The model for predicting students’ success in acquiring programming knowledge and skills is presented in this paper. In order to collect the data needed for development of the model, 159 undergraduate IT students from Faculty of Technical Sciences in Čačak were analyzed. Besides the score on programming knowledge test, the following data were also gathered for each student: high school, the subject he/she took at the entrance exam, size of student’s birthplace, average high school grade, points from high school, gender, previous education, existence of IT educational profile in high school, study year, percentage of attendance on classes, reason for enrolment, subjective assessment of preparedness for programming, solving sequential tasks, type of programming student prefers, subjective assessment of preparedness for working in industry, solving tasks with branching and cycle, solving complex tasks, knowledge level, formal education, informal education, Kolb's learning style. In order to predict students’ success in learning programming multilayer perceptron was used with backpropagation learning algorithm. The cross-validation methodology was used for the training and testing of the classifiers. Transformation process is performed on the points students achieved on the test in order to get three categories related to success. Based on the results about the relevance of the parameters, the model reached an accuracy of 92.3%. In order to facilitate the use of the model, a Web-based application for displaying the results was created. It is primarily intended for teachers with no experience in working with neural networks, who can use it for planning the teaching.

中文翻译：

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人工神经网络模型预测学生学习编程的成功

本文提出了预测学生成功学习编程知识和技能的模型。为了收集开发模型所需的数据，分析了Čačak技术学院的159名IT专业本科生。除了编程知识测试的分数外，还为每个学生收集了以下数据：高中，他/她参加入学考试的科目，学生的出生地大小，高中平均成绩，高中分数，性别，以前的教育，高中IT教育概况的存在，学习年份，上课率，入学原因，对编程准备的主观评估，解决相继任务，喜欢的编程类型，对行业工作准备的主观评估，用分支和周期的方式解决任务，解决复杂的任务，知识水平，正规教育，非正式教育，科尔布的学习风格。为了预测学生在学习编程方面的成功，将多层感知器与反向传播学习算法一起使用。交叉验证方法用于分类器的训练和测试。为了获得与成功相关的三个类别，对学生在测试中获得的分数进行了转换过程。根据参数相关性的结果，该模型的准确率达到92.3％。为了促进模型的使用，创建了一个用于显示结果的基于Web的应用程序。它主要面向没有使用神经网络经验的老师，他们可以使用它来计划教学。