The quality of education is crucial for its competitiveness in the developing world. International tests are organized at regular intervals to measure the quality of education and to see the place in the ranking of countries. The surveys on these examinations have provided a large number of variables that can be effective on the scores of the tests, including family, teacher, school and course equipment and information communication technologies, etc. The important question is which variables are relevant for the students' achievement in these tests. We investigated the barriers of mathematics success of Turkish students in the TIMSS exam and compared their status with Singaporean students who took part in at top of the ranking in the exam. For this, we employed the adaptive elastic net which is one of the regularized regression methods to dataset and compared their prediction accuracy according to three different alpha levels [0.1; 0.5; 0.9] to determine the model that has high variable selection ability with optimal prediction. The adaptive elastic net with the alpha level [0.9] was selected as superior to others. As the findings, a technology-oriented education system can help to success of the students in Turkey and the countries having similar experiences in international tests.
Informative gene selection for microarray classification via adaptive elastic net with conditional mutual information