Machine learning and data mining techniques can be used to develop business intelligence applications in the education sector. These applications analyze student data to provide insights into administrative efficiency, academic outcomes, and workforce management. Examples include using classification and clustering on student data in reporting tools or dashboards. Case studies demonstrate using spreadsheets to track student activities, digital dashboards to view key performance indicators, and data warehouses to integrate and analyze historical student data. Business intelligence becomes more important for strategic decision making and is expected to incorporate more machine learning, integration with other systems, and data productivity tools.

1. M121SSL Business Analytics And Intelligence
Answer:
Topic: Use of Machine Learning and Data Mining to develop Business Intelligence
application for an Education Sector
Introduction
Today technology become the most mandatory need to every industry of different sectors.
Machine learning and data mining are the techniques applied to the data set or to the
computer for getting results.
The use of the Business Intelligence applications brought the traditional approach of
handling work to fully automatic (Tavera Romero et al.2021).
Various industries are there who have been implemented BI technology to achieve higher
and greater outcome.
Concept Of Machine Learning (ML)
The concept of Machine Learning is the science of getting the computer to the act without
being programmed explicitly (Bi et al. 2019). It is also considered as the advanced
innovation that helped an individual to enhance the professional and industrial processes.
For example:
Image Recognition: ML is used for detecting the face in a image by measuring the three
pixel.
Medical Diagnosis: It helps to analyze the clinical parameters in order to monitor the health
condition.
Concept Of Data Mining (DM)
The process of data mining is used to look for the patterns, correlations, and anomalies
within the large data sets in order to predict the results (Olson and Lauhoff 2019). In
education sector this process enhance the learning and teaching processes.
For example:
Learning Management Systems (LMS): It is the IS which is used for tracking the information
like when the student will access each learning object, how many times the object is being

2. displayed, and how much they have been accessed.
Business Intelligence Applications
It is refers to the technology which helps the business in order to analyze, contextualize, and
organize the data of the business around the company (Teruel et al. 2019). There are
various types of techniques and tools are there which help to convert the raw data into
some meaningful information.
For example:
Data Visualization Software: Used to track and manage the vital metrics and KPIs that allow
the user to build a dashboard.
Embedded Business Intelligence Software: Integrated with the portals in order to provide
capabilities like interactive dashboard, reporting, and so on.
Use Of ML And DM To Develop BI
In the applications of Business Intelligence, the Machine Learning is vital in order to
perform tasks (Mahesh 2020). Based on the data sets present in the application, ML build an
algorithm based on the pattern which is recognized from data sets.
The application BI uses the technique of data mining in order to transform the raw data into
some meaningful information (Gan et al.2017). It picks the information from the available
data set and then compare it to assist business make decision.
Business Intelligence In Education Sector
The technology of Business Intelligence is used in order to deliver the data-based insights
which generally underpin the 3 main areas of assessment and planning such as (Scholtz,
Calitz and Haupt 2018):
Administrative efficiency and effectiveness
Academic student experience and outcomes
Workforce moral and management
BI And DM Techniques In Education
Business Intelligence Technique
Data Mining Technique
Reporting and query tools

3. Classification
OLAP (Online Analytical Processing)
Clustering
Statistical Analysis
Regression
Text Mining
Outer
Data Visualization
Sequential Patterns

4. Prediction
Association Rules
Big Data Technology
Big Data Technologies are generally the software utility which is designed for extracting,
processing, and analyzing the information from the large data which is unstructured
(Santoso 2017). This kind of data cannot be handled by using the manual data processing
software. Below some of the trending big data technologies are mentioned:
Artificial Intelligence
NoSQL Database
R Programming
Data Lakes
Predictive Analytics
Apache Spark
Advantage And Disadvantage Of ML
Advantage Of ML
Easily identify the patterns and trends
No intervention of human required
Continuous improvement
Handling multi-variety of data
Disadvantage Of ML
Data Acquisition
Resources and Time
Outcome interpretation
Susceptibility of high error
Advantage And Disadvantage Of DM

5. Advantage of DM
Disadvantage of DM
Retail/ Marketing
Privacy Issues
Banking/ Finance
Security Issues
Manufacturing
Misuse of inaccurate information
Governments
Advantage And Disadvantage Of Big Data
Advantage Of Big Data
Brings innovative solution
Improve the research and science
Improve public health and healthcare

6. Helps in sports, financial trading, etc.
Disadvantage Of Big Data
Cost huge money for traditional storage
Several data is unstructured
Increase the social stratification
Case Study #1: Spreadsheet
It is the computer application used for storage, organization, and analysis of the data in a
tabular format. The software operates on the data which is entered in the cells of the table
(Niazkar and Afzali 2017). The spreadsheet is mainly used in universities by the students in
order to track their daily academic activities easily.
Case Study #2 Digital Dashboard
It is the type of graphical UI (User Interface) which is often helps to provide a complete view
of the KPIs (Key Performance Indicators). In education sectors, teachers, students, and
other staffs get benefited from it as it give them the statistics, and capabilities to perform
online task (Seiler et al. 2019).
Case Study #3: Data Warehouse
It is the system which is generally used for data analysis and reporting and it is also
considered as the key component of BI. Data Warehouses are the central repositories of the
data which is integrated from more than one disparate sources. Here, all the historical and
current data are stored in a single place which later used to generate report.
Statistics #1
Statistics of the utilization of Business Intelligence Software:
Statistics #2
Business Intelligence in a post COVID world:
Future Of Business Intelligence
Collaboration: It will become more collaborative in order to facilitate the teamwork.
Integration: With BI, the third-party systems will be easily intertwined.
Machine Learning: AI analyzes the previous data in order to give forecasting and insight.
Data Productivity: The features which are focused on productivity will respond to inquires
automatically and bring essential data to the users.
Conclusion
Business Intelligence become the necessity for many companies today because of its
functioning capabilities.

7. The utilization of Business Intelligence applications helps to ease the work load, streamline
the workflows, and provide predictive abilities.
References
Bi, Q., Goodman, K.E., Kaminsky, J. and Lessler, J., 2019. What is machine learning? A primer
for the epidemiologist. American journal of epidemiology, 188(12), pp.2222-2239.
Gan, W., Lin, J.C.W., Chao, H.C. and Zhan, J., 2017. Data mining in distributed environment: a
survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(6),
p.e1216.
Mahesh, B., 2020. Machine Learning Algorithms-A Review. International Journal of Science
and Research (IJSR).[Internet], 9, pp.381-386.
Niazkar, M. and Afzali, S.H., 2017. Analysis of water distribution networks using MATLAB
and Excel spreadsheet: h-based methods. Computer Applications in Engineering Education,
25(1), pp.129-141.
Olson, D.L. and Lauhoff, G., 2019. Descriptive data mining. In Descriptive Data Mining (pp.
129-130). Springer, Singapore.
Santoso, L.W., 2017. Data warehouse with big data technology for higher education.
Procedia Computer Science, 124, pp.93-99.
Scholtz, B., Calitz, A. and Haupt, R., 2018. A business intelligence framework for
sustainability information management in higher education. International Journal of
Sustainability in Higher Education.
Seiler, L., Kuhnel, M., Ifenthaler, D. and Honal, A., 2019. Digital applications as smart
solutions for learning and teaching at higher education institutions.
In Utilizing learning analytics to support study success (pp. 157-174). Springer, Cham.
Tavera Romero, C.A., Ortiz, J.H., Khalaf, O.I. and Ríos Prado, A., 2021. Business intelligence:
business evolution after industry 4.0. Sustainability, 13(18), p.10026.
Teruel, M.A., Maté, A., Navarro, E., González, P. and Trujillo, J.C., 2019. The New Era of
Business Intelligence Applications: Building from a Collaborative Point of View. Business &
Information Systems Engineering, 61(5), pp.615-634.