy is exact compared to past decades where Big Data Analytics was not yet revealed. With pre-defined decision making, Big Data Analytics has open doors for more opportunities to higher education to explore industry in a competitive edge. So as to say, higher education dynamics has produced a modern way of solving mathematical problems that can affect student’s learning or generally the whole entire wealth of a particular higher educational institution.

In terms of assessing students, computerized systems for big data can provide a real time analysis as data mining and data analytics tools they can give an immediate feedback to lecturers and administration department for student’s performance hence quick decisions will be made if needed. Underlying patterns displayed by computerized systems can identically predict and visualize students who are not performing well and students who performing hence satisfying student management systems.

APPLICATION AREA

Education is one of significant and essential sectors in the world because without education things will be difficult to execute. As for individual to acquire theoretical knowledge about a particular aspect he/she need to be educated. Computer-based Systems are very important in all business applied areas for example, in retail industry, finance, Agriculture, Health etc. But speaking of nowadays Computer Systems are very dominant in Higher Education therefore, for learning and teaching to be effective and appealing, a computer-based system should be there to make this easier and possible. As we know that higher education is the most level of education where professionalism is taken into consideration, as students are preparing to go to the industry. However, higher education is the most education level that facilitate in utilizing the computer-based systems as most powerful tool in teaching, learning and monitoring. Teaching uses computer-based systems for preparing lessons, storage for students’ s work, for hosting online lessons, for demonstrations and monitoring by lectures etc. Students also known as learners capitalize computer-based system as use them to complete assignments, complete tutorials, for researching and many more. Not forgetting the Administration department as they are also utilizing the computer system by for data analysis, data capturing, data storage etc.

Higher education institutions are facing challenges in terms of handling big data for example, Botswana Accountancy College (University institutions) located in Botswana is facing challenges in handling and managing big data. Botswana Accountancy College (BAC) deals with big volumes data such as students’ personal information, students marks, programs offered, graduated students, students enrolled under a certain programme, students enrolled in sports activities, sponsored students and unsponsored students etc. There are a number of expected challenges related with the usage of analytical tools or methods for massive volumes of data in higher education. A few of these incorporate challenges related with receiving users to acknowledge big volumes of data as a channel for embracing modern forms and alter administration or management. Also, they are huge charges related with collecting, loading and generating procedures or algorithms to mine information or data, a progress that tend to be time overwhelming and complicated. Besides, most of higher education institutional data systems are not interoperational, so amassing administrative or regulatory data, online data and classroom can posture extra challenges (Daniel, B. K. & Butson, R, 2013).

Massive Volumes of Structured Data Botswana Accountancy College (BAC) has, is great opportunity as it would open doors in utilizing big data techniques and tools in handling and analyzing big data thus helping to predict future comings. Also, being able to handle and analyze Big Data by Botswana Accountancy College (BAC) would add value in terms of surpassing other profitable educational institutional commercials in marketing as more customers and clients will be gained by BAC due its powerful tools and techniques in handling Big Data infrastructure which will generate more income for the institution. With big volumes of data for students, such as enrollments of students, scholarly and disciplinary archives, university institutions have the information sets required to assist or help from analytics that are targeted. Huge volumes of data and analytical tools in higher education can be transformational, modifying the existing developments of the institution for administration department, teaching, learning, academics contributing to arrangement and prepared results and assist in addressing modern challenges that are facing university institutions (Baer, L. & Campbell, J., 2014). Furthermore, as BAC is dealing with structured or relational data it doesn’t mean that they will not come across challenges in analyzing and interpret data as they will encounter situations like data redundancy which will cause data inconsistency. Data Normalization will be definite techniques in addressing these situations in order to reduce data duplication.