AI Summary & Key Takeaways
AI InsightThe overall objective of this study is to understand how the fuzzy logic theory can be used in measuring the programming performance of the undergraduate students, as well as proving the advantages of using fuzzy logic in evaluation of students’ performance. 336 students were involved in the sample of this quantitative study.
📌 3 Key Takeaways
Abstract
The overall objective of this study is to understand how the fuzzy logic theory can be used in measuring the programming performance of the undergraduate students, as well as proving the advantages of using fuzzy logic in evaluation of students’ performance. 336 students were involved in the sample of this quantitative study. The first group was consisted of 150 students, whereas the second group was consisted of 186 students. Cluster analysis was also conducted in order to ensure the neutrality of sample. The rule-based intelligent fuzzy logic assessment logic (FLAL) system was developed. This system has a flexible database in order to assess the academic programming performances of students. Therefore, an absolute evaluation system was used in order to calculate the second group’s performance. On the other hand, FLAL system was applied to the first group to determine their programming performance. A Mamdani-type fuzzy logic algorithm mechanism having two inputs and one output was utilized. An independent sample T test was used in analyzing the data sets. As a result, there was a significant difference between first and second groups’ results in favor of the first group. While 29 students comprised of 19.3% of all the students failed in the flexible percentage system, 41 students comprised of 22% of all the students failed in the absolute evaluation system evaluating their grades via fuzzy logic system. By increasing the input parameters of the fuzzy logic rules, the results can be addressed more efficiently.
Özet
The overall objective of this study is to understand how the fuzzy logic theory can be used in measuring the programming performance of the undergraduate students, as well as proving the advantages of using fuzzy logic in evaluation of students’ performance. 336 students were involved in the sample of this quantitative study. The first group was consisted of 150 students, whereas the second group was consisted of 186 students. Cluster analysis was also conducted in order to ensure the neutrality of sample. The rule-based intelligent fuzzy logic assessment logic (FLAL) system was developed. This system has a flexible database in order to assess the academic programming performances of students. Therefore, an absolute evaluation system was used in order to calculate the second group’s performance. On the other hand, FLAL system was applied to the first group to determine their programming performance. A Mamdani-type fuzzy logic algorithm mechanism having two inputs and one output was utilized. An independent sample T test was used in analyzing the data sets. As a result, there was a significant difference between first and second groups’ results in favor of the first group. While 29 students comprised of 19.3% of all the students failed in the flexible percentage system, 41 students comprised of 22% of all the students failed in the absolute evaluation system evaluating their grades via fuzzy logic system. By increasing the input parameters of the fuzzy logic rules, the results can be addressed more efficiently.