An investigation into influence factor of student programming grade using association rule mining

Computer programming is one of the most essential skills which each graduate has to acquire.However, there are reports that they are unable to write a program well. Researches indicated there are many factors can affect student programming performance.Thus, the aim of this study is to investigate the significant factors that may influence students programming performance based information from previous student performance using data mining technique. Data mining is a data analysis technique that able to discover hidden knowledge in database. The programming dataset used in this study comprises information on the performance profile of Universiti Utara Malaysia students from 4 different bachelor programs that were Bachelor in Information Technology , Bachelor in Multimedia, Bachelor in Decision Science and Bachelor in Education specializing in IT of the November session year 2004/2005. They were required to enroll introductory programming subject as requirement to graduate . The dataset consisting of 4 19 records with 70 attributes were pre-processed and then mined using directed association rule mining algorithm namely Apriori. The result indicated that the student who has been exposed to programming prior to entering university and scored well in Mathematics and English subject during secondary Malaysian School Certificate examination were among strong indicators that contributes to good programming grades. This finding can be a guideline to the faculty to plan a teaching and learning program for new registered student

Investigating teacher's integrity through association rule mining

The selection of teachers to attend trainings is currently done randomly, by rotation and not based on their work performance.This poses a problem in selecting the right teacher to attend the right course.Up until now, there is no intelligent model to assist the school management to determine the integrity level of teacher and assign them to the right training program.Thus, this study investigates the integrity traits of teacher using association rule technique with an aim, which can assist the school management to organize a training related to teachers’ integrity performance and to avoid sending the wrong teacher for the training.A dataset of Trainees Integrity Dataset representing 1500 secondary school teachers in Langkawi Island, Malaysia in the year 2009 were pre-processed and mined using apriori. Mining knowledge was analyzed based on demographic and integrity trait of teacher.The finding indicates that adaptability and stability are the weakest integrity trait among teachers.Teachers from the age group of 26 - 30 years are found to have lower integrity performance.However, other demographic factor such as gender, race, and grade position of teachers were not able to reflect their low integrity level in this study.Finally, this study produces a component of trainee selection module which can be used as guideline for school management to propose suitable training programs for teacher to improve their integrity mainly on adaptability and stability traits