The paper pursues a twofold goal. The first goal refers to the identification of university students' needs regarding such modifications to English language courses that would improve English as a foreign language (EFL) teaching outcomes. The other goal refers to the methodical issue of achieving the first one. In this aspect, the use of selected data mining techniques in a hierarchical way in real data processing is proposed. These are: (a) Self‐Organizing Map (SOM) dataset segmentation and then (b) market basket analysis applied to the individual SOM segments. The research data were collected from the students' survey concerning their opinion of the EFL teaching process; 347 students of a faculty of a technical university in Poland completed the questionnaire. The use of SOM allowed the identification of homogeneous groups of students, while market basket analysis allowed indicating, within each group, the relationships between student opinions of effective methods of teaching English. In such a way, satisfactory student preference profiles as regards their approach to the improvement of English language competences were developed. On this basis, EFL teaching methods appropriate for the specific profile can be adapted.

中文翻译：

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在分析与英语作为外语教学的增强相关的学生偏好的两步过程中使用数据挖掘

本文追求双重目标。第一个目标是确定大学生对英语课程进行此类修改以提高英语作为外语（EFL）教学成果的需求。另一个目标是实现第一个目标的系统性问题。在这方面，提出了在实际数据处理中以分层方式使用所选数据挖掘技术。它们是：（a）自组织地图（SOM）数据集细分，然后（b）应用于单个SOM细分的市场篮子分析。研究数据是从学生的调查中收集的，有关他们对EFL教学过程的看法；波兰技术大学的347名学生完成了问卷。使用SOM可以识别同质的学生群体，而市场购物篮分析则可以在每个组中指出学生意见之间有效英语教学方法之间的关系。以这种方式，就他们提高英语能力的方法，开发了令人满意的学生偏好档案。在此基础上，可以调整适合特定情况的EFL教学方法。