A Study of First-Year Students’ Attitudes toward Programming in the Innovation in Educational Technology Course

The purpose of this study was to investigate the relationship between students’ attitudes toward programming, gender, and learning performances. The survey used for measuring students’ attitudes toward programming consisted of 20 questions on a five-point Likert scale in five dimensions (meaningfulness, interest in programming, self-efficacy, creativity, and collaboration). Ninety freshmen who had basic programming experience by using block-based programming in the Innovation in Educational Technology course were asked to take the survey. The overall reliability of the survey was found to be 0.93. The results showed that there was no significant difference between male and female freshmen in attitude toward programming, but there was a significant difference among different learning performances in dimensions of interest in programming, self-efficacy, and creativity. We performed pairwise comparisons at the same level of significance by using Fisher’s least significant difference (LSD) method to test which group differs from the other groups. The results found that low-performing students’ attitudes toward programming in dimensions of interest in programming, self-efficacy, and creativity were the lowest of all types of students. This is a challenge for instructors in planning learning activities to encourage low-performing students to have a more positive attitude toward programming.

Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods

In this study, different models were created to reduce bias by ensemble learning methods. Reducing the bias error will improve the classification performance. In order to increase the classification performance, the most appropriate ensemble learning method and ideal sample size were investigated. Bias values and learning performances of different ensemble learning methods were compared. AdaBoost ensemble learning method provided the lowest bias value with n: 250 sample size while Stacking ensemble learning method provided the lowest bias value with n: 500, n: 750, n: 1000, n: 2000, n: 4000, n: 6000, n: 8000, n: 10000, and n: 20000 sample sizes. When the learning performances were compared, AdaBoost ensemble learning method and RBF classifier achieved the best performance with n: 250 sample size (ACC = 0.956, AUC: 0.987). The AdaBoost ensemble learning method and REPTree classifier achieved the best performance with n: 20000 sample size (ACC = 0.990, AUC = 0.999). In conclusion, for reduction of bias, methods based on stacking displayed a higher performance compared to other methods.