An Analysis of Students’ Attitudes by Using Word Frequency and Word Cloud Visualization

Authors

  • Montarat Rungruangthum Rajamangala University of Technology Phra Nakhon
  • Nopamat Hongsacha
  • Thipwimol Wangkaewhiran

Keywords:

ภาพกลุ่มคำ, ข้อมูลเชิงคุณภาพ, ภาษาไทย

Abstract

This research aims to investigate the use of word frequency and word cloud visualization to analyze qualitative data in Thai, and to examine the students’ attitudes towards the training course of developing English language skills for teaching in English program, Faculty of Education, Rajabhat Rajanagarindra University. The samples in this study were the second- to the fourth-year university students in digital technology for education. These 51 participants (7 male and 44 female) were selected by using convenience sampling. The research instrument used for data collection was open-ended questionnaire asking the students’ attitudes towards participating in the training course, divided into two parts: the first part included general information, and the second part concerned feedback on the training course of developing English language skills, knowledge application of English language skills, problems, and suggestions. The data obtained from the questionnaire were prepared and analyzed by using “WordItOut”, which was compatible to Thai. The results of the study showed that word frequency and word cloud analysis could be used for Thai to summarize an overall students’ attitudes. The participants with positive attitude enjoyed learning and were aware the importance of English language for TOEIC test preparation and future careers. The students also added that the English language training should be extended longer.

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Published

2022-06-20

How to Cite

Rungruangthum, M., Hongsacha, N., & Wangkaewhiran, T. (2022). An Analysis of Students’ Attitudes by Using Word Frequency and Word Cloud Visualization. Education and Communication Technology Journal, 17(22), 63–77. Retrieved from https://so01.tci-thaijo.org/index.php/ectstou/article/view/253365

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Section

Research & Academic Articles