Performance Comparison of Decision Stump and J48 Classification Algorithm on the Programming Skill of IT Students

Rhoderick D. Malangsa, Alex C. Bacalla

Abstract


Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification is an important data mining technique with broad applications and used to classify each item in a set of data into one of predefined set of classes or groups. There are 117 instances from sophomore IT student of Southern Leyte State University .This paper has been carried out to make a perfor-mance evaluation of Decision Stump and J48 classification algorithm by percentage splitting of instances into 66% training and 34% testing, and to find out if there are any patterns in the available data that could be useful for predicting students’ programming skill at the university based on their personal, pre-university grade and current performance in first year. The experiments results shown in this paper are about classification accuracy, ROC, and Precision. The results in the paper on this dataset also show that the efficiency and classification accuracy of J48 classification is better than that of Decision Stump.

Keywords


Data Mining, Classification, Decision Stump, J48 Algorithm

Full Text:

JSET 019

References


Alaa el-Halees. 2009. Mining students data to analyze e-Learning behavior: A Case Study.

Bharadwaj BK, Pal S. 2011. Mining educational data to analyze students’ performance. International Journal of Advance Computer Science and Applications (IJACSA), Vol. 2, No. 6, pp. 63-69.

Hijazi ST, Naqvi RSMM. 2006. Factors affecting student's performance: A case of private colleges. Bangladesh e-Journal of Sociology, Vol. 3, No. 1.

Yadav SK, Bharadwaj BK, Pal S. 2011. Data mining applications: A comparative study for predicting students’ performance. International Journal of Innovative Technology and Creative Engineering (IJITCE), Vol 1, No. 12, ISSN:2045-8711.

Tsai CF, Lin YC, Wang YT. 2009. Discovering stock trading preferences by self organizingmaps and decision trees. International Journal on Artificial Intelligence Tools, 18(4), pp. 603-611. http://dx.doi.org/10.1142/S0218213009000299

Kovacic ZJ. 2010. Early prediction of student success: Mining student enrollment data. Proceedings of Informing Science & IT Education Conference.

Baker R, Corbett A, Koedinger K. 2004. Detecting Student Misuse of Intelligent Tutoring Systems. Intelligent Tutoring Systems. pp.531–540.

Barnes T. 2005. The q-matrix method: Mining student response data for knowledge. In Proceedings of the AAAI- Workshop on Educational Data Mining.

Data Mining 2009: 2nd International Conference on Educational Data Mining, Proceedings, Cordoba, Spain.

Margaret H, Danham S, Sridhar 2006. Data mining: Introductory and advanced topics. Person Education, 1st ed.

Witten IH, Frank E. 2005. Data mining: Practical machine learning tools and techniques with Java implementations. Morgan Kaufmann: San Francisco.

Yadav SK, Bharadwaj BK, Pal S. 2011. Data mining applications: A comparative study for predicting students performance. International Journal of Innovative Technology and Creative Engineering (IJITCE), Vol 1, No. 12, ISSN:2045-8711.


Refbacks

  • There are currently no refbacks.