• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2019, Volume: 12, Issue: 6, Pages: 1-8

Original Article

Development of Random Tree Based Student Competency Model in Java Programming


Objective: Java programming is perceived to be a difficult subject. The educated programmer is in demand in the local and international market in creating computer application. However, no model that would assist the teacher in identifying the needed competency in Java programming that will make it less stressful for the student to understand the topic. Methods: data mining technique was explicitly utilized the random tree to predict the academic performance of the students. The purposeof gathering the data was through survey questionnaire for those fifty (50) are enrolled IT students in the IT_103 Java Programming subject answered and poll. Java Programming class schedules are: 10.30-12.00 MTh (Monday and Thursday), and 4.00-5.30 TFri (Tuesday and Friday). The researcher observed the schedule in conducting the survey. Findings: The algorithm generated by the decision tree model shows that conditional statement, operators, arrays and loops are important Java programming competency. It means that the student should learn mastery in the topics for them to become expert in java programming. Application/Improvements: In the formulation of the course syllabus for the Java Programming more contact hours should be given to the following topics conditional statement, Operators Arrays, and Loops for the student to understand more the problem. For more improvement of the subject area, theuse of another type of that the data mining technique is recommended and with additional parameters to test the accuracy in predicting Java competency.

Keywords: Array, Data Mining, Information Technology, Java Programming, Loops, Random Tree Algorithm


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