Prima Vitasari

Applying variable precision rough set model for clustering student suffering study's anxiety

Computational models of the artificial intelligence such as rough set theory have several applications. Data clustering under rough set theory can be considered as a technique for medical decision making. One possible application is the clustering of student suffering studys anxiety. In this paper, we present the applicability of variable precision rough set model for clustering student suffering studies anxiety. The proposed technique is based on the mean of accuracy of approximation using variable precision of attributes. The datasets are taken from a survey aimed to identify of studies anxiety sources among students at Universiti Malaysia Pahang (UMP). At this stage of the research, we show how variable precision rough set model can be used to groups student in each studys anxiety. The results may potentially contribute to give a recommendation how to design intervention, to conduct a treatment in order to reduce anxiety and further to improve students academic performance.

Mining Interesting Association Rules of Student Suffering Mathematics Anxiety

Up to this moment, association rules mining are one of the most important issues in data mining application. One of the commonly and popular techniques used in data mining application is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, so called SLP-Growth (Significant Least Pattern Growth) proposed by [9] for capturing interesting rules in student suffering mathematics anxiety dataset. The dataset was taken from a survey on exploring mathematics anxiety among engineering students in Universiti Malaysia Pahang (UMP). The results of this research will provide useful information for educators to make a decision on their students more accurately, and to adapt their teaching strategies accordingly. It also can be helpful to assist students in handling their fear of mathematics and useful in increasing the quality of learning.

Mining Interesting Association Rules of Students Suffering Study Anxieties Using SLP-Growth Algorithm

One of the most popular techniques used in data mining applications is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, called SLP-Growth Significant Least Pattern Growth for capturing interesting rules from students suffering mathematics and examination anxieties datasets. The datasets were taken from a survey exploring study anxieties among engineering students in Universiti Malaysia Pahang UMP. The results of this research provide useful information for educators to make decisions on their students more accurately and adapt their teaching strategies accordingly. It also can assist students in handling their fear of mathematics and examination and increase the quality of learning.

Mining critical least association rules of student suffering language and social anxieties

One of the commonly and popular techniques used in data mining application is association rules mining. The purpose of this study is to apply an enhanced association rules mining method, so called significant least pattern growth (SLP-growth) proposed by Abdullah et al. (2010a) for capturing interesting rules in student suffering language and social anxieties dataset. The datasets were taken from a survey among engineering students in Universiti Malaysia Pahang (UMP). The results of this research will provide useful information for educators to make a decision on their students more accurately, and to adapt their teaching strategies accordingly. It can be helpful to assist students in handling their fear of language and social. Furthermore, it is also useful in increasing the quality of learning.

The effectiveness of biofeedback training among university students

University students need some support program to improve the academic performance. Biofeedback training is one of the effectiveness programs to help students handling the problem regarding of their study process. In this research, the biofeedback training to manage study anxiety in order to increase academic performance among students is proposed. The research was selected for 12 participants. They are divided into two equal groups, the experiment and the control groups. The training runs on six sessions with the experiment groups received full training and no training of the control groups. The result shows that the experiment groups perform better in reducing anxiety levels as well as increase academic performance than that the control groups. Based on these results, biofeedback training can be concluded as an effective program to improve academic performance among university students. Therefore, the participants should be practiced the techniques to get the intensive level in mastering regarding of managing study anxiety and improving academic performance.