scholarly journals Research Trends in College English Education in Korea －A Topic Analysis Using LDA Topic Modeling

2021 ◽  
Vol 15 (5) ◽  
pp. 169-183
Author(s):  
Eunhee Park
Keyword(s):  
Learning Strategies ◽  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
English Education ◽  
Research Trends ◽  
English For Specific Purposes ◽  
College English ◽  
Affective Factors ◽  
Learner Centered ◽  
Wide Range

This study investigated the research trends of college English education in Korea from 2001 to 2020. The data was collected using a Biblio data collector and a total of 313 papers were analyzed. For research purposes, the data were analyzed using frequency analysis, LDA (Latent Dirichlet Allocation), and time series analysis. The summary of the findings is as follows: In the first instance, the number of research papers regarding college English education has increased significantly in quantity for 20 years. Secondly, in analyzing the topics of the chosen papers, a total of 10 topics in college English education were found. The topics were “curriculum and level-differentiated programs (T1)”, “learners’ affective factors (T2)”, “assesment and learning strategies (T3)”, “teachers’ factors (T4)”, “English vocabulary, grammar and writing (T5)”, “English for specific purposes (T6)”, “teaching and learning methods (T7)”, “web-based learning (T8)”, “learner-centered education (T9)”, and “textbook analysis etc. (T10).” Among these topics, the three that were identified as topics increasing in popularity were “learners’ affective factors (T2)”, “English for specific purposes (T6)”, and “learner-centered education (T9).” The topics increasing in popularity shared one key characteristic: the topics were related to learners’ factors such as the learners’ motivation, the learners’ goals, and the learners’ activities in class. This study is meaningful in that it collected a wide range of data related to college English education in Korea and produced more reliable results by using big data-based LDA topic modeling techniques.

scholarly journals Topic Modelling in Bangla Language: An LDA Approach to Optimize Topics and News Classification

2018 ◽  
Vol 11 (4) ◽  
pp. 77 ◽  
Author(s):  
Malek Mouhoub ◽  
Mustakim Al Helal
Keyword(s):  
Topic Modeling ◽  
Text Categorization ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Similarity Measures ◽  
Document Collections ◽  
Statistical Language Modeling ◽  
Document Models ◽  
Wide Range ◽  
News Corpus

Topic modeling is a powerful technique for unsupervised analysis of large document collections. Topic models have a wide range of applications including tag recommendation, text categorization, keyword extraction and similarity search in the text mining, information retrieval and statistical language modeling. The research on topic modeling is gaining popularity day by day. There are various efficient topic modeling techniques available for the English language as it is one of the most spoken languages in the whole world but not for the other spoken languages. Bangla being the seventh most spoken native language in the world by population, it needs automation in different aspects. This paper deals with finding the core topics of Bangla news corpus and classifying news with similarity measures. The document models are built using LDA (Latent Dirichlet Allocation) with bigram.

scholarly journals Applicability Analysis of Task-Based Language Teaching Approach in the Context of College English Education in China

2017 ◽  
Vol 4 (4) ◽  
pp. 573
Author(s):  
Chen Yang
Keyword(s):  
Second Language Acquisition ◽  
English Education ◽  
Language Teaching ◽  
Teaching Approach ◽  
Background Information ◽  
College English ◽  
Learning Contexts ◽  
Wide Range ◽  
Education In China ◽  
Specific Section

<p><em>As a widely-used language teaching approach, Task-</em><em>B</em><em>ased Language Teaching (TBLT) has become increasingly popular in the field of second language acquisition, which can be applied in a wide range of different ages learners. The practice of TBLT in College English in China seems to be comparatively successful these years. The author starts his essay with a systematic literature review which addresses the theoretical bases of TBLT as well as the background information of College English </em><em>E</em><em>ducation in China. The essay then looks into the different practical situations in different learning contexts in Chinese colleges in terms of four English skills: listening, speaking, reading</em><em>,</em><em> writing, and tries to confirm which context is best suited to. Finally</em><em>,</em><em> the essay argues that it is difficult to define a specific section TBLT has obviously better effects than the other three in College English education, through comparing the strengths and limitations of application to each skill. In summary, TBLT can be favorably practiced in College English by narrowing down the drawbacks of the application to each skill.</em></p>

How to Identify Hot Topics in Psychology Using Topic Modeling

2018 ◽  
Vol 226 (1) ◽  
pp. 3-13 ◽  
Author(s):  
André Bittermann ◽  
Andreas Fischer
Keyword(s):  
Topic Modeling ◽  
English Language ◽  
Latent Dirichlet Allocation ◽  
Publication Output ◽  
Research Trends ◽  
Cross Cultural ◽  
Cultural Aspects ◽  
German Speaking ◽  
Latent Topics ◽  
Increasing Trends

Abstract. Latent topics and trends in psychological publications were examined to identify hotspots in psychology. Topic modeling was contrasted with a classification-based scientometric approach in order to demonstrate the benefits of the former. Specifically, the psychological publication output in the German-speaking countries containing German- and English-language publications from 1980 to 2016 documented in the PSYNDEX database was analyzed. Topic modeling based on latent Dirichlet allocation (LDA) was applied to a corpus of 314,573 publications. Input for topic modeling was the controlled terms of the publications, that is, a standardized vocabulary of keywords in psychology. Based on these controlled terms, 500 topics were determined and trending topics were identified. Hot topics, indicated by the highest increasing trends in this data, were facets of neuropsychology, online therapy, cross-cultural aspects, traumatization, and visual attention. In conclusion, the findings indicate that topics can reveal more detailed insights into research trends than standardized classifications. Possible applications of this method, limitations, and implications for research synthesis are discussed.

Research Trends of Consumer Education Using Topic Modeling

2020 ◽  
Vol 16 (2) ◽  
pp. 83-115
Author(s):  
Mira Kim ◽  
◽  
Hye Sun Hwang ◽  
Xu Li
Keyword(s):  
Topic Modeling ◽  
Research Trends ◽  
Consumer Education
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