Maintenance of academic standards is the prime concern of any educational institution. Education at the Masters level, involves the conscious involvement of time, money and effort by the candidates. With the industrial downturn and reduction in the demand for graduates at the job market due to recession, there may be many who vow to equip themselves for the futures through higher education. However without a clear and well thought through approach, institution may fail to identify the right candidate who would support the cause of maintaining high academic standards. In fact, it is much likely that many companies may prefer to recruit from B schools which give considerable importance to scientific student admission procedures. The most valuable asset of any organization is its Knowledge and the ability to use this knowledge appropriately to enhance the output. For an Educational Institution, the core strength would be the in-depth knowledge of the predictors of a student's performance. Many independent variables such as Gender, Educational background, Work experience might influence a candidate's performance in the Post Graduate level. Information generated about the candidate can be converted into fruitful knowledge about future trends.
[...] With the use of an effective data mining tool, an institution can determine relationships among "internal" factors such as Gender, Educational background, Work experience, and "external" factor such as student performance in their Post graduation LEVELS OF DATA MINING Based on the technology and statistics perspective, there could be different levels of data mining Directed data mining allows business users to infer using classes and clusters Undirected data mining uses clusters to identify pure statistical patterns Association helps in discovering links between variables. [...]
[...] In this case, data base comprised of the profile of the students for three consecutive years from and 2007 in terms of their X standard(X), Higher Secondary Undergraduate Post Graduate University Exam(P) and Post Graduate Internal all subject Viva Performance being banded, MAT / TANCET entry to the course, Gender(M/F), Prior Work Experience(Yes/No). Thousands of associative rules were generated through the XLMINER which can be interpreted as follows: Example: Conf Antecedent MAT, U4, Consequent No, P6, X5 Support(a) 6 Support(c) 9 Support U 6 Lift Ratio 6.444444 Association rules provide information of this type in the form of "if-then" statements. [...]
[...] This has been used to identify the factors contributing to the success of the students in their higher education and to discover potential student groups with similar characteristics and the degree of association to basic variables or scores TOOL FOR THE STUDY DATA MINING Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. [...]
[...] Next level of data mining can also be attempted to arrive at more meaningful inferences CONCLUSION Master of Business Administration (MBA) program admission wing is keen in thoroughly examining the admission criteria they can use to admit student into their graduate programs. The results of this study suggest that the Higher Secondary, and Gender serve as valid indicator for students who are likely to perform successfully in a professional course like MBA. References: Galit Shmueli, Nitin R. Patel, Peter C. [...]
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