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Application of data mining techniques in designing knowledge base on student competency at the post graduate level

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term papers
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  1. Introduction
  2. Review of literature
  3. The study
  4. Tool for the study: Data mining
    1. Levels of data mining
    2. Associative data mining
  5. Results of the study
  6. Limitations of the study
  7. Conclusions
  8. References

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. [...]

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