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Use of data mining in a two‐step process of profiling student preferences in relation to the enhancement of English as a foreign language teaching
Author(s) -
Nowakowska Marzena,
Bęben Karolina,
Pajęcki Michał
Publication year - 2020
Publication title -
statistical analysis and data mining: the asa data science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.381
H-Index - 33
eISSN - 1932-1872
pISSN - 1932-1864
DOI - 10.1002/sam.11478
Subject(s) - profiling (computer programming) , computer science , relation (database) , process (computing) , natural language processing , data mining , programming language
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.