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Mobile devices use in analyzing the engineering students attitude towards programming by using a fuzzy logic technique

机译:移动设备用于通过使用模糊逻辑技术分析工程学生对编程的态度

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The aim of this study is to use mobile devices in the determination of engineering students' attitudes towards programming by using a fuzzy logic technique. First of all, a mobile game that is played by engineering students is developed to make learning programming more enjoyable. After that, the proposed fuzzy logic-based attitude determination system which runs on mobile devices comes into play. Student answers and gives points between 1 and 5 to the survey questions which are presented by the developed mobile application. These points are first evaluated in the fuzzification step by using membership functions and then the fuzzied input is given to the rule base step. To get crisp output value, fuzzied output is defuzzified at the last step of the fuzzy logic-based system. Hence the attitude of the student towards programming is inferenced. The developed system is carried out with 100 first-grade students of the software engineering department. Frequency, mean, standard deviation, normality, t test, and analysis of variance (ANOVA) analyses are performed with the obtained data. Results show that the proposed fuzzy logic-based system performs much better than the classical approach. As a result of Article Reliability Analysis of the Attitude Scale Towards Mobile Learning, the scale is found highly reliable. A significant difference is found in favor of fuzzy logic-based attitude score among classical logic-based attitude scores as a result of the paired-samples t test. The results of t test and ANOVA tests according to gender, mother, and father education levels are found not statistically significant.
机译:本研究的目的是利用移动设备在使用模糊逻辑技术确定工程学生对编程的态度。首先,开发了一种由工程学生扮演的移动游戏,使学习编程更令人愉快。之后,在移动设备上运行的基于模糊的基于逻辑的姿态确定系统进入了比赛。学生答案,并在1到5之间提供由发达的移动应用程序提出的调查问题。首先通过使用隶属函数在模糊处理步骤中评估这些点,然后将模糊输入给出给规则基础步骤。为了获得清晰的输出值,在基于模糊逻辑的系统的最后一步,模糊输出在最后一步中排出。因此,学生对编程的态度被推断出来。开发系统由软件工程部门的100级学生进行。使用所获得的数据执行频率,平均值,标准偏差,正常性,T测试和方差分析(ANOVA)分析。结果表明,建议的基于模糊逻辑的系统比经典方法更好。由于物品可靠性分析态度规模朝向移动学习,尺度非常可靠。由于配对样本T检验,发现基于古典逻辑的姿态分数的模糊逻辑态度得分有利于显着的差异。根据性别,母亲和父亲教育水平的T检验和Anova测试的结果没有统计学意义。

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