Students academic performance using clustering technique
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This document summarizes a study analyzing students' academic performance data. The study collected internal and external marks for 45 students over 5 semesters. It cleaned the data, transforming the marks into sums, and used k-means clustering to group students into 4 categories (excellent, good, fair, poor) for each semester based on their internal and external marks. The analysis found the clusters followed the same performance pattern each semester, with students scoring higher internally also scoring higher externally, indicating a direct relationship between internal and external marks. The study concluded a student's university exam performance can generally be predicted from their internal marks.
Students academic performance using clustering technique
- 1. Students Academic Performance Knowledge Discovery from Data
- 2. Introduction.. Our project aim is to find students academic performance and find out whether there is any general pattern in their marks and performance. So here ,We are analyzing both internal and external marks of a student. We did the following KDD preprocessing steps to mine our data.
- 3. Learning the application domain Learning the application domain is the first step in KDD process . Need to have a clear understanding about the application domain and our objectives. The institution considered for mining is MCA batch of Rajagiri College of Social Sciences. We collected all previous year academic record from the department of computer science
- 4. Create a target data set: data selection We selected 2007-2010 batch marks for analysing the pattern. There were around 45 records(45 students). Both the internal and external marks of each student were selected, in order to find out the performance pattern.
- 5. Internal & External Dataset
- 6. Data cleaning & preprocessing Data cleaning is the step where noise and irrelevant data are removed from the large data set. This is a very important pre-processing step because our outcome would be dependent on the quality of selected data. Remove duplicate records, enter logically correct values for missing records(absent students), remove unnecessary data fields and standardize data format.
- 7. There was no much duplicate data or unnecessary data in the collected record . The dataset was partially cleaned. Student internal mark and external mark were stored in different records. By applying data integration these records were integrated into one record. The new dataset consist of internal mark details and external mark details of each student in one record.
- 9. Data reduction & transformation Data is transformed into appropriate form for making it ready for data mining step. The dataset contains marks of 5 theory paper and 2 lab paper of all 5 semesters. These marks are transformed into sum of internal marks and sum of external marks of each student for the easiness of analysing the pattern.
- 11. Cluster Analysis The data mining technique we used here is clustering. A cluster is a collection of data objects that are similar to one another within same cluster and are dissimilar to objects in other cluster. We first partitioned the set of data into groups based on data similarity and then assign labels Choosing functions of data mining
- 12. K-MEANS Partitioning The K-means algorithm takes input parameter k and partitions the set of n objects into k clusters. Here we selected no: of cluster as 4 Objects are distributed to a cluster based on cluster center to which it is nearest. For each semester we found out the clusters separately and labeled them as students Excellent, Good, Fair and Poor Choosing mining algorithms
- 13. The Tool used for pattern evaluation is ORANGE
- 15. No of cluster selected is 4
- 17. Semester 2
- 18. Semester 3
- 19. Semester 4
- 20. Semester 5
- 22. Semester 1
- 23. Semester 2
- 24. Semester 3
- 25. Semester 4
- 26. Semester 5
- 28. Data mining search for patterns of interest From the mining process we found that “All the 5 semester clusters followed the same pattern of performance”. A student with high internal mark has higher external marks and a student with less internal marks has less external marks. There is a direct relation between the internal and the external marks. At some case this evaluation is not valid, cases like Being absent for internal exam and scoring high marks for the externals (vice versa)
- 29. CONCLUSION A students performance in his university exam can be predicted with the help of his internal marks. There is a direct relation between the internal and the external marks. A student with low internals will get low marks for externals too
- 30. Use of discovered knowledge representation
- 31. Thank You