In this paper, we introduce a new student responsive model to support students who use an Intelligent Tutoring System (ITS) as an E-Learning tool. We proposed a weighted-based model to estimate and suggest learning materials for students who are pursuing a computer-based course. We have built a brand new ITS called WinITS with our proposed responsive student model and deployed it in Hanoi National University of Education-Vietnam (HNUE) in the second semester of the school year 2019-2020 with a computer science course. To compare the effectiveness of applying ITS to the students, we compare test results and analyze some other aspects related to the course. On the other hand, we conducted a survey between two groups: with and without using WinITS. 63 students are volunteers who participated in the case study. Before learning, 43 students from Group 1 will take a short survey of the Felder-Silverman questionnaire to identify learning styles, after that, they go through all the lessons from the course under the support of WinITS, the lessons will be chosen to satisfy student’s need. On another side, 18 students from Group 2 will make the same test to compare the result to Group 1. In the range of research, we illustrate that our implementation shows some encouraging results such as reducing learning time, improving test score by 1.13 standard deviations, and making the lesson more interesting and flexible. The results have revealed some advantages of studying with computer-added compared to the traditional class in various ways and showed the effectiveness of the proposed model in Intelligent Tutoring Systems.

在本文中，我们介绍了一种新的学生响应模型，以支持使用智能辅导系统（ITS）作为电子学习工具的学生。我们提出了一种基于加权的模型，以评估和建议学习计算机课程的学生的学习材料。我们使用拟议的响应式学生模型构建了一个名为WinITS的全新ITS，并在2019-2020学年下半年通过计算机科学课程将其部署在越南河内国立教育大学（HNUE）。为了比较将ITS应用于学生的效果，我们比较了测试结果并分析了与课程相关的其他方面。另一方面，我们在两个小组之间进行了调查：使用和不使用WinITS。63名学生是参加案例研究的志愿者。在学习之前，第1组中的43名学生将对Felder-Silverman问卷进行简短调查，以确定学习风格，然后，在WinITS的支持下，完成课程中的所有课程，然后选择课程来满足学生的需求。另一方面，第2组的18名学生将进行相同的测试以将结果与第1组进行比较。在研究范围内，我们说明了我们的实施显示出一些令人鼓舞的结果，例如减少了学习时间，将测试分数提高了1.13个标准差。 ，并使课程变得更加有趣和灵活。结果显示，与传统班级相比，以各种方式进行计算机辅助学习的优势，并表明了该模型在智能辅导系统中的有效性。