Discuss About The Learning Analytics Educational Data Mining?
Data mining is the procedure of finding the valuable and important knowledge by the analysis of bulk amount of data, recorded and kept in data warehouses or databases (Grossman et al. 2013). This process is done by several techniques of data mining like artificial intelligence, statistics and machine learning. A person who does data mining is known as a data miner. AIH offers financial help for the students. The business analyst or data miner has several business objectives for this particular case study (Witten et al. 2016). They are as follows:
iii) The third objective of data miner is to understand and discover the main factors that will influence the output.
The students of AIH will be getting financial help from the institution. AIH will develop their own programs of finance with all the existing institutions of finance (Larose 2014). However, there are various risks and threats. There is a high chance of risk of collecting all types of bad debts from this scheme of financially helping the students. This risk can create major difficulties in finance for both the university and the students (Liu and Motoda 2012). The initiative of the organization is that they will pay the money and the students can study without worrying. The organization has hired a data miner, who is performing a data mining project in the organization (Siemens and d Baker 2012). This data mining process would be helpful for the organization as it will help to obtain the organizational goals and objectives easily. The motivation of the project is those students and individuals, who are not able to complete their education because of expenses. The business is not utilizing the data mining yet but is planning to use it. A written report is expected from the top management level as they will provide the permission for any type of activity in the organization. This data mining would be the key technology in the business (Freitas 2013). The users of this project are the members of the organization. The main need and the expectation of the users of this project are to reduce or mitigate the financial risk of the project by means of data mining and also to obtain the organizational goals.
The students of AIH will be getting financial help from the institution. AIH will develop their own programs of finance with all the existing institutions of finance. They will not be utilizing any program that is sponsored by government (Braha 2013). AIH will become the guarantor for the funds and will be sourcing the funds from its existing institutions. They have several goals and objectives for their business. They are as follows:
i) Increment in the enrolment of students
ii) Providing assistance for the fees of students
iii) Providing assistance for the expenses of the regular needs of the student
iv) Offering help to the students to work less and study more during their academic semesters.
v) Encouraging all the professionals, international students, potential groups, low income earners, senior citizens, holidaymakers and rural residents to continue their education without any type of tension of expenses (Shmueli and Lichtendahl Jr 2017).
vi) Performing financial management in the business.
The business success criteria of this organization are as follows:
The success criteria of any business are assessed by the management level of that organization.
Activities: Inventory of Resources
The resources of any project are the amalgamation of the personnel, data, computing resources and software. The details of the resources are given in the following table:
1. Management bodies
1. Operational Data
1. Data Mining tools like MATLAB, StatSoft Statistica, Rattle
2. Data Miner
2. Fixed Extracts
2. Operating system
3. Access to Live Warehoused
3. MS SQL Server
4. Technical Team
The data mining is done by analyzing various data. These data is collected from various sources. For this particular project, all the data related to the project should be collected from the several written documentation (Bhardwaj and Pal 2012). However, this project has risks related to finance. The sources of knowledge of this project should be the financial experts as they can guide for any type of risk related to finance.
Certain tools and techniques do the data mining process. Several tools are available in the market. The data miner can for various tools like MATLAB, StatSoft Statistica, Rattle. The MATLAB is the best tool for data mining (Liao, Chu and Hsiao 2012). A proprietary programming language enables all functions like algorithm implementation, manipulations of matrix and data plotting.
The basic requirements of this project regarding the schedule of completion are as follows:
The requirements of the project regarding quality of results are as follows:
The requirements of security are as follows:
The requirements of legal issues are as follows:
The assumptions of a project define the expectations or the probable outcomes of the project (Liao, Chu and Hsiao 2012). These assumptions provide the guidelines of the outcome for any project. The overall assumptions of this particular project are as follows:
There are several types of risks present in any business. The main types of risks are the business, organizational, financial, and technical and data risks.
Business Risk: This type of risk includes the competition between two organizations, and the better result is considered.
This type of risk occurs when two organizations are providing same goods or services (Baker and Inventado 2014). The business risk can be mitigated by following perfect business strategies for the organization.
Organizational Risks: This type of risk includes the lack of skilled employees, department requesting project not having funding for the project.
This type of risk occurs when the organization is lacking skilled employees and the management is not providing enough funds (Romero and Ventura 2013). This type of can be mitigated by recruiting experienced employees for the organization.
Financial Risks: This type of risk includes lack of funds for the project and further funding depends on the initial results of data mining.
This type of risk occurs when funds provided for a project is not enough and the management will sanction more funds only if the initial results are good (Shmueli and Lichtendahl Jr 2017). This type of risk can be mitigated by taking help from financial experts.
Technical Risks: This type of risk includes all the technological risks involved in the project.
Technical risk occurs when there is any problem in the system of the organization. this type of risks can be mitigated by keeping a technical team in the organization.
Data Risks: This is the most dangerous type of risk that include all the data and the sources of data.
Data risks occur when data is retrieved from a database (Romero and Ventura 2013). This can be mitigated by implementing perfect security measures for the data.
A data mining goal of a project states the objectives of the project in technical terms. The data mining goal of this project deals with the business objectives with respect to technical terms (Freitas 2013). The probable outcome of the project enables the objectives to be achieved easily. The data mining goals enable the technical outputs of the project.
Activities: Translation of Business Questions to Data Mining Goals
The financial department of the organization can be segmented into the guidelines taken from the financial experts and the actual data collected from the sources.
There can be several problems in data mining. The problems can be divided into three parts. They are the mining methodology and user interaction, performance issues and diverse data type issues (Baker and Inventado 2014). The mining methodology is the mining various types of knowledge in databases and interactive mining of knowledge at several levels of abstraction. The diverse data types issues include handling complex data types.
Baker, R.S. and Inventado, P.S., 2014. Educational data mining and learning analytics. In Learning analytics (pp. 61-75). Springer New York.
Bhardwaj, B.K. and Pal, S., 2012. Data Mining: A prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418.
Braha, D. ed., 2013. Data mining for design and manufacturing: methods and applications (Vol. 3). Springer Science & Business Media.
Freitas, A.A., 2013. Data mining and knowledge discovery with evolutionary algorithms. Springer Science & Business Media.
Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V. and Namburu, R. eds., 2013. Data mining for scientific and engineering applications (Vol. 2). Springer Science & Business Media.
Larose, D.T., 2014. Discovering knowledge in data: an introduction to data mining. John Wiley & Sons.
Liao, S.H., Chu, P.H. and Hsiao, P.Y., 2012. Data mining techniques and applications–A decade review from 2000 to 2011. Expert systems with applications, 39(12), pp.11303-11311.
Liu, H. and Motoda, H., 2012. Feature selection for knowledge discovery and data mining (Vol. 454). Springer Science & Business Media.
Romero, C. and Ventura, S., 2013. Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), pp.12-27.
Shmueli, G. and Lichtendahl Jr, K.C., 2017. Data Mining for Business Analytics: Concepts, Techniques, and Applications in R. John Wiley & Sons.
Siemens, G. and d Baker, R.S., 2012, April. Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254). ACM.
Witten, I.H., Frank, E., Hall, M.A. and Pal, C.J., 2016. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann.
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